{"cells":[{"cell_type":"markdown","metadata":{"id":"2MyqVXWrweNk"},"source":["## **DESCRIPTIVE AND EXPLORATORY STATISTICS (EDA) (A)**\n","\n","Text for teaching purposes only. Compiled and expanded by Toni Monleón-Getino. University of Barcelona. 2024\n","\n","![](https://assets-global.website-files.com/63119622d2a6edf1d171e0bc/65d3a069871456bd33730869_GC2xT1oWgAA1rAC.jpeg )\n","\n","![](https://pbs.twimg.com/media/Dedp49tWkAAW0Bv.jpg )\n","\n","\n","\n","\n","---\n","\n","R offers many possibilities for performing descriptive and exploratory data analysis (EDA).\n","It also makes it possible to produce highly varied and high-quality graphic outputs. In this activity we present the main instructions and functions needed to perform these tasks.\n","\n","\n"," \n"," ![](https://i1.wp.com/basicmedicalkey.com/wp-content/uploads/2017/05/c01f001.jpg?w=960 )\n","\n","\n","\n","\n","An easy descriptive statistics approach to summarize the numeric and categoric data variables through the Measures of Central Tendency and Measures of Spread for every Exploratory Data Analysis process.\n","\n","---\n","\n","**About the Exploratory Data Analysis (EDA)**\n","EDA is the first step in the data analysis process. It allows us to understand the data we are dealing with by describing and summarizing the dataset’s main characteristics, often through visual methods like bar and pie charts, histograms, boxplots, scatterplots, heatmaps, and many more.\n","\n","![](http://www.mdpi.com/ijgi/ijgi-06-00368/article_deploy/html/images/ijgi-06-00368-g001.png)\n","\n","> EDA is fundamentally a creative process. And like most creative processes, the key to asking quality questions is to generate a large quantity of questions. It is difficult to ask revealing questions at the start of your analysis because you do not know what insights are contained in your dataset. On the other hand, each new question that you ask will expose you to a new aspect of your data and increase your chance of making a discovery. You can quickly drill down into the most interesting parts of your data—and develop a set of thought-provoking questions—if you follow up each question with a new question based on what you find.\n","\n","> There is no rule about which questions you should ask to guide your research. However, two types of questions will always be useful for making discoveries within your data. You can loosely word these questions as:\n","\n","\n"," > * What type of variation occurs within my variables?\n","\n"," > * What type of covariation occurs between my variables?\n","\n"," > * I’ll explain what variation and covariation are, and I’ll show you several ways to answer each question. To make the discussion easier, let’s define some terms:\n","\n"," > * A variable is a quantity, quality, or property that you can measure.\n","\n"," > * A value is the state of a variable when you measure it. The value of a variable may change from measurement to measurement.\n","\n"," > * An observation is a set of measurements made under similar conditions (you usually make all of the measurements in an observation at the same time and on the same object). An observation will contain several values, each associated with a different variable. I’ll sometimes refer to an observation as a data point.\n","\n"," > * Tabular data is a set of values, each associated with a variable and an observation.\n","\n"]},{"cell_type":"markdown","source":["A FIRST EXAMPLE:"],"metadata":{"id":"nLJDy4w__z3B"}},{"cell_type":"code","execution_count":3,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":1000},"executionInfo":{"elapsed":259,"status":"ok","timestamp":1716798934690,"user":{"displayName":"toni Monleon","userId":"05269440626861479105"},"user_tz":-120},"id":"3llY9aVvJ12U","outputId":"dc476dd3-6241-43ea-9a46-89fd31fac947"},"outputs":[{"output_type":"display_data","data":{"text/html":["\n","\n","\n","\t\n","\t\n","\n","\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\n","
A data.frame: 150 × 5
Sepal.LengthSepal.WidthPetal.LengthPetal.WidthSpecies
<dbl><dbl><dbl><dbl><fct>
5.13.51.40.2setosa
4.93.01.40.2setosa
4.73.21.30.2setosa
4.63.11.50.2setosa
5.03.61.40.2setosa
5.43.91.70.4setosa
4.63.41.40.3setosa
5.03.41.50.2setosa
4.42.91.40.2setosa
4.93.11.50.1setosa
5.43.71.50.2setosa
4.83.41.60.2setosa
4.83.01.40.1setosa
4.33.01.10.1setosa
5.84.01.20.2setosa
5.74.41.50.4setosa
5.43.91.30.4setosa
5.13.51.40.3setosa
5.73.81.70.3setosa
5.13.81.50.3setosa
5.43.41.70.2setosa
5.13.71.50.4setosa
4.63.61.00.2setosa
5.13.31.70.5setosa
4.83.41.90.2setosa
5.03.01.60.2setosa
5.03.41.60.4setosa
5.23.51.50.2setosa
5.23.41.40.2setosa
4.73.21.60.2setosa
⋮⋮⋮⋮⋮
6.93.25.72.3virginica
5.62.84.92.0virginica
7.72.86.72.0virginica
6.32.74.91.8virginica
6.73.35.72.1virginica
7.23.26.01.8virginica
6.22.84.81.8virginica
6.13.04.91.8virginica
6.42.85.62.1virginica
7.23.05.81.6virginica
7.42.86.11.9virginica
7.93.86.42.0virginica
6.42.85.62.2virginica
6.32.85.11.5virginica
6.12.65.61.4virginica
7.73.06.12.3virginica
6.33.45.62.4virginica
6.43.15.51.8virginica
6.03.04.81.8virginica
6.93.15.42.1virginica
6.73.15.62.4virginica
6.93.15.12.3virginica
5.82.75.11.9virginica
6.83.25.92.3virginica
6.73.35.72.5virginica
6.73.05.22.3virginica
6.32.55.01.9virginica
6.53.05.22.0virginica
6.23.45.42.3virginica
5.93.05.11.8virginica
\n"],"text/markdown":"\nA data.frame: 150 × 5\n\n| Sepal.Length <dbl> | Sepal.Width <dbl> | Petal.Length <dbl> | Petal.Width <dbl> | Species <fct> |\n|---|---|---|---|---|\n| 5.1 | 3.5 | 1.4 | 0.2 | setosa |\n| 4.9 | 3.0 | 1.4 | 0.2 | setosa |\n| 4.7 | 3.2 | 1.3 | 0.2 | setosa |\n| 4.6 | 3.1 | 1.5 | 0.2 | setosa |\n| 5.0 | 3.6 | 1.4 | 0.2 | setosa |\n| 5.4 | 3.9 | 1.7 | 0.4 | setosa |\n| 4.6 | 3.4 | 1.4 | 0.3 | setosa |\n| 5.0 | 3.4 | 1.5 | 0.2 | setosa |\n| 4.4 | 2.9 | 1.4 | 0.2 | setosa |\n| 4.9 | 3.1 | 1.5 | 0.1 | setosa |\n| 5.4 | 3.7 | 1.5 | 0.2 | setosa |\n| 4.8 | 3.4 | 1.6 | 0.2 | setosa |\n| 4.8 | 3.0 | 1.4 | 0.1 | setosa |\n| 4.3 | 3.0 | 1.1 | 0.1 | setosa |\n| 5.8 | 4.0 | 1.2 | 0.2 | setosa |\n| 5.7 | 4.4 | 1.5 | 0.4 | setosa |\n| 5.4 | 3.9 | 1.3 | 0.4 | setosa |\n| 5.1 | 3.5 | 1.4 | 0.3 | setosa |\n| 5.7 | 3.8 | 1.7 | 0.3 | setosa |\n| 5.1 | 3.8 | 1.5 | 0.3 | setosa |\n| 5.4 | 3.4 | 1.7 | 0.2 | setosa |\n| 5.1 | 3.7 | 1.5 | 0.4 | setosa |\n| 4.6 | 3.6 | 1.0 | 0.2 | setosa |\n| 5.1 | 3.3 | 1.7 | 0.5 | setosa |\n| 4.8 | 3.4 | 1.9 | 0.2 | setosa |\n| 5.0 | 3.0 | 1.6 | 0.2 | setosa |\n| 5.0 | 3.4 | 1.6 | 0.4 | setosa |\n| 5.2 | 3.5 | 1.5 | 0.2 | setosa |\n| 5.2 | 3.4 | 1.4 | 0.2 | setosa |\n| 4.7 | 3.2 | 1.6 | 0.2 | setosa |\n| ⋮ | ⋮ | ⋮ | ⋮ | ⋮ |\n| 6.9 | 3.2 | 5.7 | 2.3 | virginica |\n| 5.6 | 2.8 | 4.9 | 2.0 | virginica |\n| 7.7 | 2.8 | 6.7 | 2.0 | virginica |\n| 6.3 | 2.7 | 4.9 | 1.8 | virginica |\n| 6.7 | 3.3 | 5.7 | 2.1 | virginica |\n| 7.2 | 3.2 | 6.0 | 1.8 | virginica |\n| 6.2 | 2.8 | 4.8 | 1.8 | virginica |\n| 6.1 | 3.0 | 4.9 | 1.8 | virginica |\n| 6.4 | 2.8 | 5.6 | 2.1 | virginica |\n| 7.2 | 3.0 | 5.8 | 1.6 | virginica |\n| 7.4 | 2.8 | 6.1 | 1.9 | virginica |\n| 7.9 | 3.8 | 6.4 | 2.0 | virginica |\n| 6.4 | 2.8 | 5.6 | 2.2 | virginica |\n| 6.3 | 2.8 | 5.1 | 1.5 | virginica |\n| 6.1 | 2.6 | 5.6 | 1.4 | virginica |\n| 7.7 | 3.0 | 6.1 | 2.3 | virginica |\n| 6.3 | 3.4 | 5.6 | 2.4 | virginica |\n| 6.4 | 3.1 | 5.5 | 1.8 | virginica |\n| 6.0 | 3.0 | 4.8 | 1.8 | virginica |\n| 6.9 | 3.1 | 5.4 | 2.1 | virginica |\n| 6.7 | 3.1 | 5.6 | 2.4 | virginica |\n| 6.9 | 3.1 | 5.1 | 2.3 | virginica |\n| 5.8 | 2.7 | 5.1 | 1.9 | virginica |\n| 6.8 | 3.2 | 5.9 | 2.3 | virginica |\n| 6.7 | 3.3 | 5.7 | 2.5 | virginica |\n| 6.7 | 3.0 | 5.2 | 2.3 | virginica |\n| 6.3 | 2.5 | 5.0 | 1.9 | virginica |\n| 6.5 | 3.0 | 5.2 | 2.0 | virginica |\n| 6.2 | 3.4 | 5.4 | 2.3 | virginica |\n| 5.9 | 3.0 | 5.1 | 1.8 | virginica |\n\n","text/latex":"A data.frame: 150 × 5\n\\begin{tabular}{lllll}\n Sepal.Length & Sepal.Width & Petal.Length & Petal.Width & Species\\\\\n & & & & \\\\\n\\hline\n\t 5.1 & 3.5 & 1.4 & 0.2 & setosa\\\\\n\t 4.9 & 3.0 & 1.4 & 0.2 & setosa\\\\\n\t 4.7 & 3.2 & 1.3 & 0.2 & setosa\\\\\n\t 4.6 & 3.1 & 1.5 & 0.2 & setosa\\\\\n\t 5.0 & 3.6 & 1.4 & 0.2 & setosa\\\\\n\t 5.4 & 3.9 & 1.7 & 0.4 & setosa\\\\\n\t 4.6 & 3.4 & 1.4 & 0.3 & setosa\\\\\n\t 5.0 & 3.4 & 1.5 & 0.2 & setosa\\\\\n\t 4.4 & 2.9 & 1.4 & 0.2 & setosa\\\\\n\t 4.9 & 3.1 & 1.5 & 0.1 & setosa\\\\\n\t 5.4 & 3.7 & 1.5 & 0.2 & setosa\\\\\n\t 4.8 & 3.4 & 1.6 & 0.2 & setosa\\\\\n\t 4.8 & 3.0 & 1.4 & 0.1 & setosa\\\\\n\t 4.3 & 3.0 & 1.1 & 0.1 & setosa\\\\\n\t 5.8 & 4.0 & 1.2 & 0.2 & setosa\\\\\n\t 5.7 & 4.4 & 1.5 & 0.4 & setosa\\\\\n\t 5.4 & 3.9 & 1.3 & 0.4 & setosa\\\\\n\t 5.1 & 3.5 & 1.4 & 0.3 & setosa\\\\\n\t 5.7 & 3.8 & 1.7 & 0.3 & setosa\\\\\n\t 5.1 & 3.8 & 1.5 & 0.3 & setosa\\\\\n\t 5.4 & 3.4 & 1.7 & 0.2 & setosa\\\\\n\t 5.1 & 3.7 & 1.5 & 0.4 & setosa\\\\\n\t 4.6 & 3.6 & 1.0 & 0.2 & setosa\\\\\n\t 5.1 & 3.3 & 1.7 & 0.5 & setosa\\\\\n\t 4.8 & 3.4 & 1.9 & 0.2 & setosa\\\\\n\t 5.0 & 3.0 & 1.6 & 0.2 & setosa\\\\\n\t 5.0 & 3.4 & 1.6 & 0.4 & setosa\\\\\n\t 5.2 & 3.5 & 1.5 & 0.2 & setosa\\\\\n\t 5.2 & 3.4 & 1.4 & 0.2 & setosa\\\\\n\t 4.7 & 3.2 & 1.6 & 0.2 & setosa\\\\\n\t ⋮ & ⋮ & ⋮ & ⋮ & ⋮\\\\\n\t 6.9 & 3.2 & 5.7 & 2.3 & virginica\\\\\n\t 5.6 & 2.8 & 4.9 & 2.0 & virginica\\\\\n\t 7.7 & 2.8 & 6.7 & 2.0 & virginica\\\\\n\t 6.3 & 2.7 & 4.9 & 1.8 & virginica\\\\\n\t 6.7 & 3.3 & 5.7 & 2.1 & virginica\\\\\n\t 7.2 & 3.2 & 6.0 & 1.8 & virginica\\\\\n\t 6.2 & 2.8 & 4.8 & 1.8 & virginica\\\\\n\t 6.1 & 3.0 & 4.9 & 1.8 & virginica\\\\\n\t 6.4 & 2.8 & 5.6 & 2.1 & virginica\\\\\n\t 7.2 & 3.0 & 5.8 & 1.6 & virginica\\\\\n\t 7.4 & 2.8 & 6.1 & 1.9 & virginica\\\\\n\t 7.9 & 3.8 & 6.4 & 2.0 & virginica\\\\\n\t 6.4 & 2.8 & 5.6 & 2.2 & virginica\\\\\n\t 6.3 & 2.8 & 5.1 & 1.5 & virginica\\\\\n\t 6.1 & 2.6 & 5.6 & 1.4 & virginica\\\\\n\t 7.7 & 3.0 & 6.1 & 2.3 & virginica\\\\\n\t 6.3 & 3.4 & 5.6 & 2.4 & virginica\\\\\n\t 6.4 & 3.1 & 5.5 & 1.8 & virginica\\\\\n\t 6.0 & 3.0 & 4.8 & 1.8 & virginica\\\\\n\t 6.9 & 3.1 & 5.4 & 2.1 & virginica\\\\\n\t 6.7 & 3.1 & 5.6 & 2.4 & virginica\\\\\n\t 6.9 & 3.1 & 5.1 & 2.3 & virginica\\\\\n\t 5.8 & 2.7 & 5.1 & 1.9 & virginica\\\\\n\t 6.8 & 3.2 & 5.9 & 2.3 & virginica\\\\\n\t 6.7 & 3.3 & 5.7 & 2.5 & virginica\\\\\n\t 6.7 & 3.0 & 5.2 & 2.3 & virginica\\\\\n\t 6.3 & 2.5 & 5.0 & 1.9 & virginica\\\\\n\t 6.5 & 3.0 & 5.2 & 2.0 & virginica\\\\\n\t 6.2 & 3.4 & 5.4 & 2.3 & virginica\\\\\n\t 5.9 & 3.0 & 5.1 & 1.8 & virginica\\\\\n\\end{tabular}\n","text/plain":[" Sepal.Length Sepal.Width Petal.Length Petal.Width Species \n","1 5.1 3.5 1.4 0.2 setosa \n","2 4.9 3.0 1.4 0.2 setosa \n","3 4.7 3.2 1.3 0.2 setosa \n","4 4.6 3.1 1.5 0.2 setosa \n","5 5.0 3.6 1.4 0.2 setosa \n","6 5.4 3.9 1.7 0.4 setosa \n","7 4.6 3.4 1.4 0.3 setosa \n","8 5.0 3.4 1.5 0.2 setosa \n","9 4.4 2.9 1.4 0.2 setosa \n","10 4.9 3.1 1.5 0.1 setosa \n","11 5.4 3.7 1.5 0.2 setosa \n","12 4.8 3.4 1.6 0.2 setosa \n","13 4.8 3.0 1.4 0.1 setosa \n","14 4.3 3.0 1.1 0.1 setosa \n","15 5.8 4.0 1.2 0.2 setosa \n","16 5.7 4.4 1.5 0.4 setosa \n","17 5.4 3.9 1.3 0.4 setosa \n","18 5.1 3.5 1.4 0.3 setosa \n","19 5.7 3.8 1.7 0.3 setosa \n","20 5.1 3.8 1.5 0.3 setosa \n","21 5.4 3.4 1.7 0.2 setosa \n","22 5.1 3.7 1.5 0.4 setosa \n","23 4.6 3.6 1.0 0.2 setosa \n","24 5.1 3.3 1.7 0.5 setosa \n","25 4.8 3.4 1.9 0.2 setosa \n","26 5.0 3.0 1.6 0.2 setosa \n","27 5.0 3.4 1.6 0.4 setosa \n","28 5.2 3.5 1.5 0.2 setosa \n","29 5.2 3.4 1.4 0.2 setosa \n","30 4.7 3.2 1.6 0.2 setosa \n","⋮ ⋮ ⋮ ⋮ ⋮ ⋮ \n","121 6.9 3.2 5.7 2.3 virginica\n","122 5.6 2.8 4.9 2.0 virginica\n","123 7.7 2.8 6.7 2.0 virginica\n","124 6.3 2.7 4.9 1.8 virginica\n","125 6.7 3.3 5.7 2.1 virginica\n","126 7.2 3.2 6.0 1.8 virginica\n","127 6.2 2.8 4.8 1.8 virginica\n","128 6.1 3.0 4.9 1.8 virginica\n","129 6.4 2.8 5.6 2.1 virginica\n","130 7.2 3.0 5.8 1.6 virginica\n","131 7.4 2.8 6.1 1.9 virginica\n","132 7.9 3.8 6.4 2.0 virginica\n","133 6.4 2.8 5.6 2.2 virginica\n","134 6.3 2.8 5.1 1.5 virginica\n","135 6.1 2.6 5.6 1.4 virginica\n","136 7.7 3.0 6.1 2.3 virginica\n","137 6.3 3.4 5.6 2.4 virginica\n","138 6.4 3.1 5.5 1.8 virginica\n","139 6.0 3.0 4.8 1.8 virginica\n","140 6.9 3.1 5.4 2.1 virginica\n","141 6.7 3.1 5.6 2.4 virginica\n","142 6.9 3.1 5.1 2.3 virginica\n","143 5.8 2.7 5.1 1.9 virginica\n","144 6.8 3.2 5.9 2.3 virginica\n","145 6.7 3.3 5.7 2.5 virginica\n","146 6.7 3.0 5.2 2.3 virginica\n","147 6.3 2.5 5.0 1.9 virginica\n","148 6.5 3.0 5.2 2.0 virginica\n","149 6.2 3.4 5.4 2.3 virginica\n","150 5.9 3.0 5.1 1.8 virginica"]},"metadata":{}},{"output_type":"display_data","data":{"text/plain":[" Sepal.Length Sepal.Width Petal.Length Petal.Width \n"," Min. :4.300 Min. :2.000 Min. :1.000 Min. :0.100 \n"," 1st Qu.:5.100 1st Qu.:2.800 1st Qu.:1.600 1st Qu.:0.300 \n"," Median :5.800 Median :3.000 Median :4.350 Median :1.300 \n"," Mean :5.843 Mean :3.057 Mean :3.758 Mean :1.199 \n"," 3rd Qu.:6.400 3rd Qu.:3.300 3rd Qu.:5.100 3rd Qu.:1.800 \n"," Max. :7.900 Max. :4.400 Max. :6.900 Max. :2.500 \n"," Species \n"," setosa :50 \n"," versicolor:50 \n"," virginica :50 \n"," \n"," \n"," "]},"metadata":{}}],"source":["#A fundamental first task is explore your data\n","data.frame(iris)\n","\n","#data summary\n","summary(iris)\n","\n","#exercise\n","#import this data (milions of rows and try to observe variables and rows)\n","# Read CSV into DataFrame\n","# df <- read.csv(\"https://r.tiid.org/R_tute_data2007-2017.csv\")\n","# head(df)\n","# dim(df)\n","# summary(df)\n"]},{"cell_type":"markdown","source":["An advanced summary in R, see in: https://rpubs.com/werbitsky/rstatistics"],"metadata":{"id":"m788mxfvE2Tw"}},{"cell_type":"code","source":["#install.packages(\"skimr\")\n","library(skimr)"],"metadata":{"id":"vqy0dsmzE6sJ","executionInfo":{"status":"ok","timestamp":1716798939088,"user_tz":-120,"elapsed":269,"user":{"displayName":"toni Monleon","userId":"05269440626861479105"}}},"execution_count":4,"outputs":[]},{"cell_type":"code","source":["#Summarizing the whole dataset\n","#We begin with putting the whole dataset in the main function of the package called skim.\n","\n","skim(iris)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":488},"id":"s3uTlZgUFF-A","executionInfo":{"status":"error","timestamp":1716798941047,"user_tz":-120,"elapsed":271,"user":{"displayName":"toni Monleon","userId":"05269440626861479105"}},"outputId":"1e7b3443-baba-4612-975f-2347a248e741"},"execution_count":5,"outputs":[{"output_type":"stream","name":"stdout","text":["── Data Summary ────────────────────────\n"," Values\n","Name iris \n","Number of rows 150 \n","Number of columns 5 \n","_______________________ \n","Column type frequency: \n"," factor 1 \n"," numeric 4 \n","________________________ \n","Group variables None \n","\n","── Variable type: factor ───────────────────────────────────────────────────────\n"," skim_variable n_missing complete_rate ordered n_unique\n","\u001b[90m1\u001b[39m Species 0 1 FALSE 3\n"," top_counts \n","\u001b[90m1\u001b[39m set: 50, ver: 50, vir: 50\n","\n","── Variable type: numeric ──────────────────────────────────────────────────────\n"," skim_variable n_missing complete_rate mean sd p0 p25 p50 p75 p100 hist \n","\u001b[90m1\u001b[39m Sepal.Length 0 1 5.84 0.828 4.3 5.1 5.8 6.4 7.9 ▆▇▇▅▂\n","\u001b[90m2\u001b[39m Sepal.Width 0 1 3.06 0.436 2 2.8 3 3.3 4.4 ▁▆▇▂▁\n","\u001b[90m3\u001b[39m Petal.Length 0 1 3.76 1.77 1 1.6 4.35 5.1 6.9 ▇▁▆▇▂\n","\u001b[90m4\u001b[39m Petal.Width 0 1 1.20 0.762 0.1 0.3 1.3 1.8 2.5 ▇▁▇▅▃\n"]},{"output_type":"error","ename":"ERROR","evalue":"Error in is.null(text_repr) || nchar(text_repr) == 0L: 'length = 15' in coercion to 'logical(1)'\n","traceback":["Error in is.null(text_repr) || nchar(text_repr) == 0L: 'length = 15' in coercion to 'logical(1)'\nTraceback:\n"]}]},{"cell_type":"markdown","source":["As you can see, skimr provides us with a detailed summary of our data. We can get from it:\n","\n","number of rows and columns\n","columns` types and the number of variables per type\n","number of missings for each variable\n","for factors: whether ordered, number of unique values (= #levels), number of observations per groups\n","for numerics: number of missings, mean, standard deviations, values from the lowest(p0) to the highest(p100) for our population with 25% step, and a small histogram\n","Note: numeric and factor variables are separated, which makes table more easy to read."],"metadata":{"id":"x5szl82EFRxL"}},{"cell_type":"markdown","source":["Skimr is nicely working in combination with dplyr functions. So, you can aggregate the data and look at the summary just by adding one line to your dplyr code. The most usable feature here is that skimr can provide summary statistics for levels of factors that has been grouped using dplyr::group_by.\n","\n","For example, if we want to know how math score are different for Petal.Length and Sepal.Width of different species, we can start with the statistics presented below. What we get is the summary statistics on math scores for the levels of our grouping variable(species)."],"metadata":{"id":"M_6LvD0dFfF2"}},{"cell_type":"code","source":["#summary for Petal.Length and Sepal.Width by Species\n","iris %>%\n"," dplyr::select(Petal.Length, Sepal.Width, Species) %>%\n"," dplyr::group_by(Species) %>%\n"," skim()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":523},"id":"utX-u4KZFtfD","executionInfo":{"status":"error","timestamp":1716798945547,"user_tz":-120,"elapsed":266,"user":{"displayName":"toni Monleon","userId":"05269440626861479105"}},"outputId":"aab7ddb8-a92c-4ba1-b359-210855cf6e2d"},"execution_count":6,"outputs":[{"output_type":"stream","name":"stdout","text":["── Data Summary ────────────────────────\n"," Values \n","Name Piped data\n","Number of rows 150 \n","Number of columns 3 \n","_______________________ \n","Column type frequency: \n"," numeric 2 \n","________________________ \n","Group variables Species \n","\n","── Variable type: numeric ──────────────────────────────────────────────────────\n"," skim_variable Species n_missing complete_rate mean sd p0 p25 p50 p75\n","\u001b[90m1\u001b[39m Petal.Length setosa 0 1 1.46 0.174 1 1.4 1.5 1.58\n","\u001b[90m2\u001b[39m Petal.Length versicolor 0 1 4.26 0.470 3 4 4.35 4.6 \n","\u001b[90m3\u001b[39m Petal.Length virginica 0 1 5.55 0.552 4.5 5.1 5.55 5.88\n","\u001b[90m4\u001b[39m Sepal.Width setosa 0 1 3.43 0.379 2.3 3.2 3.4 3.68\n","\u001b[90m5\u001b[39m Sepal.Width versicolor 0 1 2.77 0.314 2 2.52 2.8 3 \n","\u001b[90m6\u001b[39m Sepal.Width virginica 0 1 2.97 0.322 2.2 2.8 3 3.18\n"," p100 hist \n","\u001b[90m1\u001b[39m 1.9 ▁▃▇▃▁\n","\u001b[90m2\u001b[39m 5.1 ▂▂▇▇▆\n","\u001b[90m3\u001b[39m 6.9 ▃▇▇▃▂\n","\u001b[90m4\u001b[39m 4.4 ▁▃▇▅▂\n","\u001b[90m5\u001b[39m 3.4 ▁▅▆▇▂\n","\u001b[90m6\u001b[39m 3.8 ▂▆▇▅▁\n"]},{"output_type":"error","ename":"ERROR","evalue":"Error in is.null(text_repr) || nchar(text_repr) == 0L: 'length = 13' in coercion to 'logical(1)'\n","traceback":["Error in is.null(text_repr) || nchar(text_repr) == 0L: 'length = 13' in coercion to 'logical(1)'\nTraceback:\n"]}]},{"cell_type":"markdown","source":["EXTRA FUNCTION: More possibilities using: summarytools (PROBLEMA EN COLAB, UTILIZAR EN RSTUDIO)"],"metadata":{"id":"j1NvR1sJGiev"}},{"cell_type":"code","source":["#install.packages('devtools') # if not already installed\n","library(devtools)\n","\n","\n"],"metadata":{"id":"SEdo9B_3Gn5A","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1716798964567,"user_tz":-120,"elapsed":757,"user":{"displayName":"toni Monleon","userId":"05269440626861479105"}},"outputId":"3ad26e63-8573-457f-beb6-72b72fc9c3f4"},"execution_count":7,"outputs":[{"output_type":"stream","name":"stderr","text":["Loading required package: usethis\n","\n"]}]},{"cell_type":"code","source":["install.packages('summarytools', type = 'binary')"],"metadata":{"id":"TWnmQ35cMwP7"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["A good introduction for SUMMARY-TOOLS IN: https://cran.r-project.org/web/packages/summarytools/vignettes/introduction.html"],"metadata":{"id":"63Qk7VY_Iowg"}},{"cell_type":"code","source":["library(summarytools)\n","#Summarizing the whole dataset\n","#As always, we start with summarizing the whole dataset. The output is a shiny.tag. To make your graphs attractive, specify method=\"render.\n","\n","print(dfSummary(iris), method = \"render\", varnumbers = FALSE, valid.col = FALSE)#"],"metadata":{"id":"DFFxRx3FGsTQ","colab":{"base_uri":"https://localhost:8080/","height":106},"executionInfo":{"status":"error","timestamp":1716798975621,"user_tz":-120,"elapsed":240,"user":{"displayName":"toni Monleon","userId":"05269440626861479105"}},"outputId":"2db912a2-74e6-41c2-b3ce-d5c17c9f100d"},"execution_count":8,"outputs":[{"output_type":"error","ename":"ERROR","evalue":"Error in library(summarytools): there is no package called ‘summarytools’\n","traceback":["Error in library(summarytools): there is no package called ‘summarytools’\nTraceback:\n","1. library(summarytools)"]}]},{"cell_type":"markdown","source":[" **SUGGESTED BILIOGRAPHY TO DO A GOOD EDA**"],"metadata":{"id":"y6kCLOocAf8E"}},{"cell_type":"markdown","metadata":{"id":"RA5hSNaEN400"},"source":["\n","*READ CAREFULLY* the mini-introduction in: https://towardsdatascience.com/descriptive-statistics-expectations-vs-reality-exploratory-data-analysis-eda-8336b1d0c60bb\n","\n","\n","Additional information (complementari eg Data Wrangling in:Fuente: https://bio304-class.github.io/bio304-book/index.html)\n","\n","Unos libros recomendados:\n","\n","Exploratory Data Analysis with R\n","Roger D. Peng\n","2016-09-14\n","\n","\n","![](https://bookdown.org/rdpeng/exdata/cover_sm.png)\n"]},{"cell_type":"markdown","metadata":{"id":"VJng2bLcwBsk"},"source":["### **Case example with R of exploratory and descriptive analysis of data used in the Biomedical field: Simple Fast Exploratory Data Analysis in R with DataExplorer Package (Chocolate)**\n","\n","Fuente:\n","\n","\n","\n","***In Data Science, 80% of time spent prepare data, 20% of time spent complain about the need to prepare data.***\n","\n","Installation and Loading\n","Let us begin our EDA by loading the library:"]},{"cell_type":"code","execution_count":9,"metadata":{"executionInfo":{"elapsed":495,"status":"ok","timestamp":1716798987758,"user":{"displayName":"toni Monleon","userId":"05269440626861479105"},"user_tz":-120},"id":"2tQdmChOqzqD"},"outputs":[],"source":["#Install if the package doesn't exist\n","#install.packages(\"DataExplorer\")\n","library(DataExplorer)\n"]},{"cell_type":"markdown","metadata":{"id":"fKBjX3UWx-YS"},"source":["The data-frame: \n","\n","Chocolate is one of the most popular candies in the world. Each year, residents of the United States collectively eat more than 2.8 billions pounds. However, not all chocolate bars are created equal! This dataset contains expert ratings of over 1,700 individual chocolate bars, along with information on their regional origin, percentage of cocoa, the variety of chocolate bean used and where the beans were grown.\n","\n","\n","![alt text](https://www.gastronomiavasca.net/uploads/image/file/4180/cacao_1.jpg)\n"]},{"cell_type":"markdown","metadata":{"id":"VOUgFwOdZ_Wa"},"source":["Download locally \"flavors_of_cacao.csv\"from BIOST3 data-server (http://biost3.bio.ub.edu/html/posgrado_data_science/), put the data into your GOOGLE-DRIVE \"content\" directory the file: flavors_of_cacao.csv and check if file is there."]},{"cell_type":"code","execution_count":10,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":52},"executionInfo":{"elapsed":254,"status":"ok","timestamp":1716799000181,"user":{"displayName":"toni Monleon","userId":"05269440626861479105"},"user_tz":-120},"id":"UVR4gZ9_hWfu","outputId":"b17966f4-1690-4692-8da6-bbececddf2a9"},"outputs":[{"output_type":"display_data","data":{"text/html":["'/content'"],"text/markdown":"'/content'","text/latex":"'/content'","text/plain":["[1] \"/content\""]},"metadata":{}},{"output_type":"display_data","data":{"text/html":["'sample_data'"],"text/markdown":"'sample_data'","text/latex":"'sample\\_data'","text/plain":["[1] \"sample_data\""]},"metadata":{}}],"source":["getwd()\n","\n","setwd('/content')\n","dir()\n","\n","#example about adress patway for files: setwd('/content/sample_data')\n","\n"]},{"cell_type":"markdown","metadata":{"id":"n8rQfo34irSX"},"source":["import data into R"]},{"cell_type":"code","execution_count":11,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":193},"executionInfo":{"elapsed":257,"status":"error","timestamp":1716799005034,"user":{"displayName":"toni Monleon","userId":"05269440626861479105"},"user_tz":-120},"id":"y9g7u7-Gvre3","outputId":"a3fd3b36-9ad2-4871-bbaa-3f9af57416ff"},"outputs":[{"output_type":"stream","name":"stderr","text":["Warning message in file(file, \"rt\"):\n","“cannot open file 'flavors_of_cacao.csv': No such file or directory”\n"]},{"output_type":"error","ename":"ERROR","evalue":"Error in file(file, \"rt\"): cannot open the connection\n","traceback":["Error in file(file, \"rt\"): cannot open the connection\nTraceback:\n","1. read.csv(\"flavors_of_cacao.csv\", header = T, stringsAsFactors = F)","2. read.table(file = file, header = header, sep = sep, quote = quote, \n . dec = dec, fill = fill, comment.char = comment.char, ...)","3. file(file, \"rt\")"]}],"source":["choco = read.csv('flavors_of_cacao.csv', header = T, stringsAsFactors = F)\n","#head(choco)\n","View(choco[1:10,])\n","\n","\n","\n"]},{"cell_type":"code","source":["#alternativelly is possible use the pathway from the server:\n","choco = read.csv('http://biost3.bio.ub.edu/html/posgrado_data_science/EDA_EXPERIMENTS/flavors_of_cacao.csv', header = T, stringsAsFactors = F)\n","#head(choco)\n","View(choco[1:10,])"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":431},"id":"UFS3Jh4KRLZK","executionInfo":{"status":"ok","timestamp":1716799014749,"user_tz":-120,"elapsed":2265,"user":{"displayName":"toni Monleon","userId":"05269440626861479105"}},"outputId":"622627a5-b888-4b87-9a0f-2209e484a3e0"},"execution_count":12,"outputs":[{"output_type":"display_data","data":{"text/plain":[" Company...Maker.if.known. Specific.Bean.Origin.or.Bar.Name REF Review.Date\n","1 A. Morin Agua Grande 1876 2016 \n","2 A. Morin Kpime 1676 2015 \n","3 A. Morin Atsane 1676 2015 \n","4 A. Morin Akata 1680 2015 \n","5 A. Morin Quilla 1704 2015 \n","6 A. Morin Carenero 1315 2014 \n","7 A. Morin Cuba 1315 2014 \n","8 A. Morin Sur del Lago 1315 2014 \n","9 A. Morin Puerto Cabello 1319 2014 \n","10 A. Morin Pablino 1319 2014 \n"," Cocoa.Percent Company.Location Rating Bean.Type Broad.Bean.Origin\n","1 63% France 3.75   Sao Tome \n","2 70% France 2.75   Togo \n","3 70% France 3.00   Togo \n","4 70% France 3.50   Togo \n","5 70% France 3.50   Peru \n","6 70% France 2.75 Criollo Venezuela \n","7 70% France 3.50   Cuba \n","8 70% France 3.50 Criollo Venezuela \n","9 70% France 3.75 Criollo Venezuela \n","10 70% France 4.00   Peru "],"text/html":["\n","\n","\n","\t\n","\t\n","\n","\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\n","
A data.frame: 10 × 9
Company...Maker.if.known.Specific.Bean.Origin.or.Bar.NameREFReview.DateCocoa.PercentCompany.LocationRatingBean.TypeBroad.Bean.Origin
<chr><chr><int><int><chr><chr><dbl><chr><chr>
1A. MorinAgua Grande 1876201663%France3.75  Sao Tome
2A. MorinKpime 1676201570%France2.75  Togo
3A. MorinAtsane 1676201570%France3.00  Togo
4A. MorinAkata 1680201570%France3.50  Togo
5A. MorinQuilla 1704201570%France3.50  Peru
6A. MorinCarenero 1315201470%France2.75CriolloVenezuela
7A. MorinCuba 1315201470%France3.50  Cuba
8A. MorinSur del Lago 1315201470%France3.50CriolloVenezuela
9A. MorinPuerto Cabello1319201470%France3.75CriolloVenezuela
10A. MorinPablino 1319201470%France4.00  Peru
\n"],"text/markdown":"\nA data.frame: 10 × 9\n\n| | Company...Maker.if.known. <chr> | Specific.Bean.Origin.or.Bar.Name <chr> | REF <int> | Review.Date <int> | Cocoa.Percent <chr> | Company.Location <chr> | Rating <dbl> | Bean.Type <chr> | Broad.Bean.Origin <chr> |\n|---|---|---|---|---|---|---|---|---|---|\n| 1 | A. Morin | Agua Grande | 1876 | 2016 | 63% | France | 3.75 |   | Sao Tome |\n| 2 | A. Morin | Kpime | 1676 | 2015 | 70% | France | 2.75 |   | Togo |\n| 3 | A. Morin | Atsane | 1676 | 2015 | 70% | France | 3.00 |   | Togo |\n| 4 | A. Morin | Akata | 1680 | 2015 | 70% | France | 3.50 |   | Togo |\n| 5 | A. Morin | Quilla | 1704 | 2015 | 70% | France | 3.50 |   | Peru |\n| 6 | A. Morin | Carenero | 1315 | 2014 | 70% | France | 2.75 | Criollo | Venezuela |\n| 7 | A. Morin | Cuba | 1315 | 2014 | 70% | France | 3.50 |   | Cuba |\n| 8 | A. Morin | Sur del Lago | 1315 | 2014 | 70% | France | 3.50 | Criollo | Venezuela |\n| 9 | A. Morin | Puerto Cabello | 1319 | 2014 | 70% | France | 3.75 | Criollo | Venezuela |\n| 10 | A. Morin | Pablino | 1319 | 2014 | 70% | France | 4.00 |   | Peru |\n\n","text/latex":"A data.frame: 10 × 9\n\\begin{tabular}{r|lllllllll}\n & Company...Maker.if.known. & Specific.Bean.Origin.or.Bar.Name & REF & Review.Date & Cocoa.Percent & Company.Location & Rating & Bean.Type & Broad.Bean.Origin\\\\\n & & & & & & & & & \\\\\n\\hline\n\t1 & A. Morin & Agua Grande & 1876 & 2016 & 63\\% & France & 3.75 &   & Sao Tome \\\\\n\t2 & A. Morin & Kpime & 1676 & 2015 & 70\\% & France & 2.75 &   & Togo \\\\\n\t3 & A. Morin & Atsane & 1676 & 2015 & 70\\% & France & 3.00 &   & Togo \\\\\n\t4 & A. Morin & Akata & 1680 & 2015 & 70\\% & France & 3.50 &   & Togo \\\\\n\t5 & A. Morin & Quilla & 1704 & 2015 & 70\\% & France & 3.50 &   & Peru \\\\\n\t6 & A. Morin & Carenero & 1315 & 2014 & 70\\% & France & 2.75 & Criollo & Venezuela\\\\\n\t7 & A. Morin & Cuba & 1315 & 2014 & 70\\% & France & 3.50 &   & Cuba \\\\\n\t8 & A. Morin & Sur del Lago & 1315 & 2014 & 70\\% & France & 3.50 & Criollo & Venezuela\\\\\n\t9 & A. Morin & Puerto Cabello & 1319 & 2014 & 70\\% & France & 3.75 & Criollo & Venezuela\\\\\n\t10 & A. Morin & Pablino & 1319 & 2014 & 70\\% & France & 4.00 &   & Peru \\\\\n\\end{tabular}\n"},"metadata":{}}]},{"cell_type":"code","execution_count":null,"metadata":{"id":"hWRgbpDgjGzc"},"outputs":[],"source":["\n","\n"]},{"cell_type":"markdown","metadata":{"id":"SU5d5hXs8-h2"},"source":["Try to DESCRIBE and EXPLORE Data"]},{"cell_type":"code","execution_count":13,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":34},"executionInfo":{"elapsed":271,"status":"ok","timestamp":1716799017550,"user":{"displayName":"toni Monleon","userId":"05269440626861479105"},"user_tz":-120},"id":"wzETAtGEVPF3","outputId":"b59e288c-eb8f-41bf-8cb3-5f982f0a2e41"},"outputs":[{"output_type":"display_data","data":{"text/html":["\n","
  1. 'Company...Maker.if.known.'
  2. 'Specific.Bean.Origin.or.Bar.Name'
  3. 'REF'
  4. 'Review.Date'
  5. 'Cocoa.Percent'
  6. 'Company.Location'
  7. 'Rating'
  8. 'Bean.Type'
  9. 'Broad.Bean.Origin'
\n"],"text/markdown":"1. 'Company...Maker.if.known.'\n2. 'Specific.Bean.Origin.or.Bar.Name'\n3. 'REF'\n4. 'Review.Date'\n5. 'Cocoa.Percent'\n6. 'Company.Location'\n7. 'Rating'\n8. 'Bean.Type'\n9. 'Broad.Bean.Origin'\n\n\n","text/latex":"\\begin{enumerate*}\n\\item 'Company...Maker.if.known.'\n\\item 'Specific.Bean.Origin.or.Bar.Name'\n\\item 'REF'\n\\item 'Review.Date'\n\\item 'Cocoa.Percent'\n\\item 'Company.Location'\n\\item 'Rating'\n\\item 'Bean.Type'\n\\item 'Broad.Bean.Origin'\n\\end{enumerate*}\n","text/plain":["[1] \"Company...Maker.if.known.\" \"Specific.Bean.Origin.or.Bar.Name\"\n","[3] \"REF\" \"Review.Date\" \n","[5] \"Cocoa.Percent\" \"Company.Location\" \n","[7] \"Rating\" \"Bean.Type\" \n","[9] \"Broad.Bean.Origin\" "]},"metadata":{}}],"source":["#variable names to list variable names\n","names(choco)"]},{"cell_type":"markdown","source":["A summary of variables can be:\n","\n","* Company...Maker.if.known: This variable likely stores the company that manufactures the chocolate. It might contain the full company name, or if unknown, it might include a placeholder value.\n","* Specific.Bean.Origin.or.Bar.Name: This variable could store either the specific origin of the cocoa beans used (e.g., Madagascar, Ecuador) or the name of the chocolate bar itself.\n","* REF Review.Date: This variable seems to be a reference number linked to a review date. It might be connected to a separate table containing chocolate reviews.\n","* Cocoa.Percent: This variable stores the percentage of cocoa solids in the chocolate.\n","* Company.Location: This variable likely stores the location (country or region) of the chocolate manufacturer.\n","* Rating: This variable likely stores a rating for the chocolate, possibly from consumers or reviewers.\n","* Bean.Type: This variable indicates the type of cocoa bean used (e.g., Criollo, Forastero, Trinitario).\n","* Broad.Bean.Origin: This variable provides a broader categorization of the cocoa bean origin compared to \"Specific.Bean.Origin.or.Bar.Name\" (e.g., South America, Africa).\n","\n"],"metadata":{"id":"NRd-IweBUDJq"}},{"cell_type":"markdown","metadata":{"id":"YtUwbSbzi1Qn"},"source":["**One of the most important issues is understanding the data, the meaning of the variables, how the information is collected, the structure of the data, the nature of the variables and their distribution.**\n","\n","\n","---\n","\n","\n","\n"," ** *Flavors of Cacao Rating System:* **\n","\n","5= Elite (Transcending beyond the ordinary limits)\n","4= Premium (Superior flavor development, character and style)\n","3= Satisfactory(3.0) to praiseworthy(3.75) (well made with special qualities)\n","2= Disappointing (Passable but contains at least one significant flaw)\n","1= Unpleasant (mostly unpalatable)\n","Each chocolate is evaluated from a combination of both objective qualities and subjective interpretation. A rating here only represents an experience with one bar from one batch. Batch numbers, vintages and review dates are included in the database when known.\n","\n","The database is narrowly focused on plain dark chocolate with an aim of appreciating the flavors of the cacao when made into chocolate. The ratings do not reflect health benefits, social missions, or organic status.\n","\n","** *Flavor* **\n","\n","Is the most important component of the Flavors of Cacao ratings. Diversity, balance, intensity and purity of flavors are all considered. It is possible for a straight forward single note chocolate to rate as high as a complex flavor profile that changes throughout. Genetics, terroir, post harvest techniques, processing and storage can all be discussed when considering the flavor component.\n","\n","** *Texture* **\n","\n","Has a great impact on the overall experience and it is also possible for texture related issues to impact flavor. It is a good way to evaluate the makers vision, attention to detail and level of proficiency.\n","\n","**Aftermelt** is the experience after the chocolate has melted. Higher quality chocolate will linger and be long lasting and enjoyable. Since the aftermelt is the last impression you get from the chocolate, it receives equal importance in the overall rating.\n","\n","**Overall Opinion** is really where the ratings reflect a subjective opinion. Ideally it is my evaluation of whether or not the components above worked together and an opinion on the flavor development, character and style. It is also here where each chocolate can usually be summarized by the most prominent impressions that you would remember about each chocolate.\n","\n","----\n","\n","Acknowledgements:\n","These ratings were compiled by Brady Brelinski, Founding Member of the Manhattan Chocolate Society. For up-to-date information, as well as additional content (including interviews with craft chocolate makers), please see his website: Flavors of Cacao\n","\n","----\n","\n","QUESTIONS TO ANSWER\n","\n","**Inspiration:**\n","Where are the best cocoa beans grown?\n","Which countries produce the highest-rated bars?\n","What's the relationship between cocoa solids percentage and rating?"]},{"cell_type":"markdown","metadata":{"id":"UDCZPl6ti7mt"},"source":["# Analysis of data\n","\n","## 1-Data Cleaning\n","\n","Some reformatting of data types are required before proceeding. For example, Cocoa.Percent is supposed to be a numeric value but read as a character due to the presence of % symbol, so needs to be fixed."]},{"cell_type":"code","execution_count":14,"metadata":{"id":"c60n9dqIlTTc","colab":{"base_uri":"https://localhost:8080/","height":1000},"executionInfo":{"status":"ok","timestamp":1716799022920,"user_tz":-120,"elapsed":257,"user":{"displayName":"toni Monleon","userId":"05269440626861479105"}},"outputId":"679584a7-078b-4c83-9c65-8b68aa65b674"},"outputs":[{"output_type":"display_data","data":{"text/html":["\n","\n","\n","\t\n","\t\n","\n","\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\n","
A data.frame: 1795 × 9
Company...Maker.if.known.Specific.Bean.Origin.or.Bar.NameREFReview.DateCocoa.PercentCompany.LocationRatingBean.TypeBroad.Bean.Origin
<chr><chr><int><chr><dbl><chr><dbl><chr><chr>
A. Morin Agua Grande 1876201663France 3.75  Sao Tome
A. Morin Kpime 1676201570France 2.75  Togo
A. Morin Atsane 1676201570France 3.00  Togo
A. Morin Akata 1680201570France 3.50  Togo
A. Morin Quilla 1704201570France 3.50  Peru
A. Morin Carenero 1315201470France 2.75Criollo Venezuela
A. Morin Cuba 1315201470France 3.50  Cuba
A. Morin Sur del Lago 1315201470France 3.50Criollo Venezuela
A. Morin Puerto Cabello 1319201470France 3.75Criollo Venezuela
A. Morin Pablino 1319201470France 4.00  Peru
A. Morin Panama 1011201370France 2.75  Panama
A. Morin Madagascar 1011201370France 3.00Criollo Madagascar
A. Morin Brazil 1011201370France 3.25  Brazil
A. Morin Equateur 1011201370France 3.75  Ecuador
A. Morin Colombie 1015201370France 2.75  Colombia
A. Morin Birmanie 1015201370France 3.00  Burma
A. Morin Papua New Guinea 1015201370France 3.25  Papua New Guinea
A. Morin Chuao 1015201370France 4.00Trinitario Venezuela
A. Morin Piura 1019201370France 3.25  Peru
A. Morin Chanchamayo Province 1019201370France 3.50  Peru
A. Morin Chanchamayo Province 1019201363France 4.00  Peru
A. Morin Bolivia 797201270France 3.50  Bolivia
A. Morin Peru 797201263France 3.75  Peru
Acalli Chulucanas, El Platanal 1462201570U.S.A. 3.75  Peru
Acalli Tumbes, Norandino 1470201570U.S.A. 3.75Criollo Peru
Adi Vanua Levu 705201160Fiji 2.75Trinitario Fiji
Adi Vanua Levu, Toto-A 705201180Fiji 3.25Trinitario Fiji
Adi Vanua Levu 705201188Fiji 3.50Trinitario Fiji
Adi Vanua Levu, Ami-Ami-CA 705201172Fiji 3.50Trinitario Fiji
Aequare (Gianduja)Los Rios, Quevedo, Arriba 370200955Ecuador2.75Forastero (Arriba)Ecuador
⋮⋮⋮⋮⋮⋮⋮⋮⋮
Zak's Belize, Batch 2 1578201570U.S.A. 3.50Trinitario Belize
Zak's House Blend, Batch 2 1582201560U.S.A. 3.00   
Zart PralinenMillot P., Ambanja 1820201670Austria 3.50Criollo, Trinitario Madagascar
Zart PralinenUNOCACE 1824201670Austria 2.75Nacional (Arriba) Ecuador
Zart PralinenSan Juan Estate 1824201685Austria 2.75Trinitario Trinidad
Zart PralinenKakao Kamili, Kilombero Valley 1824201685Austria 3.00Criollo, Trinitario Tanzania
Zart PralinenKakao Kamili, Kilombero Valley 1824201670Austria 3.50Criollo, Trinitario Tanzania
Zart PralinenSan Juan Estate, Gran Couva 1880201678Austria 3.50Trinitario Trinidad
Zokoko Guadalcanal 1716201678Australia3.75  Solomon Islands
Zokoko Goddess Blend 1780201665Australia3.25   
Zokoko Alto Beni 697201168Australia3.50  Bolivia
Zokoko Tokiala 701201166Australia3.50Trinitario Papua New Guinea
Zokoko Tranquilidad, Baures 701201172Australia3.75  Bolivia
Zotter Raw 1205201480Austria 2.75   
Zotter Bocas del Toro, Cocabo Co-op 801201272Austria 3.50  Panama
Zotter Amazonas Frucht 801201265Austria 3.50   
Zotter Satipo Pangoa region, 16hr conche 875201270Austria 3.00Criollo (Amarru) Peru
Zotter Satipo Pangoa region, 20hr conche 875201270Austria 3.50Criollo (Amarru) Peru
Zotter Loma Los Pinos, Yacao region, D.R. 875201262Austria 3.75  Dominican Republic
Zotter El Oro 879201275Austria 3.00Forastero (Nacional)Ecuador
Zotter Huiwani Coop 879201275Austria 3.00Criollo, Trinitario Papua New Guinea
Zotter El Ceibo Coop 879201290Austria 3.25  Bolivia
Zotter Santo Domingo 879201270Austria 3.75  Dominican Republic
Zotter Kongo, Highlands 883201268Austria 3.25Forastero Congo
Zotter Indianer, Raw 883201258Austria 3.50   
Zotter Peru 647201170Austria 3.75  Peru
Zotter Congo 749201165Austria 3.00Forastero Congo
Zotter Kerala State 749201165Austria 3.50Forastero India
Zotter Kerala State 781201162Austria 3.25  India
Zotter Brazil, Mitzi Blue 486201065Austria 3.00  Brazil
\n"],"text/markdown":"\nA data.frame: 1795 × 9\n\n| Company...Maker.if.known. <chr> | Specific.Bean.Origin.or.Bar.Name <chr> | REF <int> | Review.Date <chr> | Cocoa.Percent <dbl> | Company.Location <chr> | Rating <dbl> | Bean.Type <chr> | Broad.Bean.Origin <chr> |\n|---|---|---|---|---|---|---|---|---|\n| A. Morin | Agua Grande | 1876 | 2016 | 63 | France | 3.75 |   | Sao Tome |\n| A. Morin | Kpime | 1676 | 2015 | 70 | France | 2.75 |   | Togo |\n| A. Morin | Atsane | 1676 | 2015 | 70 | France | 3.00 |   | Togo |\n| A. Morin | Akata | 1680 | 2015 | 70 | France | 3.50 |   | Togo |\n| A. Morin | Quilla | 1704 | 2015 | 70 | France | 3.50 |   | Peru |\n| A. Morin | Carenero | 1315 | 2014 | 70 | France | 2.75 | Criollo | Venezuela |\n| A. Morin | Cuba | 1315 | 2014 | 70 | France | 3.50 |   | Cuba |\n| A. Morin | Sur del Lago | 1315 | 2014 | 70 | France | 3.50 | Criollo | Venezuela |\n| A. Morin | Puerto Cabello | 1319 | 2014 | 70 | France | 3.75 | Criollo | Venezuela |\n| A. Morin | Pablino | 1319 | 2014 | 70 | France | 4.00 |   | Peru |\n| A. Morin | Panama | 1011 | 2013 | 70 | France | 2.75 |   | Panama |\n| A. Morin | Madagascar | 1011 | 2013 | 70 | France | 3.00 | Criollo | Madagascar |\n| A. Morin | Brazil | 1011 | 2013 | 70 | France | 3.25 |   | Brazil |\n| A. Morin | Equateur | 1011 | 2013 | 70 | France | 3.75 |   | Ecuador |\n| A. Morin | Colombie | 1015 | 2013 | 70 | France | 2.75 |   | Colombia |\n| A. Morin | Birmanie | 1015 | 2013 | 70 | France | 3.00 |   | Burma |\n| A. Morin | Papua New Guinea | 1015 | 2013 | 70 | France | 3.25 |   | Papua New Guinea |\n| A. Morin | Chuao | 1015 | 2013 | 70 | France | 4.00 | Trinitario | Venezuela |\n| A. Morin | Piura | 1019 | 2013 | 70 | France | 3.25 |   | Peru |\n| A. Morin | Chanchamayo Province | 1019 | 2013 | 70 | France | 3.50 |   | Peru |\n| A. Morin | Chanchamayo Province | 1019 | 2013 | 63 | France | 4.00 |   | Peru |\n| A. Morin | Bolivia | 797 | 2012 | 70 | France | 3.50 |   | Bolivia |\n| A. Morin | Peru | 797 | 2012 | 63 | France | 3.75 |   | Peru |\n| Acalli | Chulucanas, El Platanal | 1462 | 2015 | 70 | U.S.A. | 3.75 |   | Peru |\n| Acalli | Tumbes, Norandino | 1470 | 2015 | 70 | U.S.A. | 3.75 | Criollo | Peru |\n| Adi | Vanua Levu | 705 | 2011 | 60 | Fiji | 2.75 | Trinitario | Fiji |\n| Adi | Vanua Levu, Toto-A | 705 | 2011 | 80 | Fiji | 3.25 | Trinitario | Fiji |\n| Adi | Vanua Levu | 705 | 2011 | 88 | Fiji | 3.50 | Trinitario | Fiji |\n| Adi | Vanua Levu, Ami-Ami-CA | 705 | 2011 | 72 | Fiji | 3.50 | Trinitario | Fiji |\n| Aequare (Gianduja) | Los Rios, Quevedo, Arriba | 370 | 2009 | 55 | Ecuador | 2.75 | Forastero (Arriba) | Ecuador |\n| ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ |\n| Zak's | Belize, Batch 2 | 1578 | 2015 | 70 | U.S.A. | 3.50 | Trinitario | Belize |\n| Zak's | House Blend, Batch 2 | 1582 | 2015 | 60 | U.S.A. | 3.00 |   |   |\n| Zart Pralinen | Millot P., Ambanja | 1820 | 2016 | 70 | Austria | 3.50 | Criollo, Trinitario | Madagascar |\n| Zart Pralinen | UNOCACE | 1824 | 2016 | 70 | Austria | 2.75 | Nacional (Arriba) | Ecuador |\n| Zart Pralinen | San Juan Estate | 1824 | 2016 | 85 | Austria | 2.75 | Trinitario | Trinidad |\n| Zart Pralinen | Kakao Kamili, Kilombero Valley | 1824 | 2016 | 85 | Austria | 3.00 | Criollo, Trinitario | Tanzania |\n| Zart Pralinen | Kakao Kamili, Kilombero Valley | 1824 | 2016 | 70 | Austria | 3.50 | Criollo, Trinitario | Tanzania |\n| Zart Pralinen | San Juan Estate, Gran Couva | 1880 | 2016 | 78 | Austria | 3.50 | Trinitario | Trinidad |\n| Zokoko | Guadalcanal | 1716 | 2016 | 78 | Australia | 3.75 |   | Solomon Islands |\n| Zokoko | Goddess Blend | 1780 | 2016 | 65 | Australia | 3.25 |   |   |\n| Zokoko | Alto Beni | 697 | 2011 | 68 | Australia | 3.50 |   | Bolivia |\n| Zokoko | Tokiala | 701 | 2011 | 66 | Australia | 3.50 | Trinitario | Papua New Guinea |\n| Zokoko | Tranquilidad, Baures | 701 | 2011 | 72 | Australia | 3.75 |   | Bolivia |\n| Zotter | Raw | 1205 | 2014 | 80 | Austria | 2.75 |   |   |\n| Zotter | Bocas del Toro, Cocabo Co-op | 801 | 2012 | 72 | Austria | 3.50 |   | Panama |\n| Zotter | Amazonas Frucht | 801 | 2012 | 65 | Austria | 3.50 |   |   |\n| Zotter | Satipo Pangoa region, 16hr conche | 875 | 2012 | 70 | Austria | 3.00 | Criollo (Amarru) | Peru |\n| Zotter | Satipo Pangoa region, 20hr conche | 875 | 2012 | 70 | Austria | 3.50 | Criollo (Amarru) | Peru |\n| Zotter | Loma Los Pinos, Yacao region, D.R. | 875 | 2012 | 62 | Austria | 3.75 |   | Dominican Republic |\n| Zotter | El Oro | 879 | 2012 | 75 | Austria | 3.00 | Forastero (Nacional) | Ecuador |\n| Zotter | Huiwani Coop | 879 | 2012 | 75 | Austria | 3.00 | Criollo, Trinitario | Papua New Guinea |\n| Zotter | El Ceibo Coop | 879 | 2012 | 90 | Austria | 3.25 |   | Bolivia |\n| Zotter | Santo Domingo | 879 | 2012 | 70 | Austria | 3.75 |   | Dominican Republic |\n| Zotter | Kongo, Highlands | 883 | 2012 | 68 | Austria | 3.25 | Forastero | Congo |\n| Zotter | Indianer, Raw | 883 | 2012 | 58 | Austria | 3.50 |   |   |\n| Zotter | Peru | 647 | 2011 | 70 | Austria | 3.75 |   | Peru |\n| Zotter | Congo | 749 | 2011 | 65 | Austria | 3.00 | Forastero | Congo |\n| Zotter | Kerala State | 749 | 2011 | 65 | Austria | 3.50 | Forastero | India |\n| Zotter | Kerala State | 781 | 2011 | 62 | Austria | 3.25 |   | India |\n| Zotter | Brazil, Mitzi Blue | 486 | 2010 | 65 | Austria | 3.00 |   | Brazil |\n\n","text/latex":"A data.frame: 1795 × 9\n\\begin{tabular}{lllllllll}\n Company...Maker.if.known. & Specific.Bean.Origin.or.Bar.Name & REF & Review.Date & Cocoa.Percent & Company.Location & Rating & Bean.Type & Broad.Bean.Origin\\\\\n & & & & & & & & \\\\\n\\hline\n\t A. Morin & Agua Grande & 1876 & 2016 & 63 & France & 3.75 &   & Sao Tome \\\\\n\t A. Morin & Kpime & 1676 & 2015 & 70 & France & 2.75 &   & Togo \\\\\n\t A. Morin & Atsane & 1676 & 2015 & 70 & France & 3.00 &   & Togo \\\\\n\t A. Morin & Akata & 1680 & 2015 & 70 & France & 3.50 &   & Togo \\\\\n\t A. Morin & Quilla & 1704 & 2015 & 70 & France & 3.50 &   & Peru \\\\\n\t A. Morin & Carenero & 1315 & 2014 & 70 & France & 2.75 & Criollo & Venezuela \\\\\n\t A. Morin & Cuba & 1315 & 2014 & 70 & France & 3.50 &   & Cuba \\\\\n\t A. Morin & Sur del Lago & 1315 & 2014 & 70 & France & 3.50 & Criollo & Venezuela \\\\\n\t A. Morin & Puerto Cabello & 1319 & 2014 & 70 & France & 3.75 & Criollo & Venezuela \\\\\n\t A. Morin & Pablino & 1319 & 2014 & 70 & France & 4.00 &   & Peru \\\\\n\t A. Morin & Panama & 1011 & 2013 & 70 & France & 2.75 &   & Panama \\\\\n\t A. Morin & Madagascar & 1011 & 2013 & 70 & France & 3.00 & Criollo & Madagascar \\\\\n\t A. Morin & Brazil & 1011 & 2013 & 70 & France & 3.25 &   & Brazil \\\\\n\t A. Morin & Equateur & 1011 & 2013 & 70 & France & 3.75 &   & Ecuador \\\\\n\t A. Morin & Colombie & 1015 & 2013 & 70 & France & 2.75 &   & Colombia \\\\\n\t A. Morin & Birmanie & 1015 & 2013 & 70 & France & 3.00 &   & Burma \\\\\n\t A. Morin & Papua New Guinea & 1015 & 2013 & 70 & France & 3.25 &   & Papua New Guinea\\\\\n\t A. Morin & Chuao & 1015 & 2013 & 70 & France & 4.00 & Trinitario & Venezuela \\\\\n\t A. Morin & Piura & 1019 & 2013 & 70 & France & 3.25 &   & Peru \\\\\n\t A. Morin & Chanchamayo Province & 1019 & 2013 & 70 & France & 3.50 &   & Peru \\\\\n\t A. Morin & Chanchamayo Province & 1019 & 2013 & 63 & France & 4.00 &   & Peru \\\\\n\t A. Morin & Bolivia & 797 & 2012 & 70 & France & 3.50 &   & Bolivia \\\\\n\t A. Morin & Peru & 797 & 2012 & 63 & France & 3.75 &   & Peru \\\\\n\t Acalli & Chulucanas, El Platanal & 1462 & 2015 & 70 & U.S.A. & 3.75 &   & Peru \\\\\n\t Acalli & Tumbes, Norandino & 1470 & 2015 & 70 & U.S.A. & 3.75 & Criollo & Peru \\\\\n\t Adi & Vanua Levu & 705 & 2011 & 60 & Fiji & 2.75 & Trinitario & Fiji \\\\\n\t Adi & Vanua Levu, Toto-A & 705 & 2011 & 80 & Fiji & 3.25 & Trinitario & Fiji \\\\\n\t Adi & Vanua Levu & 705 & 2011 & 88 & Fiji & 3.50 & Trinitario & Fiji \\\\\n\t Adi & Vanua Levu, Ami-Ami-CA & 705 & 2011 & 72 & Fiji & 3.50 & Trinitario & Fiji \\\\\n\t Aequare (Gianduja) & Los Rios, Quevedo, Arriba & 370 & 2009 & 55 & Ecuador & 2.75 & Forastero (Arriba) & Ecuador \\\\\n\t ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮\\\\\n\t Zak's & Belize, Batch 2 & 1578 & 2015 & 70 & U.S.A. & 3.50 & Trinitario & Belize \\\\\n\t Zak's & House Blend, Batch 2 & 1582 & 2015 & 60 & U.S.A. & 3.00 &   &   \\\\\n\t Zart Pralinen & Millot P., Ambanja & 1820 & 2016 & 70 & Austria & 3.50 & Criollo, Trinitario & Madagascar \\\\\n\t Zart Pralinen & UNOCACE & 1824 & 2016 & 70 & Austria & 2.75 & Nacional (Arriba) & Ecuador \\\\\n\t Zart Pralinen & San Juan Estate & 1824 & 2016 & 85 & Austria & 2.75 & Trinitario & Trinidad \\\\\n\t Zart Pralinen & Kakao Kamili, Kilombero Valley & 1824 & 2016 & 85 & Austria & 3.00 & Criollo, Trinitario & Tanzania \\\\\n\t Zart Pralinen & Kakao Kamili, Kilombero Valley & 1824 & 2016 & 70 & Austria & 3.50 & Criollo, Trinitario & Tanzania \\\\\n\t Zart Pralinen & San Juan Estate, Gran Couva & 1880 & 2016 & 78 & Austria & 3.50 & Trinitario & Trinidad \\\\\n\t Zokoko & Guadalcanal & 1716 & 2016 & 78 & Australia & 3.75 &   & Solomon Islands \\\\\n\t Zokoko & Goddess Blend & 1780 & 2016 & 65 & Australia & 3.25 &   &   \\\\\n\t Zokoko & Alto Beni & 697 & 2011 & 68 & Australia & 3.50 &   & Bolivia \\\\\n\t Zokoko & Tokiala & 701 & 2011 & 66 & Australia & 3.50 & Trinitario & Papua New Guinea \\\\\n\t Zokoko & Tranquilidad, Baures & 701 & 2011 & 72 & Australia & 3.75 &   & Bolivia \\\\\n\t Zotter & Raw & 1205 & 2014 & 80 & Austria & 2.75 &   &   \\\\\n\t Zotter & Bocas del Toro, Cocabo Co-op & 801 & 2012 & 72 & Austria & 3.50 &   & Panama \\\\\n\t Zotter & Amazonas Frucht & 801 & 2012 & 65 & Austria & 3.50 &   &   \\\\\n\t Zotter & Satipo Pangoa region, 16hr conche & 875 & 2012 & 70 & Austria & 3.00 & Criollo (Amarru) & Peru \\\\\n\t Zotter & Satipo Pangoa region, 20hr conche & 875 & 2012 & 70 & Austria & 3.50 & Criollo (Amarru) & Peru \\\\\n\t Zotter & Loma Los Pinos, Yacao region, D.R. & 875 & 2012 & 62 & Austria & 3.75 &   & Dominican Republic\\\\\n\t Zotter & El Oro & 879 & 2012 & 75 & Austria & 3.00 & Forastero (Nacional) & Ecuador \\\\\n\t Zotter & Huiwani Coop & 879 & 2012 & 75 & Austria & 3.00 & Criollo, Trinitario & Papua New Guinea \\\\\n\t Zotter & El Ceibo Coop & 879 & 2012 & 90 & Austria & 3.25 &   & Bolivia \\\\\n\t Zotter & Santo Domingo & 879 & 2012 & 70 & Austria & 3.75 &   & Dominican Republic\\\\\n\t Zotter & Kongo, Highlands & 883 & 2012 & 68 & Austria & 3.25 & Forastero & Congo \\\\\n\t Zotter & Indianer, Raw & 883 & 2012 & 58 & Austria & 3.50 &   &   \\\\\n\t Zotter & Peru & 647 & 2011 & 70 & Austria & 3.75 &   & Peru \\\\\n\t Zotter & Congo & 749 & 2011 & 65 & Austria & 3.00 & Forastero & Congo \\\\\n\t Zotter & Kerala State & 749 & 2011 & 65 & Austria & 3.50 & Forastero & India \\\\\n\t Zotter & Kerala State & 781 & 2011 & 62 & Austria & 3.25 &   & India \\\\\n\t Zotter & Brazil, Mitzi Blue & 486 & 2010 & 65 & Austria & 3.00 &   & Brazil \\\\\n\\end{tabular}\n","text/plain":[" Company...Maker.if.known. Specific.Bean.Origin.or.Bar.Name REF \n","1 A. Morin Agua Grande 1876\n","2 A. Morin Kpime 1676\n","3 A. Morin Atsane 1676\n","4 A. Morin Akata 1680\n","5 A. Morin Quilla 1704\n","6 A. Morin Carenero 1315\n","7 A. Morin Cuba 1315\n","8 A. Morin Sur del Lago 1315\n","9 A. Morin Puerto Cabello 1319\n","10 A. Morin Pablino 1319\n","11 A. Morin Panama 1011\n","12 A. Morin Madagascar 1011\n","13 A. Morin Brazil 1011\n","14 A. Morin Equateur 1011\n","15 A. Morin Colombie 1015\n","16 A. Morin Birmanie 1015\n","17 A. Morin Papua New Guinea 1015\n","18 A. Morin Chuao 1015\n","19 A. Morin Piura 1019\n","20 A. Morin Chanchamayo Province 1019\n","21 A. Morin Chanchamayo Province 1019\n","22 A. Morin Bolivia 797\n","23 A. Morin Peru 797\n","24 Acalli Chulucanas, El Platanal 1462\n","25 Acalli Tumbes, Norandino 1470\n","26 Adi Vanua Levu 705\n","27 Adi Vanua Levu, Toto-A 705\n","28 Adi Vanua Levu 705\n","29 Adi Vanua Levu, Ami-Ami-CA 705\n","30 Aequare (Gianduja) Los Rios, Quevedo, Arriba 370\n","⋮ ⋮ ⋮ ⋮ \n","1766 Zak's Belize, Batch 2 1578\n","1767 Zak's House Blend, Batch 2 1582\n","1768 Zart Pralinen Millot P., Ambanja 1820\n","1769 Zart Pralinen UNOCACE 1824\n","1770 Zart Pralinen San Juan Estate 1824\n","1771 Zart Pralinen Kakao Kamili, Kilombero Valley 1824\n","1772 Zart Pralinen Kakao Kamili, Kilombero Valley 1824\n","1773 Zart Pralinen San Juan Estate, Gran Couva 1880\n","1774 Zokoko Guadalcanal 1716\n","1775 Zokoko Goddess Blend 1780\n","1776 Zokoko Alto Beni 697\n","1777 Zokoko Tokiala 701\n","1778 Zokoko Tranquilidad, Baures 701\n","1779 Zotter Raw 1205\n","1780 Zotter Bocas del Toro, Cocabo Co-op 801\n","1781 Zotter Amazonas Frucht 801\n","1782 Zotter Satipo Pangoa region, 16hr conche 875\n","1783 Zotter Satipo Pangoa region, 20hr conche 875\n","1784 Zotter Loma Los Pinos, Yacao region, D.R. 875\n","1785 Zotter El Oro 879\n","1786 Zotter Huiwani Coop 879\n","1787 Zotter El Ceibo Coop 879\n","1788 Zotter Santo Domingo 879\n","1789 Zotter Kongo, Highlands 883\n","1790 Zotter Indianer, Raw 883\n","1791 Zotter Peru 647\n","1792 Zotter Congo 749\n","1793 Zotter Kerala State 749\n","1794 Zotter Kerala State 781\n","1795 Zotter Brazil, Mitzi Blue 486\n"," Review.Date Cocoa.Percent Company.Location Rating Bean.Type \n","1 2016 63 France 3.75   \n","2 2015 70 France 2.75   \n","3 2015 70 France 3.00   \n","4 2015 70 France 3.50   \n","5 2015 70 France 3.50   \n","6 2014 70 France 2.75 Criollo \n","7 2014 70 France 3.50   \n","8 2014 70 France 3.50 Criollo \n","9 2014 70 France 3.75 Criollo \n","10 2014 70 France 4.00   \n","11 2013 70 France 2.75   \n","12 2013 70 France 3.00 Criollo \n","13 2013 70 France 3.25   \n","14 2013 70 France 3.75   \n","15 2013 70 France 2.75   \n","16 2013 70 France 3.00   \n","17 2013 70 France 3.25   \n","18 2013 70 France 4.00 Trinitario \n","19 2013 70 France 3.25   \n","20 2013 70 France 3.50   \n","21 2013 63 France 4.00   \n","22 2012 70 France 3.50   \n","23 2012 63 France 3.75   \n","24 2015 70 U.S.A. 3.75   \n","25 2015 70 U.S.A. 3.75 Criollo \n","26 2011 60 Fiji 2.75 Trinitario \n","27 2011 80 Fiji 3.25 Trinitario \n","28 2011 88 Fiji 3.50 Trinitario \n","29 2011 72 Fiji 3.50 Trinitario \n","30 2009 55 Ecuador 2.75 Forastero (Arriba) \n","⋮ ⋮ ⋮ ⋮ ⋮ ⋮ \n","1766 2015 70 U.S.A. 3.50 Trinitario \n","1767 2015 60 U.S.A. 3.00   \n","1768 2016 70 Austria 3.50 Criollo, Trinitario \n","1769 2016 70 Austria 2.75 Nacional (Arriba) \n","1770 2016 85 Austria 2.75 Trinitario \n","1771 2016 85 Austria 3.00 Criollo, Trinitario \n","1772 2016 70 Austria 3.50 Criollo, Trinitario \n","1773 2016 78 Austria 3.50 Trinitario \n","1774 2016 78 Australia 3.75   \n","1775 2016 65 Australia 3.25   \n","1776 2011 68 Australia 3.50   \n","1777 2011 66 Australia 3.50 Trinitario \n","1778 2011 72 Australia 3.75   \n","1779 2014 80 Austria 2.75   \n","1780 2012 72 Austria 3.50   \n","1781 2012 65 Austria 3.50   \n","1782 2012 70 Austria 3.00 Criollo (Amarru) \n","1783 2012 70 Austria 3.50 Criollo (Amarru) \n","1784 2012 62 Austria 3.75   \n","1785 2012 75 Austria 3.00 Forastero (Nacional)\n","1786 2012 75 Austria 3.00 Criollo, Trinitario \n","1787 2012 90 Austria 3.25   \n","1788 2012 70 Austria 3.75   \n","1789 2012 68 Austria 3.25 Forastero \n","1790 2012 58 Austria 3.50   \n","1791 2011 70 Austria 3.75   \n","1792 2011 65 Austria 3.00 Forastero \n","1793 2011 65 Austria 3.50 Forastero \n","1794 2011 62 Austria 3.25   \n","1795 2010 65 Austria 3.00   \n"," Broad.Bean.Origin \n","1 Sao Tome \n","2 Togo \n","3 Togo \n","4 Togo \n","5 Peru \n","6 Venezuela \n","7 Cuba \n","8 Venezuela \n","9 Venezuela \n","10 Peru \n","11 Panama \n","12 Madagascar \n","13 Brazil \n","14 Ecuador \n","15 Colombia \n","16 Burma \n","17 Papua New Guinea \n","18 Venezuela \n","19 Peru \n","20 Peru \n","21 Peru \n","22 Bolivia \n","23 Peru \n","24 Peru \n","25 Peru \n","26 Fiji \n","27 Fiji \n","28 Fiji \n","29 Fiji \n","30 Ecuador \n","⋮ ⋮ \n","1766 Belize \n","1767   \n","1768 Madagascar \n","1769 Ecuador \n","1770 Trinidad \n","1771 Tanzania \n","1772 Tanzania \n","1773 Trinidad \n","1774 Solomon Islands \n","1775   \n","1776 Bolivia \n","1777 Papua New Guinea \n","1778 Bolivia \n","1779   \n","1780 Panama \n","1781   \n","1782 Peru \n","1783 Peru \n","1784 Dominican Republic\n","1785 Ecuador \n","1786 Papua New Guinea \n","1787 Bolivia \n","1788 Dominican Republic\n","1789 Congo \n","1790   \n","1791 Peru \n","1792 Congo \n","1793 India \n","1794 India \n","1795 Brazil "]},"metadata":{}}],"source":["choco$Cocoa.Percent = as.numeric(gsub('%','',choco$Cocoa.Percent))\n","choco$Review.Date = as.character(choco$Review.Date)\n","\n","choco"]},{"cell_type":"markdown","metadata":{"id":"wgwf92GbXVWA"},"source":["## 2-The Variables\n","\n","The very first thing that you'd want to do in your EDA is checking the dimension of the input dataset and the time of variables."]},{"cell_type":"code","execution_count":15,"metadata":{"id":"h-cVKw_sMwfF","executionInfo":{"status":"ok","timestamp":1716799027139,"user_tz":-120,"elapsed":236,"user":{"displayName":"toni Monleon","userId":"05269440626861479105"}}},"outputs":[],"source":["library(DataExplorer)\n","plot_str(choco) #only in Rstudio\n","#Gives this plot\n","\n"]},{"cell_type":"markdown","source":["see plot in:\n","http://biost3.bio.ub.edu/html/posgrado_data_science/EDA_EXPERIMENTS/eda.jpg"],"metadata":{"id":"JSb2aG2UEepA"}},{"cell_type":"markdown","metadata":{"id":"Nin6rzpQX175"},"source":["With that, we can see we've got some Continuous variables and some Categorical variables.\n","\n","\n","Man's search for Missing Values"]},{"cell_type":"code","execution_count":16,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":437},"executionInfo":{"elapsed":1064,"status":"ok","timestamp":1716799032905,"user":{"displayName":"toni Monleon","userId":"05269440626861479105"},"user_tz":-120},"id":"YxKG8FCoX0-3","outputId":"f2e7e8da-6866-4c43-ac1c-33cfc2ca5b15"},"outputs":[{"output_type":"display_data","data":{"text/plain":["plot without title"],"image/png":"iVBORw0KGgoAAAANSUhEUgAAA0gAAANICAIAAAByhViMAAAACXBIWXMAABJ0AAASdAHeZh94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\nAElFTkSuQmCC"},"metadata":{"image/png":{"width":420,"height":420}}}],"source":["choco$Cocoa.Percent[1:20]<-NA\n","\n","plot_missing(choco)"]},{"cell_type":"code","source":["choco1 <- na.omit(choco) #remove missing values\n","plot_missing(choco1)\n","dim(choco1) #data dimension now"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":454},"id":"w1IXgN1zyzi9","executionInfo":{"status":"ok","timestamp":1716799036571,"user_tz":-120,"elapsed":1301,"user":{"displayName":"toni Monleon","userId":"05269440626861479105"}},"outputId":"28e35596-fa5a-468b-ca62-12706330696c"},"execution_count":17,"outputs":[{"output_type":"display_data","data":{"text/html":["\n","
  1. 1775
  2. 9
\n"],"text/markdown":"1. 1775\n2. 9\n\n\n","text/latex":"\\begin{enumerate*}\n\\item 1775\n\\item 9\n\\end{enumerate*}\n","text/plain":["[1] 1775 9"]},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["plot without title"],"image/png":"iVBORw0KGgoAAAANSUhEUgAAA0gAAANICAIAAAByhViMAAAACXBIWXMAABJ0AAASdAHeZh94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"},"metadata":{"image/png":{"width":420,"height":420}}}]},{"cell_type":"markdown","metadata":{"id":"mmvVLDH_YAPz"},"source":["It's very important to see if the input data given for Analysis has got Missing values before diving deep into the analysis."]},{"cell_type":"markdown","metadata":{"id":"JOUx7pn3YDZn"},"source":["**Continuous Variables:**\n","Histogram is analyst's best friend to analyse/represent Continuous Variables."]},{"cell_type":"code","execution_count":18,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":437},"executionInfo":{"elapsed":834,"status":"ok","timestamp":1716799039845,"user":{"displayName":"toni Monleon","userId":"05269440626861479105"},"user_tz":-120},"id":"4EaANvEvMwUB","outputId":"6fa53bf7-b73e-492a-ec5a-9a773f1382c6"},"outputs":[{"output_type":"display_data","data":{"text/plain":["plot without title"],"image/png":"iVBORw0KGgoAAAANSUhEUgAAA0gAAANICAMAAADKOT/pAAAC91BMVEUAAAABAQECAgIDAwME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\nImpD/x9R5ThdWRzWXQAAAABJRU5ErkJggg=="},"metadata":{"image/png":{"width":420,"height":420}}}],"source":["plot_histogram(choco)"]},{"cell_type":"markdown","metadata":{"id":"nG-928_aYKzx"},"source":["Perhaps, you are a fan of Density plot, DataExplorer has got a function for that."]},{"cell_type":"code","execution_count":19,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":437},"executionInfo":{"elapsed":1005,"status":"ok","timestamp":1716799043751,"user":{"displayName":"toni Monleon","userId":"05269440626861479105"},"user_tz":-120},"id":"xbuzB1YrYNIu","outputId":"dbb6acd3-9b10-4160-acea-c445f139d099"},"outputs":[{"output_type":"display_data","data":{"text/plain":["plot without title"],"image/png":"iVBORw0KGgoAAAANSUhEUgAAA0gAAANICAMAAADKOT/pAAADAFBMVEUAAAABAQECAgIDAwME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\ngDZ5AAAAAElFTkSuQmCC"},"metadata":{"image/png":{"width":420,"height":420}}}],"source":["plot_density(choco)"]},{"cell_type":"markdown","metadata":{"id":"4ZtzUt81YRxW"},"source":["**Exploratory Multivariate Analysis**:\n","That marks the end of univariate analysis and the beginning of bivariate/multivariate analysis, starting with Correlation analysis."]},{"cell_type":"code","execution_count":20,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":472},"executionInfo":{"elapsed":902,"status":"ok","timestamp":1716799047651,"user":{"displayName":"toni Monleon","userId":"05269440626861479105"},"user_tz":-120},"id":"_3gvTapfYWmm","outputId":"67344435-fad3-4e39-ec53-2f2e1ceceb37"},"outputs":[{"output_type":"stream","name":"stderr","text":["Warning message:\n","“\u001b[1m\u001b[22mRemoved 4 rows containing missing values (`geom_text()`).”\n"]},{"output_type":"display_data","data":{"text/plain":["plot without title"],"image/png":"iVBORw0KGgoAAAANSUhEUgAAA0gAAANICAIAAAByhViMAAAACXBIWXMAABJ0AAASdAHeZh94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"},"metadata":{"image/png":{"width":420,"height":420}}}],"source":["plot_correlation(choco, type = 'continuous','Review.Date')"]},{"cell_type":"markdown","metadata":{"id":"vVhRnvE1MwCh"},"source":["**Categorical Variables-Barplots**:\n","So far we've seen the kind of EDA plots that DataExplorer lets us plot for Continuous variables and now let us see how we can do similar exercise for categorical variables. Unexpectedly, this becomes one very simple function plot_bar()."]},{"cell_type":"code","execution_count":21,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":558},"executionInfo":{"elapsed":882,"status":"ok","timestamp":1716799050945,"user":{"displayName":"toni Monleon","userId":"05269440626861479105"},"user_tz":-120},"id":"RodKGmXAYsoW","outputId":"e3629f9e-46b3-4d82-feb5-69604a10d267"},"outputs":[{"output_type":"stream","name":"stderr","text":["4 columns ignored with more than 50 categories.\n","Company...Maker.if.known.: 416 categories\n","Specific.Bean.Origin.or.Bar.Name: 1039 categories\n","Company.Location: 60 categories\n","Broad.Bean.Origin: 101 categories\n","\n","\n"]},{"output_type":"display_data","data":{"text/plain":["plot without title"],"image/png":"iVBORw0KGgoAAAANSUhEUgAAA0gAAANICAMAAADKOT/pAAAC8VBMVEUAAAABAQECAgIDAwME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\nAAxAJAAYgEgAMPB/bc2iOCJzZZAAAAAASUVORK5CYII="},"metadata":{"image/png":{"width":420,"height":420}}}],"source":["plot_bar(choco)"]},{"cell_type":"markdown","metadata":{"id":"G83QYwj5Ysfp"},"source":["And finally, if you have got only a couple of minutes (just like in the maggi noodles ad, 2 mins!) just keep it simple to use create_report() that gives a very nice presentable/shareable rendered markdown in html."]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":523},"id":"D7nd7eTqYsYF","outputId":"1c839460-1bd4-47e9-c032-aa1cc2e63ec8","executionInfo":{"status":"error","timestamp":1716652716184,"user_tz":-120,"elapsed":2056,"user":{"displayName":"toni Monleon","userId":"05269440626861479105"}}},"outputs":[{"output_type":"error","ename":"ERROR","evalue":"Error: pandoc document conversion failed with error 2\n","traceback":["Error: pandoc document conversion failed with error 2\nTraceback:\n","1. create_report(choco)","2. suppressWarnings(render(input = report_dir, output_format = output_format, \n . output_file = output_file, output_dir = output_dir, intermediates_dir = output_dir, \n . params = list(data = data, report_config = config, response = y, \n . set_title = report_title), ...))","3. withCallingHandlers(expr, warning = function(w) if (inherits(w, \n . classes)) tryInvokeRestart(\"muffleWarning\"))","4. render(input = report_dir, output_format = output_format, output_file = output_file, \n . output_dir = output_dir, intermediates_dir = output_dir, \n . params = list(data = data, report_config = config, response = y, \n . set_title = report_title), ...)","5. output_format$intermediates_generator(original_input, intermediates_dir)","6. copy_render_intermediates(original_input, intermediates_dir, \n . !self_contained)","7. find_external_resources(original_input)","8. discover_rmd_resources(input_file, discover_single_resource)","9. render(md_file, override_output_format, html_file, quiet = TRUE, \n . output_options = list(self_contained = FALSE, pandoc_args = c(\"--metadata\", \n . \"pagetitle=PREVIEW\")))","10. convert(output_file, run_citeproc)","11. convert_it(output)","12. convert_fun(output = output, input_files, pandoc_to, output_format$pandoc$from, \n . citeproc, pandoc_args, !quiet)","13. stop2(\"pandoc document conversion failed with error \", result)","14. stop(..., call. = FALSE)"]}],"source":["create_report(choco)"]},{"cell_type":"markdown","metadata":{"id":"rXDJsIfHYsOW"},"source":["# Descriptive statistics\n","\n","\n","![alt text](https://image.slidesharecdn.com/01-2-variablesmedicion-110301002439-phpapp01/95/012-variables-medicion-5-728.jpg?cb=1298939112 \"tipos de variables\")\n","\n","\n","\n","![alt text](https://enfermeriaunam.files.wordpress.com/2016/04/variables.jpg?w=1080 \"Escalas d medida\")\n","\n","\n","![alt text](http://slideplayer.es/3339947/11/images/30/%EF%80%B2+Medidas+de+homogeneidad.jpg \"Escalas d medida\")"]},{"cell_type":"code","execution_count":22,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":243,"status":"ok","timestamp":1716799061638,"user":{"displayName":"toni Monleon","userId":"05269440626861479105"},"user_tz":-120},"id":"u5_QPp3jYsGA","outputId":"6cae42e5-0c80-442d-af6f-ef1ecf1ef5df"},"outputs":[{"output_type":"stream","name":"stdout","text":["'data.frame':\t1795 obs. of 9 variables:\n"," $ Company...Maker.if.known. : chr \"A. Morin\" \"A. Morin\" \"A. Morin\" \"A. Morin\" ...\n"," $ Specific.Bean.Origin.or.Bar.Name: chr \"Agua Grande\" \"Kpime\" \"Atsane\" \"Akata\" ...\n"," $ REF : int 1876 1676 1676 1680 1704 1315 1315 1315 1319 1319 ...\n"," $ Review.Date : chr \"2016\" \"2015\" \"2015\" \"2015\" ...\n"," $ Cocoa.Percent : num NA NA NA NA NA NA NA NA NA NA ...\n"," $ Company.Location : chr \"France\" \"France\" \"France\" \"France\" ...\n"," $ Rating : num 3.75 2.75 3 3.5 3.5 2.75 3.5 3.5 3.75 4 ...\n"," $ Bean.Type : chr \" \" \" \" \" \" \" \" ...\n"," $ Broad.Bean.Origin : chr \"Sao Tome\" \"Togo\" \"Togo\" \"Togo\" ...\n"]}],"source":["#Describe \"choco\" variables\n","str(choco)"]},{"cell_type":"markdown","metadata":{"id":"yIt0Gi9Lcigl"},"source":["## Univariate descriptive analysis\n","\n","### Numeric variables\n","\n","The summary function provides a statistical summary of an object. If it is a numeric vector, summary returns the minimum, the first quartile, the median, the mean, the third quartile and the maximum of the data."]},{"cell_type":"code","execution_count":23,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":433},"executionInfo":{"elapsed":304,"status":"ok","timestamp":1716799065288,"user":{"displayName":"toni Monleon","userId":"05269440626861479105"},"user_tz":-120},"id":"k8tH5X36baTa","outputId":"82beb073-0947-4703-a8d9-12654c4e549f"},"outputs":[{"output_type":"display_data","data":{"text/plain":[" Company...Maker.if.known. Specific.Bean.Origin.or.Bar.Name REF \n"," Length:1795 Length:1795 Min. : 5 \n"," Class :character Class :character 1st Qu.: 576 \n"," Mode :character Mode :character Median :1069 \n"," Mean :1036 \n"," 3rd Qu.:1502 \n"," Max. :1952 \n"," \n"," Review.Date Cocoa.Percent Company.Location Rating \n"," Length:1795 Min. : 42.00 Length:1795 Min. :1.000 \n"," Class :character 1st Qu.: 70.00 Class :character 1st Qu.:2.875 \n"," Mode :character Median : 70.00 Mode :character Median :3.250 \n"," Mean : 71.72 Mean :3.186 \n"," 3rd Qu.: 75.00 3rd Qu.:3.500 \n"," Max. :100.00 Max. :5.000 \n"," NA's :20 \n"," Bean.Type Broad.Bean.Origin \n"," Length:1795 Length:1795 \n"," Class :character Class :character \n"," Mode :character Mode :character \n"," \n"," \n"," \n"," "]},"metadata":{}}],"source":["#Other possibility\n","summary(choco)\n","\n","#see also: skim(choco)"]},{"cell_type":"markdown","metadata":{"id":"_vvyqRiMYr84"},"source":["The function mean(x) calculates the mean of a vector or matrix x. The function var(x) calculates the estimators of the variance of a vector x, of the covariance matrix between the columns of a data matrix x, of the covariance between the vectors x and y (var(x,y)) or of the matrix of covariances between the columns of the matrices x and y (var(x,y)). Let's see some examples:"]},{"cell_type":"markdown","metadata":{"id":"4cCPoAKbYrny"},"source":["\n","![alt text](https://image.slidesharecdn.com/regresionlinealmultiple-091014112040-phpapp02/95/regresin-lineal-mltiple-54-728.jpg?cb=1255519273 \"Varianzas-covarianzas\")\n","\n","\n","\n","![alt text](https://image.slidesharecdn.com/1semanaanalisismultivariante-091103223240-phpapp01/95/1-semana-analisis-multivariante-19-728.jpg?cb=1257288619 \"Varianzas-covarianzas\")"]},{"cell_type":"markdown","metadata":{"id":"7VqTRwFUeN6V"},"source":["Other indicators of interest are the range, median, the interquartile range or the correlation between two numerical variables:"]},{"cell_type":"code","execution_count":24,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":216},"executionInfo":{"elapsed":253,"status":"ok","timestamp":1716799070370,"user":{"displayName":"toni Monleon","userId":"05269440626861479105"},"user_tz":-120},"id":"E8h064lfYrZH","outputId":"58c2f51a-763f-4c75-fa96-8dd1547f5932"},"outputs":[{"output_type":"display_data","data":{"text/html":["42"],"text/markdown":"42","text/latex":"42","text/plain":["[1] 42"]},"metadata":{}},{"output_type":"display_data","data":{"text/html":["100"],"text/markdown":"100","text/latex":"100","text/plain":["[1] 100"]},"metadata":{}},{"output_type":"display_data","data":{"text/html":["58"],"text/markdown":"58","text/latex":"58","text/plain":["[1] 58"]},"metadata":{}},{"output_type":"display_data","data":{"text/html":["70"],"text/markdown":"70","text/latex":"70","text/plain":["[1] 70"]},"metadata":{}},{"output_type":"display_data","data":{"text/html":["5"],"text/markdown":"5","text/latex":"5","text/plain":["[1] 5"]},"metadata":{}},{"output_type":"display_data","data":{"text/html":["\n","\n","\n","\t\n","\n","\n","\t\n","\t\n","\n","
A matrix: 2 × 2 of type dbl
Cocoa.PercentRating
Cocoa.Percent 1.0000000-0.1640917
Rating-0.1640917 1.0000000
\n"],"text/markdown":"\nA matrix: 2 × 2 of type dbl\n\n| | Cocoa.Percent | Rating |\n|---|---|---|\n| Cocoa.Percent | 1.0000000 | -0.1640917 |\n| Rating | -0.1640917 | 1.0000000 |\n\n","text/latex":"A matrix: 2 × 2 of type dbl\n\\begin{tabular}{r|ll}\n & Cocoa.Percent & Rating\\\\\n\\hline\n\tCocoa.Percent & 1.0000000 & -0.1640917\\\\\n\tRating & -0.1640917 & 1.0000000\\\\\n\\end{tabular}\n","text/plain":[" Cocoa.Percent Rating \n","Cocoa.Percent 1.0000000 -0.1640917\n","Rating -0.1640917 1.0000000"]},"metadata":{}}],"source":["min(choco$Cocoa.Percent,na.rm=T) #min\n","max(choco$Cocoa.Percent,na.rm=T) #max\n","\n","max(choco$Cocoa.Percent,na.rm=T) - min(choco$Cocoa.Percent,na.rm=T) #range\n","median(choco$Cocoa.Percent,na.rm=T) #median\n","IQR(choco$Cocoa.Percent,na.rm=T) #IQR\n","\n","#Pearson correlations with complete observations\n","cor(choco[,c(5,7)], use = \"complete.obs\", method=c(\"pearson\"))\n","\n"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":129},"executionInfo":{"elapsed":221,"status":"ok","timestamp":1716652748014,"user":{"displayName":"toni Monleon","userId":"05269440626861479105"},"user_tz":-120},"id":"WjIch9BJhKYn","outputId":"409a9fa9-e4e5-4cf3-b62c-31d759c90c1b"},"outputs":[{"output_type":"display_data","data":{"text/html":["\n","\n","\n","\t\n","\n","\n","\t\n","\t\n","\n","
A matrix: 2 × 2 of type dbl
Cocoa.PercentRating
Cocoa.Percent 1.0000000-0.1231922
Rating-0.1231922 1.0000000
\n"],"text/markdown":"\nA matrix: 2 × 2 of type dbl\n\n| | Cocoa.Percent | Rating |\n|---|---|---|\n| Cocoa.Percent | 1.0000000 | -0.1231922 |\n| Rating | -0.1231922 | 1.0000000 |\n\n","text/latex":"A matrix: 2 × 2 of type dbl\n\\begin{tabular}{r|ll}\n & Cocoa.Percent & Rating\\\\\n\\hline\n\tCocoa.Percent & 1.0000000 & -0.1231922\\\\\n\tRating & -0.1231922 & 1.0000000\\\\\n\\end{tabular}\n","text/plain":[" Cocoa.Percent Rating \n","Cocoa.Percent 1.0000000 -0.1231922\n","Rating -0.1231922 1.0000000"]},"metadata":{}}],"source":["#Spearman correlations with complete observations\n","cor(choco[,c(5,7)], use = \"complete.obs\", method=c(\"spearman\"))"]},{"cell_type":"markdown","metadata":{"id":"Wtq3RP5tYrJh"},"source":["\n","![](https://taps-graph-review.wikispaces.com/file/view/outlier_box_plot.gif/73782697/outlier_box_plot.gif \"IQR\")\n","\n","\n","![](https://lon03.files.wordpress.com/2011/10/boxplot.jpg \"IQR\")"]},{"cell_type":"markdown","metadata":{"id":"xTe0fJlRhhq6"},"source":["The function quantile(x,p) computes the empirical quantiles of x for a probability vector p.\n","\n","\n","![alt text](https://upload.wikimedia.org/wikipedia/commons/5/5e/Iqr_with_quantile.png \"quantile\")"]},{"cell_type":"code","execution_count":25,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":593},"executionInfo":{"elapsed":307,"status":"ok","timestamp":1716799074781,"user":{"displayName":"toni Monleon","userId":"05269440626861479105"},"user_tz":-120},"id":"WHTthDrbYq04","outputId":"09074dc5-11a3-4a88-945d-fc01e3a56478"},"outputs":[{"output_type":"stream","name":"stdout","text":["[1] \"-------------------------------------\"\n","[1] \"quantiles 1\"\n"]},{"output_type":"display_data","data":{"text/html":["30%: 70"],"text/markdown":"**30%:** 70","text/latex":"\\textbf{30\\textbackslash{}\\%:} 70","text/plain":["30% \n"," 70 "]},"metadata":{}},{"output_type":"stream","name":"stdout","text":["[1] \"-------------------------------------\"\n","[1] \"quantiles 2\"\n"]},{"output_type":"display_data","data":{"text/html":["
10%
65
30%
70
60%
72
80%
75
\n"],"text/markdown":"10%\n: 6530%\n: 7060%\n: 7280%\n: 75\n\n","text/latex":"\\begin{description*}\n\\item[10\\textbackslash{}\\%] 65\n\\item[30\\textbackslash{}\\%] 70\n\\item[60\\textbackslash{}\\%] 72\n\\item[80\\textbackslash{}\\%] 75\n\\end{description*}\n","text/plain":["10% 30% 60% 80% \n"," 65 70 72 75 "]},"metadata":{}},{"output_type":"stream","name":"stdout","text":["[1] \"-------------------------------------\"\n","[1] \"quantiles 3\"\n"]},{"output_type":"display_data","data":{"text/html":["
20%
70
30%
70
40%
70
50%
70
60%
72
70%
73
80%
75
90%
80
\n"],"text/markdown":"20%\n: 7030%\n: 7040%\n: 7050%\n: 7060%\n: 7270%\n: 7380%\n: 7590%\n: 80\n\n","text/latex":"\\begin{description*}\n\\item[20\\textbackslash{}\\%] 70\n\\item[30\\textbackslash{}\\%] 70\n\\item[40\\textbackslash{}\\%] 70\n\\item[50\\textbackslash{}\\%] 70\n\\item[60\\textbackslash{}\\%] 72\n\\item[70\\textbackslash{}\\%] 73\n\\item[80\\textbackslash{}\\%] 75\n\\item[90\\textbackslash{}\\%] 80\n\\end{description*}\n","text/plain":["20% 30% 40% 50% 60% 70% 80% 90% \n"," 70 70 70 70 72 73 75 80 "]},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Plot with title “Histogram of choco$Cocoa.Percent”"],"image/png":"iVBORw0KGgoAAAANSUhEUgAAA0gAAANICAIAAAByhViMAAAACXBIWXMAABJ0AAASdAHeZh94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"},"metadata":{"image/png":{"width":420,"height":420}}}],"source":["#quantiles\n","#In statistics and probability, quantiles are cut points dividing the range of a probability distribution into continuous intervals with equal probabilities,\n","\n","hist(choco$Cocoa.Percent, col=\"red\",breaks = 30)\n","print(\"-------------------------------------\")\n","print(\"quantiles 1\")\n","quantile(choco$Cocoa.Percent,0.3,na.rm=T) #calcular quantiles\n","print(\"-------------------------------------\")\n","print(\"quantiles 2\")\n","quantile(choco$Cocoa.Percent,c(0.1,0.3,0.6,0.8),na.rm=T)\n","print(\"-------------------------------------\")\n","print(\"quantiles 3\")\n","quantile(choco$Cocoa.Percent,seq(.2,.9,.1),na.rm=T)\n","\n"]},{"cell_type":"markdown","source":["Is normal the distribution of data for the choco percentage?"],"metadata":{"id":"FV8uQoI6-WuH"}},{"cell_type":"code","source":["#normal curve over the histogram\n","m<-mean(choco$Cocoa.Percent,na.rm=T)\n","std<-sqrt(var(choco$Cocoa.Percent,na.rm=T))\n","\n","hist(choco$Cocoa.Percent, density=20, breaks=30, prob=TRUE,\n"," xlab=\"x-variable\",\n"," main=\"normal curve over histogram\", col=\"red\")\n","\n","\n","xfit <- seq(min(choco$Cocoa.Percent,na.rm=T) , max(choco$Cocoa.Percent,na.rm=T) , length = 40)\n","yfit <- dnorm(xfit, mean = m, sd = std)\n","\n","lines(xfit, yfit, col = \"black\", lwd = 2)\n"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":437},"id":"YgLs06919qxA","executionInfo":{"status":"ok","timestamp":1716799115873,"user_tz":-120,"elapsed":440,"user":{"displayName":"toni Monleon","userId":"05269440626861479105"}},"outputId":"f2825a54-5aa9-4baf-a381-f57aa9ae2146"},"execution_count":26,"outputs":[{"output_type":"display_data","data":{"text/plain":["Plot with title “normal curve over histogram”"],"image/png":"iVBORw0KGgoAAAANSUhEUgAAA0gAAANICAIAAAByhViMAAAACXBIWXMAABJ0AAASdAHeZh94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\ngg=="},"metadata":{"image/png":{"width":420,"height":420}}}]},{"cell_type":"markdown","source":["Quite normal! But, can we apply a normal test to check-it?"],"metadata":{"id":"uI-kLhB4Azbi"}},{"cell_type":"code","source":["m<-mean(choco$Cocoa.Percent,na.rm=T)\n","std<-sqrt(var(choco$Cocoa.Percent,na.rm=T))\n","ks.test(choco$Cocoa.Percent, \"pnorm\", mean=m, sd=std)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":156},"id":"9GkkV2IrBF2f","executionInfo":{"status":"ok","timestamp":1716799120117,"user_tz":-120,"elapsed":328,"user":{"displayName":"toni Monleon","userId":"05269440626861479105"}},"outputId":"f4bc369d-253b-4261-8ec5-35427cb1b4ee"},"execution_count":27,"outputs":[{"output_type":"stream","name":"stderr","text":["Warning message in ks.test.default(choco$Cocoa.Percent, \"pnorm\", mean = m, sd = std):\n","“ties should not be present for the one-sample Kolmogorov-Smirnov test”\n"]},{"output_type":"display_data","data":{"text/plain":["\n","\tAsymptotic one-sample Kolmogorov-Smirnov test\n","\n","data: choco$Cocoa.Percent\n","D = 0.20898, p-value < 2.2e-16\n","alternative hypothesis: two-sided\n"]},"metadata":{}}]},{"cell_type":"markdown","source":["Unforntunatelly data are non-normal: p<0.05 (alpha)"],"metadata":{"id":"BtozT3NfBU29"}},{"cell_type":"markdown","source":["MORE POSSIBILITIES FOR NORMALITY TEST: shapiro.test(my_data$len)"],"metadata":{"id":"KKtnbtqbLOK5"}},{"cell_type":"code","source":["#Shapiro-Wilk’s method is widely recommended for normality test and it provides better power than K-S. It is based on the correlation between the data and the corresponding normal scores.\n","#SEE IN: http://www.sthda.com/english/wiki/normality-test-in-r\n","shapiro.test(choco$Cocoa.Percent)\n","\n","#What is the consequence of this unnormality?\n","\n"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":191},"id":"EzT63z4ALTvv","executionInfo":{"status":"ok","timestamp":1716799187710,"user_tz":-120,"elapsed":233,"user":{"displayName":"toni Monleon","userId":"05269440626861479105"}},"outputId":"201e73f6-7cd5-4aa8-f9bc-fe054dc1b925"},"execution_count":31,"outputs":[{"output_type":"display_data","data":{"text/plain":["\n","\tShapiro-Wilk normality test\n","\n","data: choco$Cocoa.Percent\n","W = 0.8738, p-value < 2.2e-16\n"]},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["\n","\tShapiro-Wilk normality test\n","\n","data: sqrt(choco$Cocoa.Percent)\n","W = 0.88773, p-value < 2.2e-16\n"]},"metadata":{}}]},{"cell_type":"markdown","metadata":{"id":"CNa8Ex5YA-rJ"},"source":["# Boxplot\n","\n","Now lets go to represent the Boxplot\n","\n","In descriptive statistics, a box plot or boxplot is a method for graphically demonstrating the locality, spread and skewness groups of numerical data through their quartiles.[1] In addition to the box on a box plot, there can be lines (which are called whiskers) extending from the box indicating variability outside the upper and lower quartiles, thus, the plot is also termed as the box-and-whisker plot and the box-and-whisker diagram. Outliers that differ significantly from the rest of the dataset may be plotted as individual points beyond the whiskers on the box-plot. Box plots are non-parametric: they display variation in samples of a statistical population without making any assumptions of the underlying statistical distribution. See in wikipedia https://en.wikipedia.org/wiki/Box_plot"]},{"cell_type":"code","execution_count":32,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":437},"executionInfo":{"elapsed":389,"status":"ok","timestamp":1716799244894,"user":{"displayName":"toni Monleon","userId":"05269440626861479105"},"user_tz":-120},"id":"EVG8_wS5A6bo","outputId":"4439df09-80ca-494f-aee5-4114dd6490e1"},"outputs":[{"output_type":"display_data","data":{"text/plain":["plot without title"],"image/png":"iVBORw0KGgoAAAANSUhEUgAAA0gAAANICAMAAADKOT/pAAAC91BMVEUAAAAAWQAAYAAA7gAA\n/wABAQECAgIDAwMEBAQFBQUGBgYHBwcICAgJCQkKCgoLCwsMDAwNDQ0ODg4PDw8QEBARERES\nEhITExMUFBQVFRUWFhYXFxcYGBgZGRkaGhobGxscHBwdHR0eHh4fHx8gICAhISEiIiIjIyMk\nJCQlJSUmJiYnJycpKSkqKiorKyssLCwtLS0uLi4vLy8wMDAxMTEyMjIzMzM0NDQ1NTU2NjY3\nNzc4ODg5OTk6Ojo7Ozs8PDw9PT0+Pj4/Pz9AQEBBQUFCQkJDQ0NERERFRUVGRkZHR0dISEhJ\nSUlKSkpLS0tMTExNTU1OTk5PT09QUFBRUVFSUlJTU1NUVFRVVVVWVlZXV1dYWFhZWVlaWlpb\nW1tcXFxdXV1eXl5fX19gYGBhYWFiYmJjY2NkZGRlZWVmZmZnZ2doaGhpaWlqampra2tsbGxt\nbW1ubm5vb29wcHBxcXFycnJzc3N0dHR1dXV2dnZ3d3d4eHh5eXl6enp7e3t8fHx9fX1+fn5/\nf3+AgICBgYGCgoKDg4OEhISFhYWGhoaHh4eIiIiJiYmKioqLi4uMjIyOjo6Pj4+QkJCRkZGS\nkpKTk5OUlJSVlZWWlpaYmJiZmZmampqbm5udnZ2enp6fn5+goKChoaGioqKjo6OkpKSlpaWm\npqanp6eoqKipqamqqqqrq6usrKytra2urq6wsLCysrKzs7O0tLS1tbW2tra3t7e4uLi5ubm6\nurq7u7u9vb2+vr6/v7/AwMDBwcHCwsLDw8PExMTFxcXGxsbHx8fIyMjJycnKysrLy8vMzMzN\nzc3Ozs7Pz8/Q0NDR0dHS0tLT09PU1NTV1dXW1tbX19fY2NjZ2dna2trb29vc3Nzd3d3e3t7f\n39/g4ODh4eHi4uLj4+Pk5OTl5eXm5ubn5+fo6Ojp6enq6urr6+vs7Ozt7e3u7u7v7+/w8PDx\n8fHy8vLz8/P09PT19fX29vb39/f4+Pj5+fn6+vr7+/v8/Pz9/f3+/v7///8o5UrlAAAACXBI\nWXMAABJ0AAASdAHeZh94AAAfo0lEQVR4nO3dfZzVVZ3A8W/t/u7lzjCDIw6gIAQzpLjmwyCS\nYoo7iWVZhppBZMUE5RMoa+jiAyukogZSVKxRaWnpoo2pKwYaa9qDzCoaIAJqZODjDChPIwO/\nP3bu/IYZWqcfOfM953DP+bz/4HdecpLvi5+fZu69Z+6VGECXiesBAB8QEqCAkAAFhAQoICRA\nASEBCggJUEBIgAJCAhQQEqCAkAAFhAQoICRAASEBCggJUEBIgAJCAhQQEqCAkAAFhAQoICRA\nASEBCggJUEBIgAJCAhQQEqCAkAAFhAQoICRAASEBCggJUEBIgAJCAhQQEqCAkAAFhAQoICRA\nASEBCggJUEBIgAJCAhQQEqCAkAAFhAQoICRAASEBCggJUEBIgAJCAhQQEqCAkAAFhAQoICRA\nASEBCggJUEBIgAJCAhQQEqCAkAAFhAQoICRAASEBCggJUEBIgAJCAhQQEqCAkAAFhAQoICRA\nASEBCggJUEBIgAJCAhQQEqCAkAAFhAQoICRAASEBCggJUEBIgAJCAhQQEqCAkAAFhAQoICRA\nASEBCggJUEBIgAJCAhQQEqCAkAAFhAQoICRAASEBCggJUEBIgAILIT2zDCgoz7z//8rNh/SU\nAAXmqff9n7n5kJ6QRuN/BqCoUZ543/8bQgL+H0ICFBASoICQAAWEBCiwHdLudYtra5es38cu\nQkKBsRtS/dRDkufcB12/LW0fIaHAWA1pw2AZUjNj9uzp4/pLVX3KRkJCgbEa0sTcwtZV0/xo\nSspGQkKBsRpSvwnt67EDUzYSEgqM1ZBys9rXM4tSNhISCozVkCrGtK9HV6ZsJCQUGKshTYnm\n7EhWW66TaSkbCQkFxmpIDcOkbFTN5Enjq0tl5DspGwkJBcbu60iNc4dm8y8j5UYsaErbR0go\nMNaPCG1/oa5uzb4yISQUGI4IhSTX8u2A6ym8xBGhgETJX37keg4fcUQoHFGSUERJBnBEKBwi\nf3uFov3niNCuxxa3+S4h6ZsgmWSRkUvcTuKj/eeI0IsHl7cplS2d/TPw91RKabIokbRTJeiU\n/fOI0G2S9nItOuXePY+NIrnX7SQ+2j+PCBGSCSJv5C+reIxkwP55RIiQTCgROSyOD5M93+JB\n0f55RIiQjMgmryNlXc/ho/3ziBAhmXF5JBJd7noKLzl7O64316T8JiGZsOtoqTzvvEo5epfr\nSTzkLKRpaf8WQjLhy9GC/GVB9BXXk3iIkMJRdFpy/XjaT/mjcwgpGOvlzmTxM3nF7SQ+shrS\n8XvpR0iWLZeHksVDstztJD6yGlImU9wmS0iWbY+uTBZXRJxkVGc1pGll7U/V8a2ddYf1aPlb\nfafH4a4n8ZDVkN49bvi7e9aEZN2ybPnPtm+/ozy7zPUkHrL7ZMPKkiv2LAnJvsf75A829Hnc\n9Rw+svys3ea39qx+c3PKNkIyZMWtt65wPYOf9s8PGiMkFBhCAhQQEqCAkAAFhAQoICRAASEB\nCggJUEBIgAJCAhQQEqCAkAAFhAQoICRAASEBCggJUEBIgAJCAhQQEqCAkAAFhAQoICRAASEB\nCggJUEBIgAJCAhQQEqCAkAAFhAQoICRAASEBCggJUEBIgAJCAhQQEqCAkAAFhAQoICRAASEB\nCggJUEBIgAJCAhQQEqCAkAAFhAQoICRAASEBCggJUEBIgAJCAhQQEqCAkAAFhAQoICRAASEB\nCggJUEBIgAJCAhQQEqCAkAAFhAQoICRAASEBCggJUEBIgAJCAhQQEqCAkAAFhBSSGX1yuT4z\nXE/hJUIKSIXkBg7MSYXrOXxESOH4pJyfv5wvZ7iexEOEFI6of3Ltn3E7h5dsh7R73eLa2iXr\n97GLkAx4Qa5NFtfKOreT+MhuSPVTD5EWg67flraPkAxYLD9KFj+SxU4H8ZLVkDYMliE1M2bP\nnj6uv1TVp2wkJAM2ydhkMZa/XX1WQ5qYW9i6apofTUnZSEgmlHZrzF8au5W6nsRDVkPqN6F9\nPXZgykZCMuEuKbk3ju8tkbtcT+IhqyHlZrWvZxalbCQkI+Zm8o9PM3Ndz+EjqyFVjGlfj65M\n2UhIZhyQD+kA11N4yWpIU6I5O5LVlutkWspGQjKhobmiXK75lwbXk3jIakgNw6RsVM3kSeOr\nS2VkWiqEZEJWjs5fPiJZ15N4yO7rSI1zh2bz313kRixoSttHSCZI64mGjPmjKeGxfkRo+wt1\ndWsaO/qNebe0OYeQ9N2WfEHKf0n6sdtJfGQ9pJ1/WfXipo5+45URx7cZJG935c9AR6bKacni\nNLnK7SQ+svyt3Q9GFOW/tes//vep+/jWzoCN0vpCbIlsdDuJj6yGtPkEKTl2RO7w84+L5Mq0\njYRkgiT3+gnhMZI+qyFdLv/W/F3dqop58ctny+0pGwnJhKkiAx57bIDIVNeTeMhqSB/6TMvl\nrgO3xE3HD0vZSEhGXJwcvb/Y9Rw+sntE6PqWyyr5bRx/qyRlIyEZ0XhusUjxuR09Z4oushrS\nockRoftkaRxf1jtlIyGZ8E6fqHrWrOqoD3+5+qyGND760e44fraix5b4jwd9KmUjIZlweubx\n/GVp5nTXk3jIakgv9ZL+p34kkh/ETbkef0zZSEgG7MqelyzOze5yO4mP7L6OtG5smXQb+Wjz\n6t+fS9tHSAaslXuTxb2y1u0kPrJ+smHLP/L/hoRkwAr5VbK4X1a5ncRHvB1XMHZmvp4sJmZ2\nup3ER4QUjmG5l/OXl3PHu57EQ4QUjvXdiyY9+ujFRd1fcT2JhwgpIK9URSJRFR0ZQEgBqU2O\nCD3geg4fEVI47hEpGjmySOQe15N4iJDCIfLt/OXb/BiFAYQUjAZpfSvBItnudhIfEVIwZsrH\nksXHZKbbSXxESMH4tpyQLIYL77WqjpCCsX3P+9ll+dZOHyGFIyNfy1++Jnxinz5CCscTIpm+\nfTMiy1xP4iFCCsjq5NMoVruew0eEFJCNY5pLyozhXe0MIKRwvDb4xMWbNy8+YfBrrifxECGF\n44Kqlk/A3lZ1oetJPERIwWjq2XrG7u6eqR8Fgs4gpGC8uucnzFfKq24n8REhBaNB/jdZ1PGR\nffoIKRyH3ZJcbznc7RxeIqRwfPeg5fnL8oPmuZ7EQ4QUjl3nl15yxx0Xl36Z94fUR0ghuaf6\n4IOr+flYEwgpICtHSHm5nLTS9Rw+IqRwvNjnc6vjePXn+rzkehIPEVI4xp3a8kJs06njHA/i\nI0IKxs7S+5PF/aW8ZbE6QgrGBnk+WTwvG9xO4iNCCsbb0vqRVH+Qt91O4iNCCscx1ybXa49x\nO4eXCCkcd5b8On/5dfc7XU/iIUIKyNXZz95ww2ezV7uew0eEFJIJUbMJrqfwEiEFJJN8GgXv\nxmUAIYWjuaOBcTyQkkwgpHDs+RQKPo3CAEIKxmSJkkUkk91O4iNCCkau/b2/i9xO4iNCCsYp\nex4bZeQUt5P4iJDCwWMkgwgpHFHyICna81gJiggpIFHyOhIdGUBIIck2pxRlXU/hJUIKyEdF\nMhmRj7qew0eEFI6x0rshjht6yzjXk3iIkMIR5ZJrjgdJ+ggpGC/L2clitLzsdhIfEVIwfiE3\nJYub5BduJ/ERIQWj7SvSWXxF0kdI4eAxkkGEFI5xUv5GHL9RLl9yPYmHCCkgJyavI53oeg4f\nEVJIJmVFspNcT+ElQgrIAJHiYpEBrufwESGF41T5SP5ypJzqehIPEVI4pCS5lvDzSPoIKRgr\n9jxbN05Wu53ER4QUjL1ONvyX20l8REjB2CifSBajZKPbSXxESOHIZFo+YGxnhjeI1EdI4bhK\nipbG8dIiudr1JB4ipIB8KXnPBk4IGUBIIWk4d9CgcxtcT+ElQgIUEBKggJAABYRUWNYu7Jrv\nf7+L/4K1rv8G9lOEVFhu/XDXlJV18V9wq+u/gf0UIYWlpsb1BJ4ipLAQkiGEFBZCMoSQwkJI\nhhBSWGbPdj2BpwgJUEBIgAJCAhTYDmn3usW1tUvW72MXIZmylpMJZtgNqX7qIcmPxAy6flva\nPkIyZcIE1xN4ympIGwbLkJoZs2dPH9dfqupTNhKSKTz9bYjVkCbmFraumuZHU1I2EpIphGSI\n1ZD67fV9xdiBKRsJyRRCMsRqSLlZ7euZRSkbCckUQjLEakgVY9rXoytTNhKSKZxsMMRqSFOi\nOTuS1ZbrZFrKRkJCgbEaUsMwKRtVM3nS+OpSGZmWCiGhwNh9Halx7tBs/mWk3IgFTWn7CAkF\nxvoRoe0v1NWtaezgNzZdekGbkYRkCCcbDHFy1u7dPy3b8Z5/+Ob5X2hzvLzd1T8DHeJkgyF2\nQ1pSXfnp38eL+oscOD9tH9/amcLT34ZYDenJbnJgpseTBw786phyeThlIyGZQkiGWA3prH7L\n49c/PqhqWxzXV34qZSMhmUJIhlgNqfcNzb88JXfk1zf2StlISKYQkiFWQ+r2s+ZfNshD+fWP\nu6VsJCRTONlgiNWQ+s5o/uU3Mi+/vqZvykZCQoGxGtIXez3a+OwxRw56JY5Xlp+XspGQUGCs\nhrSqTER6rawo/fhJ3bJ/SNlISCgwdl9Hem7ciTXPx8+dEMmH70/bR0imcLLBEDfvIvTO6+m/\nT0imcLLBEN6OKyw8/W0IIYWFkAwhpLAQkiGEFBZCMoSQwsLJBkMICVBASIACQgIUEFJYONlg\nCCGFhZMNhhBSWHj62xBCCgshGUJIYSEkQwgpLIRkCCGFhZMNhhASoICQAAWEBCggpLBwssEQ\nQgoLJxsMIaSw8PS3IYQUFkIyhJDCQkiGEFJYCMkQQgoLJxsMISRAASEBCggJUEBIYeFkgyGE\nFBZONhhCSGHh6W9DCCkshGQIIYWFkAwhpLAQkiGEFBZONhhCSIACQgIUEJJdW9cFbqvrO2AI\nIdl1sQTuYtd3wBBCsqvmA/8UtA/4+qwhIdlV88F/DtoHCakdIXUeIbm+A4YQkl2E5PoOGEJI\ndhGS6ztgCCHZRUiu74AhhGQXIbm+A4YQkl2E5PoOGEJIdhGS6ztgCCHZRUiu74AhhGQXIbm+\nA4YQkl2E5PoOGEJIdhGS6ztgCCHZRUiu74AhhGQXIbm+A4YQkl2E5PoOGEJIdhGS6ztgCCHZ\nRUiu74AhhGQXIbm+A4YQkl2E5PoOGEJIdhGS6ztgCCHZRUiu74AhhGQXIbm+A4YQkl2E5PoO\nGEJIdhGS6ztgCCHZRUiu74AhhGQXIbm+A4YQkl2E5PoOGEJIdhGS6ztgCCHZRUiu74AhhGQX\nIbm+A4YQkl2E5PoOGEJIdhGS6ztgCCHZRUiu74AhhGQXIbm+A4YQkl2E5PoOGEJIdhGS6ztg\nCCHZRUiu74AhtkPavW5xbe2S9fvYRUi+IqS9dD6k+qmHSItB129L20dIviKkvXQ6pA2DZUjN\njNmzp4/rL1X1KRsJyVeEtJdOhzQxt7B11TQ/mpKykZB8RUh76XRI/Sa0r8cOTNlISL4ipL10\nOqTcrPb1zKKUjYTkK0LaS6dDqhjTvh5dmbKRkHxFSHvpdEhTojk7ktWW62RaykZC8hUh7aXT\nITUMk7JRNZMnja8ulZFpqRCSrwhpL51/Halx7tBs/mWk3IgFTWn7PA5JAkdI7bp0RGj7C3V1\naxo7+I2Nnz69zZHydlf+jP0YIbm+A4ZYDemlt1J+c8uMaW3O8PYr0pOu/0N27UnXd8AQqyFJ\n9xs7+lr0Xv5+axefHjjXf/+m2A2pMnvkY//IRo9Dgp/shjTtqeNk1OP73khIpixd6noCT1kO\nKW76zsFy6k837WMjIZlS4+uDfddshxTHW27uLdmPTrzxeykbCckUQjLEfkhxvPXOs3qIpP1b\nCMkUQjLERUj5P/bpe/4zZSMhmUJIhjgKaR8IyRRCMsRqSMXT/8GNhGTK3Xe7nsBTvIsQoICQ\nAAWEBCggpLBwssEQQgoLz9oZQkhhISRDCCkshGQIIYWFkAwhpLAQkiGEFBZONhhCSIACQgIU\nEBKggJDCwskGQwgpLDxrZwghhYWQDCGksBCSIYQUFkIyhJDCQkiGEFJYONlgCCEBCggJUEBI\ngAJCCgsnGwwhpLDwrJ0hhBQWQjKEkMJCSIYQUlgIyRBCCgshGUJIYeFkgyGEBCggJEABIQEK\nCCksnGwwhJDCwrN2hhBSWAjJEEIKCyEZQkhhISRDCCkshGQIIYWFkw2GEBKggJAABYQEKCCk\nsHCywRBCCgvP2hlCSGEhJEMIKSyEZAghhYWQDCGksBCSIYQUFk42GEJIgAJCAhQQEqCAkMLC\nyQZDCCksPGtnCCGFhZAMIaSwEJIhhBQWQjKEkArL0mldc8wxXfwX8GRFxwipsNx9etccc0wX\n/wWcjOgYIQEKCAlQQEiAAkIKy5gxrifwFCEFZJ60mOd6Dh8RUjiuEYnKyyORG1xP4iFCCofI\nOfnLOWL+BoaHkIKxWaJkEclmt5P4iJCC8VnplSx6JV+ZoImQgjFGypNFufDUnTpCCgbf2plE\nSOEQOS5/OY4nGwwgpHDcKRLlcpHIna4n8RAhBWRplH89NuInIQwgpIBclZxsmO56Dh8RUjhm\nivS+7bbeIjNdT+IhQgqHyKL8ZRFPNhhASMHYKCXJors0uJ3ER7ZD2r1ucW3tkvX72EVIBlwl\n1fGbjz32ZlwtV7mexT92Q6qfekjyeHfQ9dvS9hGSAbfJUWfmn/7+zJFym+tZ/GM1pA2DZUjN\njNmzp4/rL1X1KRsJyQSJqn/X2Pi7f414jKTPakgTcwtbV03zoykpGwnJhEhOzF+G7zkqBEVW\nQ+o3oX09dmDKRkIyoKms+Xvq7t2bfylrcj2Lf6yGlJvVvp5ZlLKRkAzYKE/nK5LuT8tG17P4\nx2pIFXsd3x9dmbKRkAzYJNnkmZ6sbHI9i3+shjQlmrMjWW25TqalbCQkEzLNDcVxtuVXKLMa\nUsMwKRtVM3nS+OpSGZmWCiGZIPL1/OXrnGwwwO7rSI1zh7Z8e5EbsSD18S4hGXCDSNFX58//\nShFvI2SA9SNC21+oq1vT2MFv/PmID7fpI2935c9AR3KSfXDsUUeNfTArOdez+MfFWbumZ5/o\n4IzQztqFbb7BVyR9AySTLDIywO0kPrIb0hOTmn+5s2/zN3dVqT9dxrd2Jux5bMRjJAOshvRY\n0QG743vlgC9c8olM8bKUjYRkQpQcaYg42WCA1ZCqD1kTx4MrNjQvf19yVspGQjIi/3Pm+Z82\ndz2Hj6yGdOAV+ZcFk/dw/8ZBKRsJyYxM/inTjOspvGQ1pB7/Ecc7ol+2rL/VPWUjIaHAWA3p\n5CFb4/hjV+SXO6qqUjYSEgqM1ZAekGGP7Kw79Kdb3/39afLDlI2EhAJj9+nvH/WQko9USDYr\n0Td3p+wjJBQYyy/IvjrnjIqy4t7HX1aXuo2QUGB4FyFAASEBCggJUEBIgAJCCsltxSLFvKmd\nCYQUkAOS92w4wPUcPiKkcFRIZnUcr85I2vvOoHMIKRz8PJJBhBSMJ+TQZNG3E/cc+0BIwZgs\nZySLM2Sy20l8REjBaPuKdChfkfQRUjh4jGQQIYVjkGReiuOXMlLhehIPEVJAeiSvI/VwPYeP\nCCkkM/Jv/D3D9RReIqSAzC+quuCCqqL5rufwESGF48Fud+Qvt3d7yPUkHiKkcJxwaXK99ES3\nc3iJkIKxJfptsng82up2Eh8RUjD+KquTxWrZ4HYSHxFSMBqLFiWLh4s6+lgddAkhheOzo5Pr\n59Ledh2dQ0jheLbHRZvieNNFPZ51PYmHCCkgj1cWVR1bNPhx13P4iJBCMr+ye/dKXo81gZAC\ncpL0HTWqr5zkeg4fEVI4LpJb8pdb5CLXk3iIkMLR/eTkenLaR1OhcwgpGBvkJ8niJ7wgq4+Q\ngvG0PJwsHpan3U7iI0IKxna5NllcLdvdTuIjQgpHRc+Wo0GNPflRc32EFI6lmX6/juNH+mV4\nRVYfIQXkwQMkiqTsQddz+IiQQhLl3/skcj2FlwgpHC/lK8q39JLrSTxESOGIkjfi6sHXJAMI\nKRx7Aop4p1V9hBSM86X1aFCxnO92Eh8RUjBGSHmyOEhGuJ3ER4QUjGfav7V7xu0kPiKkcIh8\nN3/5Lp9GYQAhheMUkcyFF0Yip7iexEOEFJDhyadRDHc9h48IKSg3FRff5HoGPxESoICQAAWE\nBCggJEABIQEKCAlQQEiAAkICFBBSWNavdz2BpwgpIJsvPUjkoEs3u57DR4QUjk3H/svP1669\n64hjKUkfIYXj8iEN+UvDkG+6nsRDhBSMXX3uSBa399nldhIfEVIwXpMVyeJP8rrbSXxESMF4\nS5Yni+XylttJfERIwdg98HvJYt7A3W4n8REhhePGvi/lLy/2vdHxID4ipHA0ntH7W4sWzex1\nRqPrSTxESAFpmje8tHT4vCbXc/iIkMKyi2e+zSAkQAEhAQoICVBASIACQgIUEBKggJAABYQE\nKCAkQAEhAQoICVBASIACQgIU2A5p97rFtbVL9vUuhYSEAmM3pPqphyQfYzro+m1p+wgJBcZq\nSBsGy5CaGbNnTx/XX6rqUzYSEgqM1ZAm5ha2rprmR1NSNhISCozVkPpNaF+PHZiykZBQYKyG\nlJvVvp5ZlLKRkMzY8fCcOQ/vcD2Fl6yGVDGmfT26MmUjIRnx6IdKhw8v/dCjrufwkdWQpkRz\nWv/vcMt1Mi1lIyGZ8HTJZc1/rW9fVvK060k8ZDWkhmFSNqpm8qTx1aUyMi0VQjLhzHOT6zln\nup3DS3ZfR2qcOzSbfxkpN2LBe99cbUt9m7mEpG9H7pFk8UiOd4hUZ/2I0PYX6urWdHQj12Zl\nL2935c9AR/4qq5PFavmr20l85OKsXeMfH32xg3+8fFmba/iKpG9r5vFk8T+ZrW4n8ZHVkG5o\neb7otvLmrzjHpz7g5TGSCSddklwvOcnpGH6yGlLLM3UPSvE5F54sPdembCQkExZ1W5C//LDb\nIteTeMh+SEN6rmz+9ZfR11I2EpIRC4qPrKk5svsC13P4yHpIr8s1LeuzB6RsJCQz/nxrTc2c\nP7uewkvWQ1ovd7asp+dSNhISCoz1kJp63tyyntArZSMhocDYDWncU2veuPrw/JOvq3qclbKR\nkFBg7IaUuC+Of94j88eUjYSEAmM1pNu/M2PK+LOrl8Tx/AEPpG0kJBQYR+8i9E76JzASEgoM\nb8cFKCAkQAEhAQoICVBASIACQgIUEBKggJAABYQEKCAkQAEhAQoICVBASIACQgIUEBKggJAA\nBYQEKCAkQAEhAQoICVBASIACQgIUEBKggJACsvv20w4++LTbd7uew0eEFI6dny+bunDh1LLP\n73Q9iYcIKRxzeq/KX1b2vtX1JB4ipHBUtgZ062C3c3iJkIJRL60fgP201LudxEeEFIxXZWWy\nWCGvuZ3ER4QUjF3lP08Wd5WnfxYIOoGQwjHpqJa/1XeOmuR6Eg8RUjjePGLor15//VdDj3jT\n9SQeIqSAvDG+WKR4/Buu5/ARIQXl3RUr3nU9g58ICVBASIACQgIUEBKggJAABYQEKCAkQAEh\nAQoICVBASIACQgIUEBKggJAABYQEKCAkQAEhAQoICVBASIACQgIUEBKggJAABYQEKCAkQAEh\nAQoICVBASIACQgIUEBKggJBCsmvZHXcs41PGTCCkgNQdLZWVcnSd6zl8REjheL7nlzbG8cYv\n9VztehIPEVI4zv3k7vxl1yc/73oSDxFSMN7t/mCyeKA7HzamjpCCsUFav6V7Xja4ncRHhBSM\nd+R3yeLJaIvbSXxESOEYdmVyvWKY2zm8REjhuLeoNn+pLbrP9SQeIqSA3Jw99corT83e7HoO\nHxFSSJZPO/PMactdT+ElQgIUEBKggJAABbZD2r1ucW3tkvX72EVIKDB2Q6qfeoi0GHT9trR9\nhIQCYzWkDYNlSM2M2bOnj+svVfUpGwkJBcZqSBNzC1tXTfOjKSkbCQkFxmpI/Sa0r8cOTNlI\nSCgwVkPKzWpfzyxK2UhIKDBWQ6oY074eXZmykZBQYKyGNCWasyNZbblOpqVsJCQUGKshNQyT\nslE1kyeNry6VkWmpEBIKjN3XkRrnDs3mX0bKjVjQlLaPkFBgrB8R2v5CXd2axo5+57llba4h\nJBQWN2ftNk9b9Z5/tjaSvWzt8p8BWOQmpL/IA+/9h+/Ut1kkHX7NAvZXdk827DFOPjlxYsrG\nJwgJhcVqSPI3UjY+JUCBecpeSJdnhy5qyFsh9zQ0pO18ZhlQUJ55/z10/jHSU0OjizfFf+cx\nEhCWLjzZsPOWkv73ERIQd/FZu7Wj5Kz1hAR09env23sdMIOQgK6+jvTaF4WQgK6/IPvfU1cq\nzAEUNPNvxwUEgJAABYQEKCAkQAEhAQoICVBASIACQgIUEBKggJAABYQEKCAkQAEhAQoICVBA\nSIACQgIUEBKggJAABYQEKCAkQAEhAQoICVBASIACQgIUEBKggJAABYQEKCAkQAEhAQoICVBA\nSIACQgIUEBKggJAABYQEKCAkQAEhAQoICVBASIACQgIUEBKggJAABYQEKCAkQAEhAQoICVBA\nSIACQgIUEBKggJAABYQEKCAkQAEhAQoICVBASIACQgIUEBKggJAABYQEKCAkQAEhAQoICVBA\nSIACQgIUEBKggJAABYQEKCAkQAEhAQoICVBASIACQgIUEBKggJAABYQEKCAkQAEhAQoICVBA\nSIACQgIUEBKggJAABYQEKCAkQAEhAQoICVBASIACQgIUEBKggJAABYQEKCAkQAEhAQoICVBA\nSIACQgIUEBKggJAABYQEKCAkQAEhAQoICVBASIACQgIUEBKggJAABYQEKCAkQAEhAQoICVBA\nSIACQgIUEBKggJAABYQEKPg/F2tf/PejhAkAAAAASUVORK5CYII="},"metadata":{"image/png":{"width":420,"height":420}}}],"source":["boxplot(choco$Cocoa.Percent,col=\"green\")"]},{"cell_type":"markdown","metadata":{"id":"2Y8TW7CuA6UY"},"source":["Boxplot by all groups of bean.origin"]},{"cell_type":"code","execution_count":33,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":437},"executionInfo":{"elapsed":480,"status":"ok","timestamp":1716799248195,"user":{"displayName":"toni Monleon","userId":"05269440626861479105"},"user_tz":-120},"id":"UUOAOaSwA6Mb","outputId":"02b8e434-2c52-47ee-8ea7-74f8aa6483ba"},"outputs":[{"output_type":"display_data","data":{"text/plain":["plot without title"],"image/png":"iVBORw0KGgoAAAANSUhEUgAAA0gAAANICAIAAAByhViMAAAACXBIWXMAABJ0AAASdAHeZh94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\nTkSuQmCC"},"metadata":{"image/png":{"width":420,"height":420}}}],"source":["# Basic boxplot\n","boxplot(choco$Cocoa.Percent ~ choco$Broad.Bean.Origin , col=terrain.colors(5) ) #it s very confused"]},{"cell_type":"markdown","metadata":{"id":"Fe4AlFESA6EQ"},"source":["Violin plot with selected Broad.Bean.Origin"]},{"cell_type":"code","execution_count":35,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":22561,"status":"ok","timestamp":1716799279529,"user":{"displayName":"toni Monleon","userId":"05269440626861479105"},"user_tz":-120},"id":"fDC6uB7TEQZB","outputId":"8083ea86-0330-4aa4-c76e-30679599f074"},"outputs":[{"output_type":"stream","name":"stderr","text":["Installing package into ‘/usr/local/lib/R/site-library’\n","(as ‘lib’ is unspecified)\n","\n","also installing the dependencies ‘sm’, ‘zoo’\n","\n","\n"]}],"source":["install.packages(\"vioplot\")"]},{"cell_type":"code","execution_count":36,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":1000},"executionInfo":{"elapsed":291,"status":"ok","timestamp":1716799283263,"user":{"displayName":"toni Monleon","userId":"05269440626861479105"},"user_tz":-120},"id":"s7dovxHqA5xa","outputId":"3f79c57a-8f92-497f-edd1-933c241df30e"},"outputs":[{"output_type":"stream","name":"stderr","text":["Loading required package: sm\n","\n","Package 'sm', version 2.2-6.0: type help(sm) for summary information\n","\n","Loading required package: zoo\n","\n","\n","Attaching package: ‘zoo’\n","\n","\n","The following objects are masked from ‘package:base’:\n","\n"," as.Date, as.Date.numeric\n","\n","\n"]},{"output_type":"display_data","data":{"text/plain":["\n","   \n"," 1 73 \n"," Africa, Carribean, C. Am. Australia \n"," 1 3 \n"," Belize Bolivia \n"," 49 57 \n"," Brazil Burma \n"," 58 1 \n"," Cameroon Carribean \n"," 1 8 \n"," Carribean(DR/Jam/Tri) Central and S. America \n"," 1 4 \n"," Colombia Colombia, Ecuador \n"," 40 3 \n"," Congo Cost Rica, Ven \n"," 10 1 \n"," Costa Rica Cuba \n"," 38 11 \n"," Dom. Rep., Madagascar Domincan Republic \n"," 1 25 \n"," Dominican Rep., Bali Dominican Republic \n"," 1 141 \n"," DR, Ecuador, Peru Ecuador \n"," 1 193 \n"," Ecuador, Costa Rica Ecuador, Mad., PNG \n"," 1 1 \n"," El Salvador Fiji \n"," 2 8 \n"," Gabon Ghana \n"," 1 33 \n"," Ghana & Madagascar Ghana, Domin. Rep \n"," 1 2 \n"," Ghana, Panama, Ecuador Gre., PNG, Haw., Haiti, Mad \n"," 1 1 \n"," Grenada Guat., D.R., Peru, Mad., PNG \n"," 19 1 \n"," Guatemala Haiti \n"," 28 9 \n"," Hawaii Honduras \n"," 28 15 \n"," India Indonesia \n"," 4 16 \n"," Indonesia, Ghana Ivory Coast \n"," 1 5 \n"," Jamaica Liberia \n"," 20 3 \n"," Mad., Java, PNG Madagascar \n"," 1 145 \n"," Madagascar & Ecuador Malaysia \n"," 1 3 \n"," Martinique Mexico \n"," 1 30 \n"," Nicaragua Nigeria \n"," 60 1 \n"," Panama Papua New Guinea \n"," 7 42 \n"," Peru Peru, Belize \n"," 165 1 \n"," Peru, Dom. Rep Peru, Ecuador \n"," 1 1 \n"," Peru, Ecuador, Venezuela Peru, Mad., Dom. Rep. \n"," 1 1 \n"," Peru, Madagascar Peru(SMartin,Pangoa,nacional) \n"," 1 1 \n"," Philippines PNG, Vanuatu, Mad \n"," 5 1 \n"," Principe Puerto Rico \n"," 1 4 \n"," Samoa Sao Tome \n"," 2 10 \n"," Sao Tome & Principe Solomon Islands \n"," 7 4 \n"," South America South America, Africa \n"," 3 1 \n"," Sri Lanka St. Lucia \n"," 2 8 \n"," Suriname Tanzania \n"," 1 34 \n"," Tobago Togo \n"," 2 3 \n"," Trinidad Trinidad-Tobago \n"," 33 1 \n"," Trinidad, Ecuador Trinidad, Tobago \n"," 1 2 \n"," Uganda Vanuatu \n"," 8 7 \n"," Ven, Bolivia, D.R. Ven, Trinidad, Ecuador \n"," 1 1 \n"," Ven., Indonesia, Ecuad. Ven., Trinidad, Mad. \n"," 1 1 \n"," Ven.,Ecu.,Peru,Nic. Venez,Africa,Brasil,Peru,Mex \n"," 1 1 \n"," Venezuela Venezuela, Carribean \n"," 214 1 \n"," Venezuela, Dom. Rep. Venezuela, Ghana \n"," 1 1 \n"," Venezuela, Java Venezuela, Trinidad \n"," 1 1 \n"," Venezuela/ Ghana Vietnam \n"," 1 38 \n"," West Africa \n"," 6 "]},"metadata":{}}],"source":["#violin plot\n","# Charge the vioplot library\n","library(vioplot)\n","\n","#see all choco$Broad.Bean.Origin\n","table(choco$Broad.Bean.Origin)\n","\n"]},{"cell_type":"code","execution_count":37,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":437},"executionInfo":{"elapsed":342,"status":"ok","timestamp":1716799287547,"user":{"displayName":"toni Monleon","userId":"05269440626861479105"},"user_tz":-120},"id":"-78BrjmJMTlY","outputId":"5171e3c7-2dff-412e-e3bd-b0ce93b6d980"},"outputs":[{"output_type":"display_data","data":{"text/plain":["Plot with title “”"],"image/png":"iVBORw0KGgoAAAANSUhEUgAAA0gAAANICAIAAAByhViMAAAACXBIWXMAABJ0AAASdAHeZh94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\ngh0AAAAASUVORK5CYII="},"metadata":{"image/png":{"width":420,"height":420}}}],"source":["# Draw the violin plot (= boxplot)\n","with(choco , vioplot( Cocoa.Percent[Broad.Bean.Origin==\"Brazil\"] , Cocoa.Percent[Broad.Bean.Origin==\"Australia\"], Cocoa.Percent[Broad.Bean.Origin==\"Ecuador\"], col=rgb(0.1,0.4,0.7,0.7) , names=c(\"A\",\"B\",\"C\") ))"]},{"cell_type":"code","execution_count":38,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":506},"executionInfo":{"elapsed":1276,"status":"ok","timestamp":1716799312621,"user":{"displayName":"toni Monleon","userId":"05269440626861479105"},"user_tz":-120},"id":"YD2lkFMrCeDw","outputId":"bce5e0ee-b6de-48aa-d0cc-4bc0238a2eda"},"outputs":[{"output_type":"stream","name":"stderr","text":["Warning message:\n","“\u001b[1m\u001b[22mRemoved 2 rows containing non-finite values (`stat_boxplot()`).”\n","Warning message:\n","“\u001b[1m\u001b[22mRemoved 2 rows containing missing values (`geom_point()`).”\n"]},{"output_type":"display_data","data":{"text/plain":["plot without title"],"image/png":"iVBORw0KGgoAAAANSUhEUgAAA0gAAANICAIAAAByhViMAAAACXBIWXMAABJ0AAASdAHeZh94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\nRK5CYII="},"metadata":{"image/png":{"width":420,"height":420}}}],"source":["#Boxplot with ggplot2\n","\n","# install.packages(\"ggplot2\")\n","library(ggplot2)\n","\n","#like before we are goint to subset df\n","#with(choco , vioplot( Cocoa.Percent[Broad.Bean.Origin==\"Brazil\"] , Cocoa.Percent[Broad.Bean.Origin==\"Australia\"], Cocoa.Percent[Broad.Bean.Origin==\"Ecuador\"], col=rgb(0.1,0.4,0.7,0.7) , names=c(\"A\",\"B\",\"C\") ))\n","\n","#select only three types\n","df <- subset(choco, Broad.Bean.Origin==\"Brazil\" | Broad.Bean.Origin==\"Australia\"| Broad.Bean.Origin==\"Ecuador\")\n","\n","# Box plot by group with jitter\n","ggplot(df, aes(x = Broad.Bean.Origin, y = Cocoa.Percent, fill=Broad.Bean.Origin)) +\n"," geom_boxplot(outlier.shape = NA) +\n"," geom_jitter(colour = \"black\")\n","\n","\n","\n"]},{"cell_type":"markdown","metadata":{"id":"TU5QoC4_A5h4"},"source":["### Robust descriptive measures\n","\n","See examples in "]},{"cell_type":"markdown","metadata":{"id":"gJJ1xdMxMs8-"},"source":["# Other: tables\n","\n","A statistical table is a method used to present statistical data by arranging the numbers systematically and describing some mass processes."]},{"cell_type":"code","execution_count":41,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":54259,"status":"ok","timestamp":1716799401768,"user":{"displayName":"toni Monleon","userId":"05269440626861479105"},"user_tz":-120},"id":"1edkoFT1NPDU","outputId":"377a9a19-9d3d-4293-8203-90862874d337"},"outputs":[{"output_type":"stream","name":"stderr","text":["Installing package into ‘/usr/local/lib/R/site-library’\n","(as ‘lib’ is unspecified)\n","\n","also installing the dependencies ‘checkmate’, ‘htmlTable’, ‘viridis’, ‘Formula’\n","\n","\n"]}],"source":["install.packages(\"Hmisc\") #if necessary"]},{"cell_type":"code","execution_count":42,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":1000},"executionInfo":{"elapsed":549,"status":"ok","timestamp":1716799414720,"user":{"displayName":"toni Monleon","userId":"05269440626861479105"},"user_tz":-120},"id":"kqcHbqdONj0L","outputId":"80db38a2-f288-4917-fcb3-65056900c1f3"},"outputs":[{"output_type":"stream","name":"stderr","text":["\n","Attaching package: ‘Hmisc’\n","\n","\n","The following objects are masked from ‘package:base’:\n","\n"," format.pval, units\n","\n","\n"]},{"output_type":"display_data","data":{"text/plain":["choco \n","\n"," 9 Variables 1795 Observations\n","--------------------------------------------------------------------------------\n","Company...Maker.if.known. \n"," n missing distinct \n"," 1795 0 416 \n","\n","lowest : A. Morin Acalli Adi Aequare (Gianduja) Ah Cacao \n","highest: Xocolla Zak's Zart Pralinen Zokoko Zotter \n","--------------------------------------------------------------------------------\n","Specific.Bean.Origin.or.Bar.Name \n"," n missing distinct \n"," 1795 0 1039 \n","\n","lowest : \"heirloom\", Arriba Nacional 100 percent 2009 Hapa Nibby A case of the Xerces Blues, triple roast Abinao \n","highest: Xoconusco, Chiapas Xocunusco, Chiapas, Pichucalco Zorzal Reserva Zorzal Reserva w/ Charles Kerchner Zorzal Reserva, 2015 H., Kerchner \n","--------------------------------------------------------------------------------\n","REF \n"," n missing distinct Info Mean Gmd .05 .10 \n"," 1795 0 440 1 1036 637.7 129 241 \n"," .25 .50 .75 .90 .95 \n"," 576 1069 1502 1772 1864 \n","\n","lowest : 5 15 24 32 40, highest: 1936 1940 1944 1948 1952\n","--------------------------------------------------------------------------------\n","Review.Date \n"," n missing distinct \n"," 1795 0 12 \n"," \n","Value 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016\n","Frequency 72 77 93 123 111 165 195 184 247 285 219\n","Proportion 0.040 0.043 0.052 0.069 0.062 0.092 0.109 0.103 0.138 0.159 0.122\n"," \n","Value 2017\n","Frequency 24\n","Proportion 0.013\n","--------------------------------------------------------------------------------\n","Cocoa.Percent \n"," n missing distinct Info Mean Gmd .05 .10 \n"," 1775 20 45 0.947 71.72 6.269 62.0 65.0 \n"," .25 .50 .75 .90 .95 \n"," 70.0 70.0 75.0 80.0 82.3 \n","\n","lowest : 42 46 50 53 55, highest: 89 90 91 99 100\n","--------------------------------------------------------------------------------\n","Company.Location \n"," n missing distinct \n"," 1795 0 60 \n","\n","lowest : Amsterdam Argentina Australia Austria Belgium \n","highest: U.K. U.S.A. Venezuela Vietnam Wales \n","--------------------------------------------------------------------------------\n","Rating \n"," n missing distinct Info Mean Gmd .05 .10 \n"," 1795 0 13 0.973 3.186 0.5246 2.500 2.500 \n"," .25 .50 .75 .90 .95 \n"," 2.875 3.250 3.500 3.750 4.000 \n"," \n","Value 1.00 1.50 1.75 2.00 2.25 2.50 2.75 3.00 3.25 3.50 3.75\n","Frequency 4 10 3 32 14 127 259 341 303 392 210\n","Proportion 0.002 0.006 0.002 0.018 0.008 0.071 0.144 0.190 0.169 0.218 0.117\n"," \n","Value 4.00 5.00\n","Frequency 98 2\n","Proportion 0.055 0.001\n","\n","For the frequency table, variable is rounded to the nearest 0\n","--------------------------------------------------------------------------------\n","Bean.Type \n"," n missing distinct \n"," 1794 1 41 \n","\n","lowest :   Amazon Amazon mix Amazon, ICS Beniano \n","highest: Trinitario (Scavina) Trinitario, Criollo Trinitario, Forastero Trinitario, Nacional Trinitario, TCGA \n","--------------------------------------------------------------------------------\n","Broad.Bean.Origin \n"," n missing distinct \n"," 1794 1 100 \n","\n","lowest :   Africa, Carribean, C. Am. Australia Belize Bolivia \n","highest: Venezuela, Java Venezuela, Trinidad Venezuela/ Ghana Vietnam West Africa \n","--------------------------------------------------------------------------------"]},"metadata":{}}],"source":["#Maybe a bit nicer and more informative for describe information in a data frame\n","\n","\n","library(Hmisc)\n","describe(choco) #can describe all variables"]},{"cell_type":"markdown","metadata":{"id":"tgTSCpqsYSj6"},"source":["More and best alternatives for frequency tables\n","\n","Function ctab (Package catspec) and Function stat.table (Epi)"]},{"cell_type":"code","execution_count":45,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":103577,"status":"ok","timestamp":1716799541389,"user":{"displayName":"toni Monleon","userId":"05269440626861479105"},"user_tz":-120},"id":"L6IjeoQGYZhO","outputId":"b556fb97-7a57-489c-dde4-c04636d23896"},"outputs":[{"output_type":"stream","name":"stderr","text":["Installing package into ‘/usr/local/lib/R/site-library’\n","(as ‘lib’ is unspecified)\n","\n","also installing the dependencies ‘RcppArmadillo’, ‘cmprsk’, ‘etm’, ‘numDeriv’\n","\n","\n"]}],"source":["install.packages(\"Epi\")"]},{"cell_type":"code","execution_count":46,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":530},"executionInfo":{"elapsed":1648,"status":"ok","timestamp":1716799559719,"user":{"displayName":"toni Monleon","userId":"05269440626861479105"},"user_tz":-120},"id":"zHF3tFH_Y6OJ","outputId":"fd4a2aa1-4b50-4c45-d2af-d73f9902f7aa"},"outputs":[{"output_type":"display_data","data":{"text/html":["\n","\n","\n","\t\n","\n","\n","\t\n","\n","
A stat.table: 1 × 42 of type dbl
 AmazonAmazon mixAmazon, ICSBenianoBlendBlend-Forastero,CriolloCCN51Criollo⋯NacionalNacional (Arriba)TrinitarioTrinitario (85% Criollo)Trinitario (Amelonado)Trinitario (Scavina)Trinitario, CriolloTrinitario, ForasteroTrinitario, NacionalTrinitario, TCGA
count()188712234111153⋯234192119211
\n"],"text/markdown":"\nA stat.table: 1 × 42 of type dbl\n\n| | |   | Amazon | Amazon mix | Amazon, ICS | Beniano | Blend | Blend-Forastero,Criollo | CCN51 | Criollo | ⋯ | Nacional | Nacional (Arriba) | Trinitario | Trinitario (85% Criollo) | Trinitario (Amelonado) | Trinitario (Scavina) | Trinitario, Criollo | Trinitario, Forastero | Trinitario, Nacional | Trinitario, TCGA |\n|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| count() | 1 | 887 | 1 | 2 | 2 | 3 | 41 | 1 | 1 | 153 | ⋯ | 2 | 3 | 419 | 2 | 1 | 1 | 9 | 2 | 1 | 1 |\n\n","text/latex":"A stat.table: 1 × 42 of type dbl\n\\begin{tabular}{r|lllllllllllllllllllll}\n & &   & Amazon & Amazon mix & Amazon, ICS & Beniano & Blend & Blend-Forastero,Criollo & CCN51 & Criollo & ⋯ & Nacional & Nacional (Arriba) & Trinitario & Trinitario (85\\% Criollo) & Trinitario (Amelonado) & Trinitario (Scavina) & Trinitario, Criollo & Trinitario, Forastero & Trinitario, Nacional & Trinitario, TCGA\\\\\n\\hline\n\tcount() & 1 & 887 & 1 & 2 & 2 & 3 & 41 & 1 & 1 & 153 & ⋯ & 2 & 3 & 419 & 2 & 1 & 1 & 9 & 2 & 1 & 1\\\\\n\\end{tabular}\n","text/plain":["   Amazon Amazon mix Amazon, ICS Beniano Blend\n","count() 1 887 1 2 2 3 41 \n"," Blend-Forastero,Criollo CCN51 Criollo ⋯ Nacional Nacional (Arriba)\n","count() 1 1 153 ⋯ 2 3 \n"," Trinitario Trinitario (85% Criollo) Trinitario (Amelonado)\n","count() 419 2 1 \n"," Trinitario (Scavina) Trinitario, Criollo Trinitario, Forastero\n","count() 1 9 2 \n"," Trinitario, Nacional Trinitario, TCGA\n","count() 1 1 "]},"metadata":{}},{"output_type":"stream","name":"stdout","text":["[1] \"------------------------------------------------------------------------------------------\"\n"]},{"output_type":"display_data","data":{"text/html":["\n","\n","\n","\t\n","\n","\n","\t\n","\t\n","\n","
A stat.table: 2 × 43 of type dbl
 AmazonAmazon mixAmazon, ICSBenianoBlendBlend-Forastero,CriolloCCN51Criollo⋯Nacional (Arriba)TrinitarioTrinitario (85% Criollo)Trinitario (Amelonado)Trinitario (Scavina)Trinitario, CriolloTrinitario, ForasteroTrinitario, NacionalTrinitario, TCGATotal
N1.00000000887.000001.000000002.00000002.00000003.000000041.0000001.000000001.00000000153.000000⋯3.0000000419.000002.00000001.000000001.000000009.00000002.00000001.000000001.000000001795
%0.05571031 49.415040.055710310.11142060.11142060.1671309 2.2841230.055710310.05571031 8.523677⋯0.1671309 23.342620.11142060.055710310.055710310.50139280.11142060.055710310.05571031 100
\n"],"text/markdown":"\nA stat.table: 2 × 43 of type dbl\n\n| | |   | Amazon | Amazon mix | Amazon, ICS | Beniano | Blend | Blend-Forastero,Criollo | CCN51 | Criollo | ⋯ | Nacional (Arriba) | Trinitario | Trinitario (85% Criollo) | Trinitario (Amelonado) | Trinitario (Scavina) | Trinitario, Criollo | Trinitario, Forastero | Trinitario, Nacional | Trinitario, TCGA | Total |\n|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| N | 1.00000000 | 887.00000 | 1.00000000 | 2.0000000 | 2.0000000 | 3.0000000 | 41.000000 | 1.00000000 | 1.00000000 | 153.000000 | ⋯ | 3.0000000 | 419.00000 | 2.0000000 | 1.00000000 | 1.00000000 | 9.0000000 | 2.0000000 | 1.00000000 | 1.00000000 | 1795 |\n| % | 0.05571031 | 49.41504 | 0.05571031 | 0.1114206 | 0.1114206 | 0.1671309 | 2.284123 | 0.05571031 | 0.05571031 | 8.523677 | ⋯ | 0.1671309 | 23.34262 | 0.1114206 | 0.05571031 | 0.05571031 | 0.5013928 | 0.1114206 | 0.05571031 | 0.05571031 | 100 |\n\n","text/latex":"A stat.table: 2 × 43 of type dbl\n\\begin{tabular}{r|lllllllllllllllllllll}\n & &   & Amazon & Amazon mix & Amazon, ICS & Beniano & Blend & Blend-Forastero,Criollo & CCN51 & Criollo & ⋯ & Nacional (Arriba) & Trinitario & Trinitario (85\\% Criollo) & Trinitario (Amelonado) & Trinitario (Scavina) & Trinitario, Criollo & Trinitario, Forastero & Trinitario, Nacional & Trinitario, TCGA & Total\\\\\n\\hline\n\tN & 1.00000000 & 887.00000 & 1.00000000 & 2.0000000 & 2.0000000 & 3.0000000 & 41.000000 & 1.00000000 & 1.00000000 & 153.000000 & ⋯ & 3.0000000 & 419.00000 & 2.0000000 & 1.00000000 & 1.00000000 & 9.0000000 & 2.0000000 & 1.00000000 & 1.00000000 & 1795\\\\\n\t\\% & 0.05571031 & 49.41504 & 0.05571031 & 0.1114206 & 0.1114206 & 0.1671309 & 2.284123 & 0.05571031 & 0.05571031 & 8.523677 & ⋯ & 0.1671309 & 23.34262 & 0.1114206 & 0.05571031 & 0.05571031 & 0.5013928 & 0.1114206 & 0.05571031 & 0.05571031 & 100\\\\\n\\end{tabular}\n","text/plain":["   Amazon Amazon mix Amazon, ICS Beniano Blend \n","N 1.00000000 887.00000 1.00000000 2.0000000 2.0000000 3.0000000 41.000000\n","% 0.05571031 49.41504 0.05571031 0.1114206 0.1114206 0.1671309 2.284123\n"," Blend-Forastero,Criollo CCN51 Criollo ⋯ Nacional (Arriba) Trinitario\n","N 1.00000000 1.00000000 153.000000 ⋯ 3.0000000 419.00000 \n","% 0.05571031 0.05571031 8.523677 ⋯ 0.1671309 23.34262 \n"," Trinitario (85% Criollo) Trinitario (Amelonado) Trinitario (Scavina)\n","N 2.0000000 1.00000000 1.00000000 \n","% 0.1114206 0.05571031 0.05571031 \n"," Trinitario, Criollo Trinitario, Forastero Trinitario, Nacional\n","N 9.0000000 2.0000000 1.00000000 \n","% 0.5013928 0.1114206 0.05571031 \n"," Trinitario, TCGA Total\n","N 1.00000000 1795 \n","% 0.05571031 100 "]},"metadata":{}},{"output_type":"stream","name":"stdout","text":["[1] \"------------------------------------------------------------------------------------------\"\n"]},{"output_type":"display_data","data":{"text/html":["\n","\n","\n","\t\n","\n","\n","\t\n","\t\n","\t\n","\n","
A stat.table: 3 × 43 of type dbl
 AmazonAmazon mixAmazon, ICSBenianoBlendBlend-Forastero,CriolloCCN51Criollo⋯Nacional (Arriba)TrinitarioTrinitario (85% Criollo)Trinitario (Amelonado)Trinitario (Scavina)Trinitario, CriolloTrinitario, ForasteroTrinitario, NacionalTrinitario, TCGATotal
N 1.00000000887.00000 1.00000000 2.0000000 2.0000000 3.000000041.000000 1.00000000 1.00000000153.000000⋯ 3.0000000419.00000 2.0000000 1.00000000 1.00000000 9.0000000 2.0000000 1.00000000 1.000000001795.00000
% 0.05571031 49.41504 0.05571031 0.1114206 0.1114206 0.1671309 2.284123 0.05571031 0.05571031 8.523677⋯ 0.1671309 23.34262 0.1114206 0.05571031 0.05571031 0.5013928 0.1114206 0.05571031 0.05571031 100.00000
Av. income70.00000000 71.5917470.0000000074.000000067.500000071.000000071.39024470.0000000065.00000000 72.067114⋯73.3333333 71.7452270.000000070.0000000070.0000000068.333333370.500000070.0000000072.00000000 71.72141
\n"],"text/markdown":"\nA stat.table: 3 × 43 of type dbl\n\n| | |   | Amazon | Amazon mix | Amazon, ICS | Beniano | Blend | Blend-Forastero,Criollo | CCN51 | Criollo | ⋯ | Nacional (Arriba) | Trinitario | Trinitario (85% Criollo) | Trinitario (Amelonado) | Trinitario (Scavina) | Trinitario, Criollo | Trinitario, Forastero | Trinitario, Nacional | Trinitario, TCGA | Total |\n|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| N | 1.00000000 | 887.00000 | 1.00000000 | 2.0000000 | 2.0000000 | 3.0000000 | 41.000000 | 1.00000000 | 1.00000000 | 153.000000 | ⋯ | 3.0000000 | 419.00000 | 2.0000000 | 1.00000000 | 1.00000000 | 9.0000000 | 2.0000000 | 1.00000000 | 1.00000000 | 1795.00000 |\n| % | 0.05571031 | 49.41504 | 0.05571031 | 0.1114206 | 0.1114206 | 0.1671309 | 2.284123 | 0.05571031 | 0.05571031 | 8.523677 | ⋯ | 0.1671309 | 23.34262 | 0.1114206 | 0.05571031 | 0.05571031 | 0.5013928 | 0.1114206 | 0.05571031 | 0.05571031 | 100.00000 |\n| Av. income | 70.00000000 | 71.59174 | 70.00000000 | 74.0000000 | 67.5000000 | 71.0000000 | 71.390244 | 70.00000000 | 65.00000000 | 72.067114 | ⋯ | 73.3333333 | 71.74522 | 70.0000000 | 70.00000000 | 70.00000000 | 68.3333333 | 70.5000000 | 70.00000000 | 72.00000000 | 71.72141 |\n\n","text/latex":"A stat.table: 3 × 43 of type dbl\n\\begin{tabular}{r|lllllllllllllllllllll}\n & &   & Amazon & Amazon mix & Amazon, ICS & Beniano & Blend & Blend-Forastero,Criollo & CCN51 & Criollo & ⋯ & Nacional (Arriba) & Trinitario & Trinitario (85\\% Criollo) & Trinitario (Amelonado) & Trinitario (Scavina) & Trinitario, Criollo & Trinitario, Forastero & Trinitario, Nacional & Trinitario, TCGA & Total\\\\\n\\hline\n\tN & 1.00000000 & 887.00000 & 1.00000000 & 2.0000000 & 2.0000000 & 3.0000000 & 41.000000 & 1.00000000 & 1.00000000 & 153.000000 & ⋯ & 3.0000000 & 419.00000 & 2.0000000 & 1.00000000 & 1.00000000 & 9.0000000 & 2.0000000 & 1.00000000 & 1.00000000 & 1795.00000\\\\\n\t\\% & 0.05571031 & 49.41504 & 0.05571031 & 0.1114206 & 0.1114206 & 0.1671309 & 2.284123 & 0.05571031 & 0.05571031 & 8.523677 & ⋯ & 0.1671309 & 23.34262 & 0.1114206 & 0.05571031 & 0.05571031 & 0.5013928 & 0.1114206 & 0.05571031 & 0.05571031 & 100.00000\\\\\n\tAv. income & 70.00000000 & 71.59174 & 70.00000000 & 74.0000000 & 67.5000000 & 71.0000000 & 71.390244 & 70.00000000 & 65.00000000 & 72.067114 & ⋯ & 73.3333333 & 71.74522 & 70.0000000 & 70.00000000 & 70.00000000 & 68.3333333 & 70.5000000 & 70.00000000 & 72.00000000 & 71.72141\\\\\n\\end{tabular}\n","text/plain":["   Amazon Amazon mix Amazon, ICS Beniano \n","N 1.00000000 887.00000 1.00000000 2.0000000 2.0000000 3.0000000\n","% 0.05571031 49.41504 0.05571031 0.1114206 0.1114206 0.1671309\n","Av. income 70.00000000 71.59174 70.00000000 74.0000000 67.5000000 71.0000000\n"," Blend Blend-Forastero,Criollo CCN51 Criollo ⋯\n","N 41.000000 1.00000000 1.00000000 153.000000 ⋯\n","% 2.284123 0.05571031 0.05571031 8.523677 ⋯\n","Av. income 71.390244 70.00000000 65.00000000 72.067114 ⋯\n"," Nacional (Arriba) Trinitario Trinitario (85% Criollo)\n","N 3.0000000 419.00000 2.0000000 \n","% 0.1671309 23.34262 0.1114206 \n","Av. income 73.3333333 71.74522 70.0000000 \n"," Trinitario (Amelonado) Trinitario (Scavina) Trinitario, Criollo\n","N 1.00000000 1.00000000 9.0000000 \n","% 0.05571031 0.05571031 0.5013928 \n","Av. income 70.00000000 70.00000000 68.3333333 \n"," Trinitario, Forastero Trinitario, Nacional Trinitario, TCGA\n","N 2.0000000 1.00000000 1.00000000 \n","% 0.1114206 0.05571031 0.05571031 \n","Av. income 70.5000000 70.00000000 72.00000000 \n"," Total \n","N 1795.00000\n","% 100.00000\n","Av. income 71.72141"]},"metadata":{}}],"source":["# install.packages(\"Epi\")\n","#Nice histogram tables\n","library(Epi)\n","stat.table(list(Bean.Type), data = choco)#table for Bean.type\n","\n","print(\"------------------------------------------------------------------------------------------\")\n","\n","stat.table(list(Region = Bean.Type), list(N = count(), \"%\" = percent(Bean.Type)),\n"," data = choco, margins = T)\n","\n","print(\"------------------------------------------------------------------------------------------\")\n","\n","stat.table(list(Region = Bean.Type), list(N = count(), \"%\" = percent(Bean.Type),\n"," \"Av. income\" = mean(Cocoa.Percent)),\n"," data = choco, margins = T)"]},{"cell_type":"markdown","metadata":{"id":"eHlMg7xOajNN"},"source":[]},{"cell_type":"code","execution_count":47,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":4339,"status":"ok","timestamp":1716799617395,"user":{"displayName":"toni Monleon","userId":"05269440626861479105"},"user_tz":-120},"id":"O_RiSP53Y5_6","outputId":"fd7315b3-9c89-49cc-f2c5-2ffec48f2021"},"outputs":[{"output_type":"stream","name":"stderr","text":["Installing package into ‘/usr/local/lib/R/site-library’\n","(as ‘lib’ is unspecified)\n","\n"]}],"source":["install.packages(\"descr\")"]},{"cell_type":"code","execution_count":48,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":1000},"executionInfo":{"elapsed":252,"status":"ok","timestamp":1716799620508,"user":{"displayName":"toni Monleon","userId":"05269440626861479105"},"user_tz":-120},"id":"sqQWdVHIaoUt","outputId":"eade4e52-87ff-4590-e5cb-96abdb5cec0d"},"outputs":[{"output_type":"display_data","data":{"text/html":["\n","\n","\n","\t\n","\n","\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\n","
A freqtable: 43 × 2 of type dbl
FrequencyPercent
1 0.05571031
  887 49.41504178
Amazon 1 0.05571031
Amazon mix 2 0.11142061
Amazon, ICS 2 0.11142061
Beniano 3 0.16713092
Blend 41 2.28412256
Blend-Forastero,Criollo 1 0.05571031
CCN51 1 0.05571031
Criollo 153 8.52367688
Criollo (Amarru) 2 0.11142061
Criollo (Ocumare 61) 2 0.11142061
Criollo (Ocumare 67) 1 0.05571031
Criollo (Ocumare 77) 1 0.05571031
Criollo (Ocumare) 1 0.05571031
Criollo (Porcelana) 10 0.55710306
Criollo (Wild) 1 0.05571031
Criollo, + 1 0.05571031
Criollo, Forastero 2 0.11142061
Criollo, Trinitario 39 2.17270195
EET 3 0.16713092
Forastero 87 4.84679666
Forastero (Amelonado) 1 0.05571031
Forastero (Arriba) 37 2.06128134
Forastero (Arriba) ASS 6 0.33426184
Forastero (Arriba) ASSS 1 0.05571031
Forastero (Catongo) 2 0.11142061
Forastero (Nacional) 52 2.89693593
Forastero (Parazinho) 8 0.44568245
Forastero, Trinitario 1 0.05571031
Forastero(Arriba, CCN) 1 0.05571031
Matina 3 0.16713092
Nacional 2 0.11142061
Nacional (Arriba) 3 0.16713092
Trinitario 419 23.34261838
Trinitario (85% Criollo) 2 0.11142061
Trinitario (Amelonado) 1 0.05571031
Trinitario (Scavina) 1 0.05571031
Trinitario, Criollo 9 0.50139276
Trinitario, Forastero 2 0.11142061
Trinitario, Nacional 1 0.05571031
Trinitario, TCGA 1 0.05571031
Total1795100.00000000
\n"],"text/markdown":"\nA freqtable: 43 × 2 of type dbl\n\n| | Frequency | Percent |\n|---|---|---|\n| | 1 | 0.05571031 |\n|   | 887 | 49.41504178 |\n| Amazon | 1 | 0.05571031 |\n| Amazon mix | 2 | 0.11142061 |\n| Amazon, ICS | 2 | 0.11142061 |\n| Beniano | 3 | 0.16713092 |\n| Blend | 41 | 2.28412256 |\n| Blend-Forastero,Criollo | 1 | 0.05571031 |\n| CCN51 | 1 | 0.05571031 |\n| Criollo | 153 | 8.52367688 |\n| Criollo (Amarru) | 2 | 0.11142061 |\n| Criollo (Ocumare 61) | 2 | 0.11142061 |\n| Criollo (Ocumare 67) | 1 | 0.05571031 |\n| Criollo (Ocumare 77) | 1 | 0.05571031 |\n| Criollo (Ocumare) | 1 | 0.05571031 |\n| Criollo (Porcelana) | 10 | 0.55710306 |\n| Criollo (Wild) | 1 | 0.05571031 |\n| Criollo, + | 1 | 0.05571031 |\n| Criollo, Forastero | 2 | 0.11142061 |\n| Criollo, Trinitario | 39 | 2.17270195 |\n| EET | 3 | 0.16713092 |\n| Forastero | 87 | 4.84679666 |\n| Forastero (Amelonado) | 1 | 0.05571031 |\n| Forastero (Arriba) | 37 | 2.06128134 |\n| Forastero (Arriba) ASS | 6 | 0.33426184 |\n| Forastero (Arriba) ASSS | 1 | 0.05571031 |\n| Forastero (Catongo) | 2 | 0.11142061 |\n| Forastero (Nacional) | 52 | 2.89693593 |\n| Forastero (Parazinho) | 8 | 0.44568245 |\n| Forastero, Trinitario | 1 | 0.05571031 |\n| Forastero(Arriba, CCN) | 1 | 0.05571031 |\n| Matina | 3 | 0.16713092 |\n| Nacional | 2 | 0.11142061 |\n| Nacional (Arriba) | 3 | 0.16713092 |\n| Trinitario | 419 | 23.34261838 |\n| Trinitario (85% Criollo) | 2 | 0.11142061 |\n| Trinitario (Amelonado) | 1 | 0.05571031 |\n| Trinitario (Scavina) | 1 | 0.05571031 |\n| Trinitario, Criollo | 9 | 0.50139276 |\n| Trinitario, Forastero | 2 | 0.11142061 |\n| Trinitario, Nacional | 1 | 0.05571031 |\n| Trinitario, TCGA | 1 | 0.05571031 |\n| Total | 1795 | 100.00000000 |\n\n","text/latex":"A freqtable: 43 × 2 of type dbl\n\\begin{tabular}{r|ll}\n & Frequency & Percent\\\\\n\\hline\n\t & 1 & 0.05571031\\\\\n\t  & 887 & 49.41504178\\\\\n\tAmazon & 1 & 0.05571031\\\\\n\tAmazon mix & 2 & 0.11142061\\\\\n\tAmazon, ICS & 2 & 0.11142061\\\\\n\tBeniano & 3 & 0.16713092\\\\\n\tBlend & 41 & 2.28412256\\\\\n\tBlend-Forastero,Criollo & 1 & 0.05571031\\\\\n\tCCN51 & 1 & 0.05571031\\\\\n\tCriollo & 153 & 8.52367688\\\\\n\tCriollo (Amarru) & 2 & 0.11142061\\\\\n\tCriollo (Ocumare 61) & 2 & 0.11142061\\\\\n\tCriollo (Ocumare 67) & 1 & 0.05571031\\\\\n\tCriollo (Ocumare 77) & 1 & 0.05571031\\\\\n\tCriollo (Ocumare) & 1 & 0.05571031\\\\\n\tCriollo (Porcelana) & 10 & 0.55710306\\\\\n\tCriollo (Wild) & 1 & 0.05571031\\\\\n\tCriollo, + & 1 & 0.05571031\\\\\n\tCriollo, Forastero & 2 & 0.11142061\\\\\n\tCriollo, Trinitario & 39 & 2.17270195\\\\\n\tEET & 3 & 0.16713092\\\\\n\tForastero & 87 & 4.84679666\\\\\n\tForastero (Amelonado) & 1 & 0.05571031\\\\\n\tForastero (Arriba) & 37 & 2.06128134\\\\\n\tForastero (Arriba) ASS & 6 & 0.33426184\\\\\n\tForastero (Arriba) ASSS & 1 & 0.05571031\\\\\n\tForastero (Catongo) & 2 & 0.11142061\\\\\n\tForastero (Nacional) & 52 & 2.89693593\\\\\n\tForastero (Parazinho) & 8 & 0.44568245\\\\\n\tForastero, Trinitario & 1 & 0.05571031\\\\\n\tForastero(Arriba, CCN) & 1 & 0.05571031\\\\\n\tMatina & 3 & 0.16713092\\\\\n\tNacional & 2 & 0.11142061\\\\\n\tNacional (Arriba) & 3 & 0.16713092\\\\\n\tTrinitario & 419 & 23.34261838\\\\\n\tTrinitario (85\\% Criollo) & 2 & 0.11142061\\\\\n\tTrinitario (Amelonado) & 1 & 0.05571031\\\\\n\tTrinitario (Scavina) & 1 & 0.05571031\\\\\n\tTrinitario, Criollo & 9 & 0.50139276\\\\\n\tTrinitario, Forastero & 2 & 0.11142061\\\\\n\tTrinitario, Nacional & 1 & 0.05571031\\\\\n\tTrinitario, TCGA & 1 & 0.05571031\\\\\n\tTotal & 1795 & 100.00000000\\\\\n\\end{tabular}\n","text/plain":[" Frequency Percent \n"," 1 0.05571031\n","  887 49.41504178\n","Amazon 1 0.05571031\n","Amazon mix 2 0.11142061\n","Amazon, ICS 2 0.11142061\n","Beniano 3 0.16713092\n","Blend 41 2.28412256\n","Blend-Forastero,Criollo 1 0.05571031\n","CCN51 1 0.05571031\n","Criollo 153 8.52367688\n","Criollo (Amarru) 2 0.11142061\n","Criollo (Ocumare 61) 2 0.11142061\n","Criollo (Ocumare 67) 1 0.05571031\n","Criollo (Ocumare 77) 1 0.05571031\n","Criollo (Ocumare) 1 0.05571031\n","Criollo (Porcelana) 10 0.55710306\n","Criollo (Wild) 1 0.05571031\n","Criollo, + 1 0.05571031\n","Criollo, Forastero 2 0.11142061\n","Criollo, Trinitario 39 2.17270195\n","EET 3 0.16713092\n","Forastero 87 4.84679666\n","Forastero (Amelonado) 1 0.05571031\n","Forastero (Arriba) 37 2.06128134\n","Forastero (Arriba) ASS 6 0.33426184\n","Forastero (Arriba) ASSS 1 0.05571031\n","Forastero (Catongo) 2 0.11142061\n","Forastero (Nacional) 52 2.89693593\n","Forastero (Parazinho) 8 0.44568245\n","Forastero, Trinitario 1 0.05571031\n","Forastero(Arriba, CCN) 1 0.05571031\n","Matina 3 0.16713092\n","Nacional 2 0.11142061\n","Nacional (Arriba) 3 0.16713092\n","Trinitario 419 23.34261838\n","Trinitario (85% Criollo) 2 0.11142061\n","Trinitario (Amelonado) 1 0.05571031\n","Trinitario (Scavina) 1 0.05571031\n","Trinitario, Criollo 9 0.50139276\n","Trinitario, Forastero 2 0.11142061\n","Trinitario, Nacional 1 0.05571031\n","Trinitario, TCGA 1 0.05571031\n","Total 1795 100.00000000"]},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["plot without title"],"image/png":"iVBORw0KGgoAAAANSUhEUgAAA0gAAANICAMAAADKOT/pAAAC/VBMVEUAAAABAQECAgIDAwME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\n5RjpYGxiyAAAAABJRU5ErkJggg=="},"metadata":{"image/png":{"width":420,"height":420}}}],"source":["library(descr) #vertical hystogram tables\n","#freq(choco$Bean.Type)\n","freq(choco$Bean.Type, plot = T)"]},{"cell_type":"markdown","source":["A professional method for tables:"],"metadata":{"id":"T-0JkJLAe6T2"}},{"cell_type":"code","source":[" #https://cran.r-project.org/web/packages/table1/vignettes/table1-examples.html\n"," #table1(~ factor(sex) + age + factor(ulcer) + thickness | status, data=melanoma2)\n","\n"," require(devtools)\n"," devtools::install_github(\"benjaminrich/table1\")\n","\n"," library(table1)\n","\n","#render for continous variables\n","my.render.cont <- function(x) {\n"," with(stats.apply.rounding(stats.default(x), digits=2), c(\"\",\n"," \"Mean (SD)\"=sprintf(\"%s (± %s)\", MEAN, SD)))\n","}\n","#render for discrete variables\n","my.render.cat <- function(x) {\n"," c(\"\", sapply(stats.default(x), function(y) with(y,\n"," sprintf(\"%d (%0.0f %%)\", FREQ, PCT))))\n","}"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"Zpkx-8UTe-Hm","executionInfo":{"status":"ok","timestamp":1716799676533,"user_tz":-120,"elapsed":43336,"user":{"displayName":"toni Monleon","userId":"05269440626861479105"}},"outputId":"daee024a-ad0a-4b4a-b1b1-92fe19c602db"},"execution_count":49,"outputs":[{"output_type":"stream","name":"stderr","text":["Downloading GitHub repo benjaminrich/table1@HEAD\n","\n"]},{"output_type":"stream","name":"stdout","text":["xfun (0.41 -> 0.44 ) [CRAN]\n","fastmap (1.1.1 -> 1.2.0 ) [CRAN]\n","digest (0.6.34 -> 0.6.35 ) [CRAN]\n","highr (0.9 -> 0.11 ) [CRAN]\n","htmltools (0.5.7 -> 0.5.8.1) [CRAN]\n","knitr (1.45 -> 1.46 ) [CRAN]\n"]},{"output_type":"stream","name":"stderr","text":["Installing 6 packages: xfun, fastmap, digest, highr, htmltools, knitr\n","\n","Installing packages into ‘/usr/local/lib/R/site-library’\n","(as ‘lib’ is unspecified)\n","\n"]},{"output_type":"stream","name":"stdout","text":["\u001b[36m──\u001b[39m \u001b[36mR CMD build\u001b[39m \u001b[36m─────────────────────────────────────────────────────────────────\u001b[39m\n","* checking for file ‘/tmp/Rtmpq2a8hK/remotes1c342e45644d/benjaminrich-table1-b6660c5/DESCRIPTION’ ... OK\n","* preparing ‘table1’:\n","* checking DESCRIPTION meta-information ... OK\n","* checking for LF line-endings in source and make files and shell scripts\n","* checking for empty or unneeded directories\n","* building ‘table1_1.4.4.tar.gz’\n","\n"]},{"output_type":"stream","name":"stderr","text":["Installing package into ‘/usr/local/lib/R/site-library’\n","(as ‘lib’ is unspecified)\n","\n","\n","Attaching package: ‘table1’\n","\n","\n","The following objects are masked from ‘package:Hmisc’:\n","\n"," label, label<-, units\n","\n","\n","The following objects are masked from ‘package:base’:\n","\n"," units, units<-\n","\n","\n"]}]},{"cell_type":"code","source":["#example for choco (ONLY IN RSTUDIO)\n","\n","table1(~ Cocoa.Percent | Bean.Type, data=choco,render.continuous=my.render.cont, render.categorical=my.render.cat)\n","\n","table1(~ Cocoa.Percent + choco$Rating| Bean.Type, data=choco,render.continuous=my.render.cont, render.categorical=my.render.cat)\n","\n"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":1000},"id":"ssuzaaecfKxF","executionInfo":{"status":"ok","timestamp":1716799681363,"user_tz":-120,"elapsed":1094,"user":{"displayName":"toni Monleon","userId":"05269440626861479105"}},"outputId":"b10e815c-3fc2-4198-9b80-6a23c7ec275b"},"execution_count":50,"outputs":[{"output_type":"stream","name":"stderr","text":["Warning message in .table1.internal(x = x, labels = labels, groupspan = groupspan, :\n","“Table has 43 columns. Are you sure this is what you want?”\n"]},{"output_type":"display_data","data":{"text/html":["'<table class=\"Rtable1\">\\n<thead>\\n<tr>\\n<th class=\\'rowlabel firstrow lastrow\\'></th>\\n<th class=\\'firstrow lastrow\\'><span class=\\'stratlabel\\'>NA<br><span class=\\'stratn\\'>(N=1)</span></span></th>\\n<th class=\\'firstrow lastrow\\'><span class=\\'stratlabel\\'> <br><span class=\\'stratn\\'>(N=887)</span></span></th>\\n<th class=\\'firstrow lastrow\\'><span class=\\'stratlabel\\'>Amazon<br><span class=\\'stratn\\'>(N=1)</span></span></th>\\n<th class=\\'firstrow lastrow\\'><span class=\\'stratlabel\\'>Amazon mix<br><span class=\\'stratn\\'>(N=2)</span></span></th>\\n<th class=\\'firstrow lastrow\\'><span class=\\'stratlabel\\'>Amazon, ICS<br><span class=\\'stratn\\'>(N=2)</span></span></th>\\n<th class=\\'firstrow lastrow\\'><span class=\\'stratlabel\\'>Beniano<br><span class=\\'stratn\\'>(N=3)</span></span></th>\\n<th class=\\'firstrow lastrow\\'><span class=\\'stratlabel\\'>Blend<br><span class=\\'stratn\\'>(N=41)</span></span></th>\\n<th class=\\'firstrow lastrow\\'><span class=\\'stratlabel\\'>Blend-Forastero,Criollo<br><span class=\\'stratn\\'>(N=1)</span></span></th>\\n<th class=\\'firstrow lastrow\\'><span class=\\'stratlabel\\'>CCN51<br><span class=\\'stratn\\'>(N=1)</span></span></th>\\n<th class=\\'firstrow lastrow\\'><span class=\\'stratlabel\\'>Criollo<br><span class=\\'stratn\\'>(N=153)</span></span></th>\\n<th class=\\'firstrow lastrow\\'><span class=\\'stratlabel\\'>Criollo (Amarru)<br><span class=\\'stratn\\'>(N=2)</span></span></th>\\n<th class=\\'firstrow lastrow\\'><span class=\\'stratlabel\\'>Criollo (Ocumare 61)<br><span class=\\'stratn\\'>(N=2)</span></span></th>\\n<th class=\\'firstrow lastrow\\'><span class=\\'stratlabel\\'>Criollo (Ocumare 67)<br><span class=\\'stratn\\'>(N=1)</span></span></th>\\n<th class=\\'firstrow lastrow\\'><span class=\\'stratlabel\\'>Criollo (Ocumare 77)<br><span class=\\'stratn\\'>(N=1)</span></span></th>\\n<th class=\\'firstrow lastrow\\'><span class=\\'stratlabel\\'>Criollo (Ocumare)<br><span class=\\'stratn\\'>(N=1)</span></span></th>\\n<th class=\\'firstrow lastrow\\'><span class=\\'stratlabel\\'>Criollo (Porcelana)<br><span class=\\'stratn\\'>(N=10)</span></span></th>\\n<th class=\\'firstrow lastrow\\'><span class=\\'stratlabel\\'>Criollo (Wild)<br><span class=\\'stratn\\'>(N=1)</span></span></th>\\n<th class=\\'firstrow lastrow\\'><span class=\\'stratlabel\\'>Criollo, +<br><span class=\\'stratn\\'>(N=1)</span></span></th>\\n<th class=\\'firstrow lastrow\\'><span class=\\'stratlabel\\'>Criollo, Forastero<br><span class=\\'stratn\\'>(N=2)</span></span></th>\\n<th class=\\'firstrow lastrow\\'><span class=\\'stratlabel\\'>Criollo, Trinitario<br><span class=\\'stratn\\'>(N=39)</span></span></th>\\n<th class=\\'firstrow lastrow\\'><span class=\\'stratlabel\\'>EET<br><span class=\\'stratn\\'>(N=3)</span></span></th>\\n<th class=\\'firstrow lastrow\\'><span class=\\'stratlabel\\'>Forastero<br><span class=\\'stratn\\'>(N=87)</span></span></th>\\n<th class=\\'firstrow lastrow\\'><span class=\\'stratlabel\\'>Forastero (Amelonado)<br><span class=\\'stratn\\'>(N=1)</span></span></th>\\n<th class=\\'firstrow lastrow\\'><span class=\\'stratlabel\\'>Forastero (Arriba)<br><span class=\\'stratn\\'>(N=37)</span></span></th>\\n<th class=\\'firstrow lastrow\\'><span class=\\'stratlabel\\'>Forastero (Arriba) ASS<br><span class=\\'stratn\\'>(N=6)</span></span></th>\\n<th class=\\'firstrow lastrow\\'><span class=\\'stratlabel\\'>Forastero (Arriba) ASSS<br><span class=\\'stratn\\'>(N=1)</span></span></th>\\n<th class=\\'firstrow lastrow\\'><span class=\\'stratlabel\\'>Forastero (Catongo)<br><span class=\\'stratn\\'>(N=2)</span></span></th>\\n<th class=\\'firstrow lastrow\\'><span class=\\'stratlabel\\'>Forastero (Nacional)<br><span class=\\'stratn\\'>(N=52)</span></span></th>\\n<th class=\\'firstrow lastrow\\'><span class=\\'stratlabel\\'>Forastero (Parazinho)<br><span class=\\'stratn\\'>(N=8)</span></span></th>\\n<th class=\\'firstrow lastrow\\'><span class=\\'stratlabel\\'>Forastero, Trinitario<br><span class=\\'stratn\\'>(N=1)</span></span></th>\\n<th class=\\'firstrow lastrow\\'><span class=\\'stratlabel\\'>Forastero(Arriba, CCN)<br><span class=\\'stratn\\'>(N=1)</span></span></th>\\n<th class=\\'firstrow lastrow\\'><span class=\\'stratlabel\\'>Matina<br><span class=\\'stratn\\'>(N=3)</span></span></th>\\n<th class=\\'firstrow lastrow\\'><span class=\\'stratlabel\\'>Nacional<br><span class=\\'stratn\\'>(N=2)</span></span></th>\\n<th class=\\'firstrow lastrow\\'><span class=\\'stratlabel\\'>Nacional (Arriba)<br><span class=\\'stratn\\'>(N=3)</span></span></th>\\n<th class=\\'firstrow lastrow\\'><span class=\\'stratlabel\\'>Trinitario<br><span class=\\'stratn\\'>(N=419)</span></span></th>\\n<th class=\\'firstrow lastrow\\'><span class=\\'stratlabel\\'>Trinitario (85% Criollo)<br><span class=\\'stratn\\'>(N=2)</span></span></th>\\n<th class=\\'firstrow lastrow\\'><span class=\\'stratlabel\\'>Trinitario (Amelonado)<br><span class=\\'stratn\\'>(N=1)</span></span></th>\\n<th class=\\'firstrow lastrow\\'><span class=\\'stratlabel\\'>Trinitario (Scavina)<br><span class=\\'stratn\\'>(N=1)</span></span></th>\\n<th class=\\'firstrow lastrow\\'><span class=\\'stratlabel\\'>Trinitario, Criollo<br><span class=\\'stratn\\'>(N=9)</span></span></th>\\n<th class=\\'firstrow lastrow\\'><span class=\\'stratlabel\\'>Trinitario, Forastero<br><span class=\\'stratn\\'>(N=2)</span></span></th>\\n<th class=\\'firstrow lastrow\\'><span class=\\'stratlabel\\'>Trinitario, Nacional<br><span class=\\'stratn\\'>(N=1)</span></span></th>\\n<th class=\\'firstrow lastrow\\'><span class=\\'stratlabel\\'>Trinitario, TCGA<br><span class=\\'stratn\\'>(N=1)</span></span></th>\\n<th class=\\'firstrow lastrow\\'><span class=\\'stratlabel\\'>Overall<br><span class=\\'stratn\\'>(N=1795)</span></span></th>\\n</tr>\\n</thead>\\n<tbody>\\n<tr>\\n<td class=\\'rowlabel firstrow\\'>Cocoa.Percent</td>\\n<td class=\\'firstrow\\'></td>\\n<td class=\\'firstrow\\'></td>\\n<td class=\\'firstrow\\'></td>\\n<td class=\\'firstrow\\'></td>\\n<td class=\\'firstrow\\'></td>\\n<td class=\\'firstrow\\'></td>\\n<td class=\\'firstrow\\'></td>\\n<td class=\\'firstrow\\'></td>\\n<td class=\\'firstrow\\'></td>\\n<td class=\\'firstrow\\'></td>\\n<td class=\\'firstrow\\'></td>\\n<td class=\\'firstrow\\'></td>\\n<td class=\\'firstrow\\'></td>\\n<td class=\\'firstrow\\'></td>\\n<td class=\\'firstrow\\'></td>\\n<td class=\\'firstrow\\'></td>\\n<td class=\\'firstrow\\'></td>\\n<td class=\\'firstrow\\'></td>\\n<td class=\\'firstrow\\'></td>\\n<td class=\\'firstrow\\'></td>\\n<td class=\\'firstrow\\'></td>\\n<td class=\\'firstrow\\'></td>\\n<td class=\\'firstrow\\'></td>\\n<td class=\\'firstrow\\'></td>\\n<td class=\\'firstrow\\'></td>\\n<td class=\\'firstrow\\'></td>\\n<td class=\\'firstrow\\'></td>\\n<td class=\\'firstrow\\'></td>\\n<td class=\\'firstrow\\'></td>\\n<td class=\\'firstrow\\'></td>\\n<td class=\\'firstrow\\'></td>\\n<td class=\\'firstrow\\'></td>\\n<td class=\\'firstrow\\'></td>\\n<td class=\\'firstrow\\'></td>\\n<td class=\\'firstrow\\'></td>\\n<td class=\\'firstrow\\'></td>\\n<td class=\\'firstrow\\'></td>\\n<td class=\\'firstrow\\'></td>\\n<td class=\\'firstrow\\'></td>\\n<td class=\\'firstrow\\'></td>\\n<td class=\\'firstrow\\'></td>\\n<td class=\\'firstrow\\'></td>\\n<td class=\\'firstrow\\'></td>\\n</tr>\\n<tr>\\n<td class=\\'rowlabel\\'>Mean (SD)</td>\\n<td>70 (&plusmn; NA)</td>\\n<td>72 (&plusmn; 6.6)</td>\\n<td>70 (&plusmn; NA)</td>\\n<td>74 (&plusmn; 0)</td>\\n<td>68 (&plusmn; 0.71)</td>\\n<td>71 (&plusmn; 3.6)</td>\\n<td>71 (&plusmn; 6.5)</td>\\n<td>70 (&plusmn; NA)</td>\\n<td>65 (&plusmn; NA)</td>\\n<td>72 (&plusmn; 5.8)</td>\\n<td>70 (&plusmn; 0)</td>\\n<td>74 (&plusmn; 2.1)</td>\\n<td>70 (&plusmn; NA)</td>\\n<td>70 (&plusmn; NA)</td>\\n<td>80 (&plusmn; NA)</td>\\n<td>70 (&plusmn; 4.0)</td>\\n<td>68 (&plusmn; NA)</td>\\n<td>65 (&plusmn; NA)</td>\\n<td>77 (&plusmn; 2.1)</td>\\n<td>72 (&plusmn; 4.6)</td>\\n<td>71 (&plusmn; 5.1)</td>\\n<td>72 (&plusmn; 6.6)</td>\\n<td>70 (&plusmn; NA)</td>\\n<td>73 (&plusmn; 12)</td>\\n<td>69 (&plusmn; 10)</td>\\n<td>70 (&plusmn; NA)</td>\\n<td>73 (&plusmn; 3.5)</td>\\n<td>73 (&plusmn; 7.5)</td>\\n<td>69 (&plusmn; 10)</td>\\n<td>70 (&plusmn; NA)</td>\\n<td>75 (&plusmn; NA)</td>\\n<td>74 (&plusmn; 6.9)</td>\\n<td>71 (&plusmn; 1.4)</td>\\n<td>73 (&plusmn; 2.9)</td>\\n<td>72 (&plusmn; 5.3)</td>\\n<td>70 (&plusmn; 0)</td>\\n<td>70 (&plusmn; NA)</td>\\n<td>70 (&plusmn; NA)</td>\\n<td>68 (&plusmn; 3.2)</td>\\n<td>71 (&plusmn; 6.4)</td>\\n<td>70 (&plusmn; NA)</td>\\n<td>72 (&plusmn; NA)</td>\\n<td>72 (&plusmn; 6.4)</td>\\n</tr>\\n<tr>\\n<td class=\\'rowlabel lastrow\\'>Missing</td>\\n<td class=\\'lastrow\\'>0 (0%)</td>\\n<td class=\\'lastrow\\'>15 (1.7%)</td>\\n<td class=\\'lastrow\\'>0 (0%)</td>\\n<td class=\\'lastrow\\'>0 (0%)</td>\\n<td class=\\'lastrow\\'>0 (0%)</td>\\n<td class=\\'lastrow\\'>0 (0%)</td>\\n<td class=\\'lastrow\\'>0 (0%)</td>\\n<td class=\\'lastrow\\'>0 (0%)</td>\\n<td class=\\'lastrow\\'>0 (0%)</td>\\n<td class=\\'lastrow\\'>4 (2.6%)</td>\\n<td class=\\'lastrow\\'>0 (0%)</td>\\n<td class=\\'lastrow\\'>0 (0%)</td>\\n<td class=\\'lastrow\\'>0 (0%)</td>\\n<td class=\\'lastrow\\'>0 (0%)</td>\\n<td class=\\'lastrow\\'>0 (0%)</td>\\n<td class=\\'lastrow\\'>0 (0%)</td>\\n<td class=\\'lastrow\\'>0 (0%)</td>\\n<td class=\\'lastrow\\'>0 (0%)</td>\\n<td class=\\'lastrow\\'>0 (0%)</td>\\n<td class=\\'lastrow\\'>0 (0%)</td>\\n<td class=\\'lastrow\\'>0 (0%)</td>\\n<td class=\\'lastrow\\'>0 (0%)</td>\\n<td class=\\'lastrow\\'>0 (0%)</td>\\n<td class=\\'lastrow\\'>0 (0%)</td>\\n<td class=\\'lastrow\\'>0 (0%)</td>\\n<td class=\\'lastrow\\'>0 (0%)</td>\\n<td class=\\'lastrow\\'>0 (0%)</td>\\n<td class=\\'lastrow\\'>0 (0%)</td>\\n<td class=\\'lastrow\\'>0 (0%)</td>\\n<td class=\\'lastrow\\'>0 (0%)</td>\\n<td class=\\'lastrow\\'>0 (0%)</td>\\n<td class=\\'lastrow\\'>0 (0%)</td>\\n<td class=\\'lastrow\\'>0 (0%)</td>\\n<td class=\\'lastrow\\'>0 (0%)</td>\\n<td class=\\'lastrow\\'>1 (0.2%)</td>\\n<td class=\\'lastrow\\'>0 (0%)</td>\\n<td class=\\'lastrow\\'>0 (0%)</td>\\n<td class=\\'lastrow\\'>0 (0%)</td>\\n<td class=\\'lastrow\\'>0 (0%)</td>\\n<td class=\\'lastrow\\'>0 (0%)</td>\\n<td class=\\'lastrow\\'>0 (0%)</td>\\n<td class=\\'lastrow\\'>0 (0%)</td>\\n<td class=\\'lastrow\\'>20 (1.1%)</td>\\n</tr>\\n</tbody>\\n</table>\\n'"],"text/markdown":"'<table class=\"Rtable1\">\\n<thead>\\n<tr>\\n<th class=\\'rowlabel firstrow lastrow\\'></th>\\n<th class=\\'firstrow lastrow\\'><span class=\\'stratlabel\\'>NA<br><span class=\\'stratn\\'>(N=1)</span></span></th>\\n<th class=\\'firstrow lastrow\\'><span class=\\'stratlabel\\'> <br><span class=\\'stratn\\'>(N=887)</span></span></th>\\n<th class=\\'firstrow lastrow\\'><span class=\\'stratlabel\\'>Amazon<br><span class=\\'stratn\\'>(N=1)</span></span></th>\\n<th class=\\'firstrow lastrow\\'><span class=\\'stratlabel\\'>Amazon mix<br><span class=\\'stratn\\'>(N=2)</span></span></th>\\n<th class=\\'firstrow lastrow\\'><span class=\\'stratlabel\\'>Amazon, ICS<br><span class=\\'stratn\\'>(N=2)</span></span></th>\\n<th class=\\'firstrow lastrow\\'><span class=\\'stratlabel\\'>Beniano<br><span class=\\'stratn\\'>(N=3)</span></span></th>\\n<th class=\\'firstrow lastrow\\'><span class=\\'stratlabel\\'>Blend<br><span class=\\'stratn\\'>(N=41)</span></span></th>\\n<th class=\\'firstrow lastrow\\'><span class=\\'stratlabel\\'>Blend-Forastero,Criollo<br><span class=\\'stratn\\'>(N=1)</span></span></th>\\n<th class=\\'firstrow lastrow\\'><span class=\\'stratlabel\\'>CCN51<br><span class=\\'stratn\\'>(N=1)</span></span></th>\\n<th class=\\'firstrow lastrow\\'><span class=\\'stratlabel\\'>Criollo<br><span class=\\'stratn\\'>(N=153)</span></span></th>\\n<th class=\\'firstrow lastrow\\'><span class=\\'stratlabel\\'>Criollo (Amarru)<br><span class=\\'stratn\\'>(N=2)</span></span></th>\\n<th class=\\'firstrow lastrow\\'><span class=\\'stratlabel\\'>Criollo (Ocumare 61)<br><span class=\\'stratn\\'>(N=2)</span></span></th>\\n<th class=\\'firstrow lastrow\\'><span class=\\'stratlabel\\'>Criollo (Ocumare 67)<br><span class=\\'stratn\\'>(N=1)</span></span></th>\\n<th class=\\'firstrow lastrow\\'><span class=\\'stratlabel\\'>Criollo (Ocumare 77)<br><span class=\\'stratn\\'>(N=1)</span></span></th>\\n<th class=\\'firstrow lastrow\\'><span class=\\'stratlabel\\'>Criollo (Ocumare)<br><span class=\\'stratn\\'>(N=1)</span></span></th>\\n<th class=\\'firstrow lastrow\\'><span class=\\'stratlabel\\'>Criollo (Porcelana)<br><span class=\\'stratn\\'>(N=10)</span></span></th>\\n<th class=\\'firstrow lastrow\\'><span class=\\'stratlabel\\'>Criollo (Wild)<br><span class=\\'stratn\\'>(N=1)</span></span></th>\\n<th class=\\'firstrow lastrow\\'><span class=\\'stratlabel\\'>Criollo, +<br><span class=\\'stratn\\'>(N=1)</span></span></th>\\n<th class=\\'firstrow lastrow\\'><span class=\\'stratlabel\\'>Criollo, Forastero<br><span class=\\'stratn\\'>(N=2)</span></span></th>\\n<th class=\\'firstrow lastrow\\'><span class=\\'stratlabel\\'>Criollo, Trinitario<br><span class=\\'stratn\\'>(N=39)</span></span></th>\\n<th class=\\'firstrow lastrow\\'><span class=\\'stratlabel\\'>EET<br><span class=\\'stratn\\'>(N=3)</span></span></th>\\n<th class=\\'firstrow lastrow\\'><span class=\\'stratlabel\\'>Forastero<br><span class=\\'stratn\\'>(N=87)</span></span></th>\\n<th class=\\'firstrow lastrow\\'><span class=\\'stratlabel\\'>Forastero (Amelonado)<br><span class=\\'stratn\\'>(N=1)</span></span></th>\\n<th class=\\'firstrow lastrow\\'><span class=\\'stratlabel\\'>Forastero (Arriba)<br><span class=\\'stratn\\'>(N=37)</span></span></th>\\n<th class=\\'firstrow lastrow\\'><span class=\\'stratlabel\\'>Forastero (Arriba) ASS<br><span class=\\'stratn\\'>(N=6)</span></span></th>\\n<th class=\\'firstrow lastrow\\'><span class=\\'stratlabel\\'>Forastero (Arriba) ASSS<br><span class=\\'stratn\\'>(N=1)</span></span></th>\\n<th class=\\'firstrow lastrow\\'><span class=\\'stratlabel\\'>Forastero (Catongo)<br><span class=\\'stratn\\'>(N=2)</span></span></th>\\n<th class=\\'firstrow lastrow\\'><span class=\\'stratlabel\\'>Forastero (Nacional)<br><span class=\\'stratn\\'>(N=52)</span></span></th>\\n<th class=\\'firstrow lastrow\\'><span class=\\'stratlabel\\'>Forastero (Parazinho)<br><span class=\\'stratn\\'>(N=8)</span></span></th>\\n<th class=\\'firstrow lastrow\\'><span class=\\'stratlabel\\'>Forastero, Trinitario<br><span class=\\'stratn\\'>(N=1)</span></span></th>\\n<th class=\\'firstrow lastrow\\'><span class=\\'stratlabel\\'>Forastero(Arriba, CCN)<br><span class=\\'stratn\\'>(N=1)</span></span></th>\\n<th class=\\'firstrow lastrow\\'><span class=\\'stratlabel\\'>Matina<br><span class=\\'stratn\\'>(N=3)</span></span></th>\\n<th class=\\'firstrow lastrow\\'><span class=\\'stratlabel\\'>Nacional<br><span class=\\'stratn\\'>(N=2)</span></span></th>\\n<th class=\\'firstrow lastrow\\'><span class=\\'stratlabel\\'>Nacional (Arriba)<br><span class=\\'stratn\\'>(N=3)</span></span></th>\\n<th class=\\'firstrow lastrow\\'><span class=\\'stratlabel\\'>Trinitario<br><span class=\\'stratn\\'>(N=419)</span></span></th>\\n<th class=\\'firstrow lastrow\\'><span class=\\'stratlabel\\'>Trinitario (85% Criollo)<br><span class=\\'stratn\\'>(N=2)</span></span></th>\\n<th class=\\'firstrow lastrow\\'><span class=\\'stratlabel\\'>Trinitario (Amelonado)<br><span class=\\'stratn\\'>(N=1)</span></span></th>\\n<th class=\\'firstrow lastrow\\'><span class=\\'stratlabel\\'>Trinitario (Scavina)<br><span class=\\'stratn\\'>(N=1)</span></span></th>\\n<th class=\\'firstrow lastrow\\'><span class=\\'stratlabel\\'>Trinitario, Criollo<br><span class=\\'stratn\\'>(N=9)</span></span></th>\\n<th class=\\'firstrow lastrow\\'><span class=\\'stratlabel\\'>Trinitario, Forastero<br><span class=\\'stratn\\'>(N=2)</span></span></th>\\n<th class=\\'firstrow lastrow\\'><span class=\\'stratlabel\\'>Trinitario, Nacional<br><span class=\\'stratn\\'>(N=1)</span></span></th>\\n<th class=\\'firstrow lastrow\\'><span class=\\'stratlabel\\'>Trinitario, TCGA<br><span class=\\'stratn\\'>(N=1)</span></span></th>\\n<th class=\\'firstrow lastrow\\'><span class=\\'stratlabel\\'>Overall<br><span class=\\'stratn\\'>(N=1795)</span></span></th>\\n</tr>\\n</thead>\\n<tbody>\\n<tr>\\n<td class=\\'rowlabel firstrow\\'>Cocoa.Percent</td>\\n<td class=\\'firstrow\\'></td>\\n<td class=\\'firstrow\\'></td>\\n<td class=\\'firstrow\\'></td>\\n<td class=\\'firstrow\\'></td>\\n<td class=\\'firstrow\\'></td>\\n<td class=\\'firstrow\\'></td>\\n<td class=\\'firstrow\\'></td>\\n<td class=\\'firstrow\\'></td>\\n<td class=\\'firstrow\\'></td>\\n<td class=\\'firstrow\\'></td>\\n<td class=\\'firstrow\\'></td>\\n<td class=\\'firstrow\\'></td>\\n<td class=\\'firstrow\\'></td>\\n<td class=\\'firstrow\\'></td>\\n<td class=\\'firstrow\\'></td>\\n<td class=\\'firstrow\\'></td>\\n<td class=\\'firstrow\\'></td>\\n<td class=\\'firstrow\\'></td>\\n<td class=\\'firstrow\\'></td>\\n<td class=\\'firstrow\\'></td>\\n<td class=\\'firstrow\\'></td>\\n<td class=\\'firstrow\\'></td>\\n<td class=\\'firstrow\\'></td>\\n<td class=\\'firstrow\\'></td>\\n<td class=\\'firstrow\\'></td>\\n<td class=\\'firstrow\\'></td>\\n<td class=\\'firstrow\\'></td>\\n<td class=\\'firstrow\\'></td>\\n<td class=\\'firstrow\\'></td>\\n<td class=\\'firstrow\\'></td>\\n<td class=\\'firstrow\\'></td>\\n<td class=\\'firstrow\\'></td>\\n<td class=\\'firstrow\\'></td>\\n<td class=\\'firstrow\\'></td>\\n<td class=\\'firstrow\\'></td>\\n<td class=\\'firstrow\\'></td>\\n<td class=\\'firstrow\\'></td>\\n<td class=\\'firstrow\\'></td>\\n<td class=\\'firstrow\\'></td>\\n<td class=\\'firstrow\\'></td>\\n<td class=\\'firstrow\\'></td>\\n<td class=\\'firstrow\\'></td>\\n<td class=\\'firstrow\\'></td>\\n</tr>\\n<tr>\\n<td class=\\'rowlabel\\'>Mean (SD)</td>\\n<td>70 (&plusmn; NA)</td>\\n<td>72 (&plusmn; 6.6)</td>\\n<td>70 (&plusmn; NA)</td>\\n<td>74 (&plusmn; 0)</td>\\n<td>68 (&plusmn; 0.71)</td>\\n<td>71 (&plusmn; 3.6)</td>\\n<td>71 (&plusmn; 6.5)</td>\\n<td>70 (&plusmn; NA)</td>\\n<td>65 (&plusmn; NA)</td>\\n<td>72 (&plusmn; 5.8)</td>\\n<td>70 (&plusmn; 0)</td>\\n<td>74 (&plusmn; 2.1)</td>\\n<td>70 (&plusmn; NA)</td>\\n<td>70 (&plusmn; NA)</td>\\n<td>80 (&plusmn; NA)</td>\\n<td>70 (&plusmn; 4.0)</td>\\n<td>68 (&plusmn; NA)</td>\\n<td>65 (&plusmn; NA)</td>\\n<td>77 (&plusmn; 2.1)</td>\\n<td>72 (&plusmn; 4.6)</td>\\n<td>71 (&plusmn; 5.1)</td>\\n<td>72 (&plusmn; 6.6)</td>\\n<td>70 (&plusmn; NA)</td>\\n<td>73 (&plusmn; 12)</td>\\n<td>69 (&plusmn; 10)</td>\\n<td>70 (&plusmn; NA)</td>\\n<td>73 (&plusmn; 3.5)</td>\\n<td>73 (&plusmn; 7.5)</td>\\n<td>69 (&plusmn; 10)</td>\\n<td>70 (&plusmn; NA)</td>\\n<td>75 (&plusmn; NA)</td>\\n<td>74 (&plusmn; 6.9)</td>\\n<td>71 (&plusmn; 1.4)</td>\\n<td>73 (&plusmn; 2.9)</td>\\n<td>72 (&plusmn; 5.3)</td>\\n<td>70 (&plusmn; 0)</td>\\n<td>70 (&plusmn; NA)</td>\\n<td>70 (&plusmn; NA)</td>\\n<td>68 (&plusmn; 3.2)</td>\\n<td>71 (&plusmn; 6.4)</td>\\n<td>70 (&plusmn; NA)</td>\\n<td>72 (&plusmn; NA)</td>\\n<td>72 (&plusmn; 6.4)</td>\\n</tr>\\n<tr>\\n<td class=\\'rowlabel lastrow\\'>Missing</td>\\n<td class=\\'lastrow\\'>0 (0%)</td>\\n<td class=\\'lastrow\\'>15 (1.7%)</td>\\n<td class=\\'lastrow\\'>0 (0%)</td>\\n<td class=\\'lastrow\\'>0 (0%)</td>\\n<td class=\\'lastrow\\'>0 (0%)</td>\\n<td class=\\'lastrow\\'>0 (0%)</td>\\n<td class=\\'lastrow\\'>0 (0%)</td>\\n<td class=\\'lastrow\\'>0 (0%)</td>\\n<td class=\\'lastrow\\'>0 (0%)</td>\\n<td class=\\'lastrow\\'>4 (2.6%)</td>\\n<td class=\\'lastrow\\'>0 (0%)</td>\\n<td class=\\'lastrow\\'>0 (0%)</td>\\n<td class=\\'lastrow\\'>0 (0%)</td>\\n<td class=\\'lastrow\\'>0 (0%)</td>\\n<td class=\\'lastrow\\'>0 (0%)</td>\\n<td class=\\'lastrow\\'>0 (0%)</td>\\n<td class=\\'lastrow\\'>0 (0%)</td>\\n<td class=\\'lastrow\\'>0 (0%)</td>\\n<td class=\\'lastrow\\'>0 (0%)</td>\\n<td class=\\'lastrow\\'>0 (0%)</td>\\n<td class=\\'lastrow\\'>0 (0%)</td>\\n<td class=\\'lastrow\\'>0 (0%)</td>\\n<td class=\\'lastrow\\'>0 (0%)</td>\\n<td class=\\'lastrow\\'>0 (0%)</td>\\n<td class=\\'lastrow\\'>0 (0%)</td>\\n<td class=\\'lastrow\\'>0 (0%)</td>\\n<td class=\\'lastrow\\'>0 (0%)</td>\\n<td class=\\'lastrow\\'>0 (0%)</td>\\n<td class=\\'lastrow\\'>0 (0%)</td>\\n<td class=\\'lastrow\\'>0 (0%)</td>\\n<td class=\\'lastrow\\'>0 (0%)</td>\\n<td class=\\'lastrow\\'>0 (0%)</td>\\n<td class=\\'lastrow\\'>0 (0%)</td>\\n<td class=\\'lastrow\\'>0 (0%)</td>\\n<td class=\\'lastrow\\'>1 (0.2%)</td>\\n<td class=\\'lastrow\\'>0 (0%)</td>\\n<td class=\\'lastrow\\'>0 (0%)</td>\\n<td class=\\'lastrow\\'>0 (0%)</td>\\n<td class=\\'lastrow\\'>0 (0%)</td>\\n<td class=\\'lastrow\\'>0 (0%)</td>\\n<td class=\\'lastrow\\'>0 (0%)</td>\\n<td class=\\'lastrow\\'>0 (0%)</td>\\n<td class=\\'lastrow\\'>20 (1.1%)</td>\\n</tr>\\n</tbody>\\n</table>\\n'","text/latex":"'\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n
NA
(N=1)		 
(N=887)		Amazon
(N=1)		Amazon mix
(N=2)		Amazon, ICS
(N=2)		Beniano
(N=3)		Blend
(N=41)		Blend-Forastero,Criollo
(N=1)		CCN51
(N=1)		Criollo
(N=153)		Criollo (Amarru)
(N=2)		Criollo (Ocumare 61)
(N=2)		Criollo (Ocumare 67)
(N=1)		Criollo (Ocumare 77)
(N=1)		Criollo (Ocumare)
(N=1)		Criollo (Porcelana)
(N=10)		Criollo (Wild)
(N=1)		Criollo, +
(N=1)		Criollo, Forastero
(N=2)		Criollo, Trinitario
(N=39)		EET
(N=3)		Forastero
(N=87)		Forastero (Amelonado)
(N=1)		Forastero (Arriba)
(N=37)		Forastero (Arriba) ASS
(N=6)		Forastero (Arriba) ASSS
(N=1)		Forastero (Catongo)
(N=2)		Forastero (Nacional)
(N=52)		Forastero (Parazinho)
(N=8)		Forastero, Trinitario
(N=1)		Forastero(Arriba, CCN)
(N=1)		Matina
(N=3)		Nacional
(N=2)		Nacional (Arriba)
(N=3)		Trinitario
(N=419)		Trinitario (85\\% Criollo)
(N=2)		Trinitario (Amelonado)
(N=1)		Trinitario (Scavina)
(N=1)		Trinitario, Criollo
(N=9)		Trinitario, Forastero
(N=2)		Trinitario, Nacional
(N=1)		Trinitario, TCGA
(N=1)		Overall
(N=1795)
Cocoa.Percent
Mean (SD)70 (\\± NA)72 (\\± 6.6)70 (\\± NA)74 (\\± 0)68 (\\± 0.71)71 (\\± 3.6)71 (\\± 6.5)70 (\\± NA)65 (\\± NA)72 (\\± 5.8)70 (\\± 0)74 (\\± 2.1)70 (\\± NA)70 (\\± NA)80 (\\± NA)70 (\\± 4.0)68 (\\± NA)65 (\\± NA)77 (\\± 2.1)72 (\\± 4.6)71 (\\± 5.1)72 (\\± 6.6)70 (\\± NA)73 (\\± 12)69 (\\± 10)70 (\\± NA)73 (\\± 3.5)73 (\\± 7.5)69 (\\± 10)70 (\\± NA)75 (\\± NA)74 (\\± 6.9)71 (\\± 1.4)73 (\\± 2.9)72 (\\± 5.3)70 (\\± 0)70 (\\± NA)70 (\\± NA)68 (\\± 3.2)71 (\\± 6.4)70 (\\± NA)72 (\\± NA)72 (\\± 6.4)
Missing0 (0\\%)15 (1.7\\%)0 (0\\%)0 (0\\%)0 (0\\%)0 (0\\%)0 (0\\%)0 (0\\%)0 (0\\%)4 (2.6\\%)0 (0\\%)0 (0\\%)0 (0\\%)0 (0\\%)0 (0\\%)0 (0\\%)0 (0\\%)0 (0\\%)0 (0\\%)0 (0\\%)0 (0\\%)0 (0\\%)0 (0\\%)0 (0\\%)0 (0\\%)0 (0\\%)0 (0\\%)0 (0\\%)0 (0\\%)0 (0\\%)0 (0\\%)0 (0\\%)0 (0\\%)0 (0\\%)1 (0.2\\%)0 (0\\%)0 (0\\%)0 (0\\%)0 (0\\%)0 (0\\%)0 (0\\%)0 (0\\%)20 (1.1\\%)
\\textbackslash{}n'","text/plain":["\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","
NA
(N=1)		 
(N=887)		Amazon
(N=1)		Amazon mix
(N=2)		Amazon, ICS
(N=2)		Beniano
(N=3)		Blend
(N=41)		Blend-Forastero,Criollo
(N=1)		CCN51
(N=1)		Criollo
(N=153)		Criollo (Amarru)
(N=2)		Criollo (Ocumare 61)
(N=2)		Criollo (Ocumare 67)
(N=1)		Criollo (Ocumare 77)
(N=1)		Criollo (Ocumare)
(N=1)		Criollo (Porcelana)
(N=10)		Criollo (Wild)
(N=1)		Criollo, +
(N=1)		Criollo, Forastero
(N=2)		Criollo, Trinitario
(N=39)		EET
(N=3)		Forastero
(N=87)		Forastero (Amelonado)
(N=1)		Forastero (Arriba)
(N=37)		Forastero (Arriba) ASS
(N=6)		Forastero (Arriba) ASSS
(N=1)		Forastero (Catongo)
(N=2)		Forastero (Nacional)
(N=52)		Forastero (Parazinho)
(N=8)		Forastero, Trinitario
(N=1)		Forastero(Arriba, CCN)
(N=1)		Matina
(N=3)		Nacional
(N=2)		Nacional (Arriba)
(N=3)		Trinitario
(N=419)		Trinitario (85% Criollo)
(N=2)		Trinitario (Amelonado)
(N=1)		Trinitario (Scavina)
(N=1)		Trinitario, Criollo
(N=9)		Trinitario, Forastero
(N=2)		Trinitario, Nacional
(N=1)		Trinitario, TCGA
(N=1)		Overall
(N=1795)
Cocoa.Percent
Mean (SD)70 (± NA)72 (± 6.6)70 (± NA)74 (± 0)68 (± 0.71)71 (± 3.6)71 (± 6.5)70 (± NA)65 (± NA)72 (± 5.8)70 (± 0)74 (± 2.1)70 (± NA)70 (± NA)80 (± NA)70 (± 4.0)68 (± NA)65 (± NA)77 (± 2.1)72 (± 4.6)71 (± 5.1)72 (± 6.6)70 (± NA)73 (± 12)69 (± 10)70 (± NA)73 (± 3.5)73 (± 7.5)69 (± 10)70 (± NA)75 (± NA)74 (± 6.9)71 (± 1.4)73 (± 2.9)72 (± 5.3)70 (± 0)70 (± NA)70 (± NA)68 (± 3.2)71 (± 6.4)70 (± NA)72 (± NA)72 (± 6.4)
Missing0 (0%)15 (1.7%)0 (0%)0 (0%)0 (0%)0 (0%)0 (0%)0 (0%)0 (0%)4 (2.6%)0 (0%)0 (0%)0 (0%)0 (0%)0 (0%)0 (0%)0 (0%)0 (0%)0 (0%)0 (0%)0 (0%)0 (0%)0 (0%)0 (0%)0 (0%)0 (0%)0 (0%)0 (0%)0 (0%)0 (0%)0 (0%)0 (0%)0 (0%)0 (0%)1 (0.2%)0 (0%)0 (0%)0 (0%)0 (0%)0 (0%)0 (0%)0 (0%)20 (1.1%)
"]},"metadata":{}},{"output_type":"stream","name":"stderr","text":["Warning message in .table1.internal(x = x, labels = labels, groupspan = groupspan, :\n","“Table has 43 columns. Are you sure this is what you want?”\n"]},{"output_type":"display_data","data":{"text/html":["'<table class=\"Rtable1\">\\n<thead>\\n<tr>\\n<th class=\\'rowlabel firstrow lastrow\\'></th>\\n<th class=\\'firstrow lastrow\\'><span class=\\'stratlabel\\'>NA<br><span class=\\'stratn\\'>(N=1)</span></span></th>\\n<th class=\\'firstrow lastrow\\'><span class=\\'stratlabel\\'> <br><span class=\\'stratn\\'>(N=887)</span></span></th>\\n<th class=\\'firstrow lastrow\\'><span class=\\'stratlabel\\'>Amazon<br><span class=\\'stratn\\'>(N=1)</span></span></th>\\n<th class=\\'firstrow lastrow\\'><span class=\\'stratlabel\\'>Amazon mix<br><span class=\\'stratn\\'>(N=2)</span></span></th>\\n<th class=\\'firstrow lastrow\\'><span class=\\'stratlabel\\'>Amazon, ICS<br><span class=\\'stratn\\'>(N=2)</span></span></th>\\n<th class=\\'firstrow lastrow\\'><span class=\\'stratlabel\\'>Beniano<br><span class=\\'stratn\\'>(N=3)</span></span></th>\\n<th class=\\'firstrow lastrow\\'><span class=\\'stratlabel\\'>Blend<br><span class=\\'stratn\\'>(N=41)</span></span></th>\\n<th class=\\'firstrow lastrow\\'><span class=\\'stratlabel\\'>Blend-Forastero,Criollo<br><span class=\\'stratn\\'>(N=1)</span></span></th>\\n<th class=\\'firstrow lastrow\\'><span class=\\'stratlabel\\'>CCN51<br><span class=\\'stratn\\'>(N=1)</span></span></th>\\n<th class=\\'firstrow lastrow\\'><span class=\\'stratlabel\\'>Criollo<br><span class=\\'stratn\\'>(N=153)</span></span></th>\\n<th class=\\'firstrow lastrow\\'><span class=\\'stratlabel\\'>Criollo (Amarru)<br><span class=\\'stratn\\'>(N=2)</span></span></th>\\n<th class=\\'firstrow lastrow\\'><span class=\\'stratlabel\\'>Criollo (Ocumare 61)<br><span class=\\'stratn\\'>(N=2)</span></span></th>\\n<th class=\\'firstrow lastrow\\'><span class=\\'stratlabel\\'>Criollo (Ocumare 67)<br><span class=\\'stratn\\'>(N=1)</span></span></th>\\n<th class=\\'firstrow lastrow\\'><span class=\\'stratlabel\\'>Criollo (Ocumare 77)<br><span class=\\'stratn\\'>(N=1)</span></span></th>\\n<th class=\\'firstrow lastrow\\'><span class=\\'stratlabel\\'>Criollo (Ocumare)<br><span class=\\'stratn\\'>(N=1)</span></span></th>\\n<th class=\\'firstrow lastrow\\'><span class=\\'stratlabel\\'>Criollo (Porcelana)<br><span class=\\'stratn\\'>(N=10)</span></span></th>\\n<th class=\\'firstrow lastrow\\'><span class=\\'stratlabel\\'>Criollo (Wild)<br><span class=\\'stratn\\'>(N=1)</span></span></th>\\n<th class=\\'firstrow lastrow\\'><span class=\\'stratlabel\\'>Criollo, +<br><span class=\\'stratn\\'>(N=1)</span></span></th>\\n<th class=\\'firstrow lastrow\\'><span class=\\'stratlabel\\'>Criollo, Forastero<br><span class=\\'stratn\\'>(N=2)</span></span></th>\\n<th class=\\'firstrow lastrow\\'><span class=\\'stratlabel\\'>Criollo, Trinitario<br><span class=\\'stratn\\'>(N=39)</span></span></th>\\n<th class=\\'firstrow lastrow\\'><span class=\\'stratlabel\\'>EET<br><span class=\\'stratn\\'>(N=3)</span></span></th>\\n<th class=\\'firstrow lastrow\\'><span class=\\'stratlabel\\'>Forastero<br><span class=\\'stratn\\'>(N=87)</span></span></th>\\n<th class=\\'firstrow lastrow\\'><span class=\\'stratlabel\\'>Forastero (Amelonado)<br><span class=\\'stratn\\'>(N=1)</span></span></th>\\n<th class=\\'firstrow lastrow\\'><span class=\\'stratlabel\\'>Forastero (Arriba)<br><span class=\\'stratn\\'>(N=37)</span></span></th>\\n<th class=\\'firstrow lastrow\\'><span class=\\'stratlabel\\'>Forastero (Arriba) ASS<br><span class=\\'stratn\\'>(N=6)</span></span></th>\\n<th class=\\'firstrow lastrow\\'><span class=\\'stratlabel\\'>Forastero (Arriba) ASSS<br><span class=\\'stratn\\'>(N=1)</span></span></th>\\n<th class=\\'firstrow lastrow\\'><span class=\\'stratlabel\\'>Forastero (Catongo)<br><span class=\\'stratn\\'>(N=2)</span></span></th>\\n<th class=\\'firstrow lastrow\\'><span class=\\'stratlabel\\'>Forastero (Nacional)<br><span class=\\'stratn\\'>(N=52)</span></span></th>\\n<th class=\\'firstrow lastrow\\'><span class=\\'stratlabel\\'>Forastero (Parazinho)<br><span class=\\'stratn\\'>(N=8)</span></span></th>\\n<th class=\\'firstrow lastrow\\'><span class=\\'stratlabel\\'>Forastero, Trinitario<br><span class=\\'stratn\\'>(N=1)</span></span></th>\\n<th class=\\'firstrow lastrow\\'><span class=\\'stratlabel\\'>Forastero(Arriba, CCN)<br><span class=\\'stratn\\'>(N=1)</span></span></th>\\n<th class=\\'firstrow lastrow\\'><span class=\\'stratlabel\\'>Matina<br><span class=\\'stratn\\'>(N=3)</span></span></th>\\n<th class=\\'firstrow lastrow\\'><span class=\\'stratlabel\\'>Nacional<br><span class=\\'stratn\\'>(N=2)</span></span></th>\\n<th class=\\'firstrow lastrow\\'><span class=\\'stratlabel\\'>Nacional (Arriba)<br><span class=\\'stratn\\'>(N=3)</span></span></th>\\n<th class=\\'firstrow lastrow\\'><span class=\\'stratlabel\\'>Trinitario<br><span class=\\'stratn\\'>(N=419)</span></span></th>\\n<th class=\\'firstrow lastrow\\'><span class=\\'stratlabel\\'>Trinitario (85% Criollo)<br><span class=\\'stratn\\'>(N=2)</span></span></th>\\n<th class=\\'firstrow lastrow\\'><span class=\\'stratlabel\\'>Trinitario (Amelonado)<br><span class=\\'stratn\\'>(N=1)</span></span></th>\\n<th class=\\'firstrow lastrow\\'><span class=\\'stratlabel\\'>Trinitario (Scavina)<br><span class=\\'stratn\\'>(N=1)</span></span></th>\\n<th class=\\'firstrow lastrow\\'><span class=\\'stratlabel\\'>Trinitario, Criollo<br><span class=\\'stratn\\'>(N=9)</span></span></th>\\n<th class=\\'firstrow lastrow\\'><span class=\\'stratlabel\\'>Trinitario, Forastero<br><span class=\\'stratn\\'>(N=2)</span></span></th>\\n<th class=\\'firstrow lastrow\\'><span class=\\'stratlabel\\'>Trinitario, Nacional<br><span class=\\'stratn\\'>(N=1)</span></span></th>\\n<th class=\\'firstrow lastrow\\'><span class=\\'stratlabel\\'>Trinitario, TCGA<br><span class=\\'stratn\\'>(N=1)</span></span></th>\\n<th class=\\'firstrow lastrow\\'><span class=\\'stratlabel\\'>Overall<br><span class=\\'stratn\\'>(N=1795)</span></span></th>\\n</tr>\\n</thead>\\n<tbody>\\n<tr>\\n<td class=\\'rowlabel firstrow\\'>Cocoa.Percent</td>\\n<td class=\\'firstrow\\'></td>\\n<td class=\\'firstrow\\'></td>\\n<td class=\\'firstrow\\'></td>\\n<td class=\\'firstrow\\'></td>\\n<td class=\\'firstrow\\'></td>\\n<td class=\\'firstrow\\'></td>\\n<td class=\\'firstrow\\'></td>\\n<td class=\\'firstrow\\'></td>\\n<td class=\\'firstrow\\'></td>\\n<td class=\\'firstrow\\'></td>\\n<td class=\\'firstrow\\'></td>\\n<td class=\\'firstrow\\'></td>\\n<td class=\\'firstrow\\'></td>\\n<td class=\\'firstrow\\'></td>\\n<td class=\\'firstrow\\'></td>\\n<td class=\\'firstrow\\'></td>\\n<td class=\\'firstrow\\'></td>\\n<td class=\\'firstrow\\'></td>\\n<td class=\\'firstrow\\'></td>\\n<td class=\\'firstrow\\'></td>\\n<td class=\\'firstrow\\'></td>\\n<td class=\\'firstrow\\'></td>\\n<td class=\\'firstrow\\'></td>\\n<td class=\\'firstrow\\'></td>\\n<td class=\\'firstrow\\'></td>\\n<td class=\\'firstrow\\'></td>\\n<td class=\\'firstrow\\'></td>\\n<td class=\\'firstrow\\'></td>\\n<td class=\\'firstrow\\'></td>\\n<td class=\\'firstrow\\'></td>\\n<td class=\\'firstrow\\'></td>\\n<td class=\\'firstrow\\'></td>\\n<td class=\\'firstrow\\'></td>\\n<td class=\\'firstrow\\'></td>\\n<td class=\\'firstrow\\'></td>\\n<td class=\\'firstrow\\'></td>\\n<td class=\\'firstrow\\'></td>\\n<td class=\\'firstrow\\'></td>\\n<td class=\\'firstrow\\'></td>\\n<td class=\\'firstrow\\'></td>\\n<td class=\\'firstrow\\'></td>\\n<td class=\\'firstrow\\'></td>\\n<td class=\\'firstrow\\'></td>\\n</tr>\\n<tr>\\n<td class=\\'rowlabel\\'>Mean (SD)</td>\\n<td>70 (&plusmn; NA)</td>\\n<td>72 (&plusmn; 6.6)</td>\\n<td>70 (&plusmn; NA)</td>\\n<td>74 (&plusmn; 0)</td>\\n<td>68 (&plusmn; 0.71)</td>\\n<td>71 (&plusmn; 3.6)</td>\\n<td>71 (&plusmn; 6.5)</td>\\n<td>70 (&plusmn; NA)</td>\\n<td>65 (&plusmn; NA)</td>\\n<td>72 (&plusmn; 5.8)</td>\\n<td>70 (&plusmn; 0)</td>\\n<td>74 (&plusmn; 2.1)</td>\\n<td>70 (&plusmn; NA)</td>\\n<td>70 (&plusmn; NA)</td>\\n<td>80 (&plusmn; NA)</td>\\n<td>70 (&plusmn; 4.0)</td>\\n<td>68 (&plusmn; NA)</td>\\n<td>65 (&plusmn; NA)</td>\\n<td>77 (&plusmn; 2.1)</td>\\n<td>72 (&plusmn; 4.6)</td>\\n<td>71 (&plusmn; 5.1)</td>\\n<td>72 (&plusmn; 6.6)</td>\\n<td>70 (&plusmn; NA)</td>\\n<td>73 (&plusmn; 12)</td>\\n<td>69 (&plusmn; 10)</td>\\n<td>70 (&plusmn; NA)</td>\\n<td>73 (&plusmn; 3.5)</td>\\n<td>73 (&plusmn; 7.5)</td>\\n<td>69 (&plusmn; 10)</td>\\n<td>70 (&plusmn; NA)</td>\\n<td>75 (&plusmn; NA)</td>\\n<td>74 (&plusmn; 6.9)</td>\\n<td>71 (&plusmn; 1.4)</td>\\n<td>73 (&plusmn; 2.9)</td>\\n<td>72 (&plusmn; 5.3)</td>\\n<td>70 (&plusmn; 0)</td>\\n<td>70 (&plusmn; NA)</td>\\n<td>70 (&plusmn; NA)</td>\\n<td>68 (&plusmn; 3.2)</td>\\n<td>71 (&plusmn; 6.4)</td>\\n<td>70 (&plusmn; NA)</td>\\n<td>72 (&plusmn; NA)</td>\\n<td>72 (&plusmn; 6.4)</td>\\n</tr>\\n<tr>\\n<td class=\\'rowlabel lastrow\\'>Missing</td>\\n<td class=\\'lastrow\\'>0 (0%)</td>\\n<td class=\\'lastrow\\'>15 (1.7%)</td>\\n<td class=\\'lastrow\\'>0 (0%)</td>\\n<td class=\\'lastrow\\'>0 (0%)</td>\\n<td class=\\'lastrow\\'>0 (0%)</td>\\n<td class=\\'lastrow\\'>0 (0%)</td>\\n<td class=\\'lastrow\\'>0 (0%)</td>\\n<td class=\\'lastrow\\'>0 (0%)</td>\\n<td class=\\'lastrow\\'>0 (0%)</td>\\n<td class=\\'lastrow\\'>4 (2.6%)</td>\\n<td class=\\'lastrow\\'>0 (0%)</td>\\n<td class=\\'lastrow\\'>0 (0%)</td>\\n<td class=\\'lastrow\\'>0 (0%)</td>\\n<td class=\\'lastrow\\'>0 (0%)</td>\\n<td class=\\'lastrow\\'>0 (0%)</td>\\n<td class=\\'lastrow\\'>0 (0%)</td>\\n<td class=\\'lastrow\\'>0 (0%)</td>\\n<td class=\\'lastrow\\'>0 (0%)</td>\\n<td class=\\'lastrow\\'>0 (0%)</td>\\n<td class=\\'lastrow\\'>0 (0%)</td>\\n<td class=\\'lastrow\\'>0 (0%)</td>\\n<td class=\\'lastrow\\'>0 (0%)</td>\\n<td class=\\'lastrow\\'>0 (0%)</td>\\n<td class=\\'lastrow\\'>0 (0%)</td>\\n<td class=\\'lastrow\\'>0 (0%)</td>\\n<td class=\\'lastrow\\'>0 (0%)</td>\\n<td class=\\'lastrow\\'>0 (0%)</td>\\n<td class=\\'lastrow\\'>0 (0%)</td>\\n<td class=\\'lastrow\\'>0 (0%)</td>\\n<td class=\\'lastrow\\'>0 (0%)</td>\\n<td class=\\'lastrow\\'>0 (0%)</td>\\n<td class=\\'lastrow\\'>0 (0%)</td>\\n<td class=\\'lastrow\\'>0 (0%)</td>\\n<td class=\\'lastrow\\'>0 (0%)</td>\\n<td class=\\'lastrow\\'>1 (0.2%)</td>\\n<td class=\\'lastrow\\'>0 (0%)</td>\\n<td class=\\'lastrow\\'>0 (0%)</td>\\n<td class=\\'lastrow\\'>0 (0%)</td>\\n<td class=\\'lastrow\\'>0 (0%)</td>\\n<td class=\\'lastrow\\'>0 (0%)</td>\\n<td class=\\'lastrow\\'>0 (0%)</td>\\n<td class=\\'lastrow\\'>0 (0%)</td>\\n<td class=\\'lastrow\\'>20 (1.1%)</td>\\n</tr>\\n<tr>\\n<td class=\\'rowlabel firstrow\\'>choco$Rating</td>\\n<td class=\\'firstrow\\'></td>\\n<td class=\\'firstrow\\'></td>\\n<td class=\\'firstrow\\'></td>\\n<td class=\\'firstrow\\'></td>\\n<td class=\\'firstrow\\'></td>\\n<td class=\\'firstrow\\'></td>\\n<td class=\\'firstrow\\'></td>\\n<td class=\\'firstrow\\'></td>\\n<td class=\\'firstrow\\'></td>\\n<td class=\\'firstrow\\'></td>\\n<td class=\\'firstrow\\'></td>\\n<td class=\\'firstrow\\'></td>\\n<td class=\\'firstrow\\'></td>\\n<td class=\\'firstrow\\'></td>\\n<td class=\\'firstrow\\'></td>\\n<td class=\\'firstrow\\'></td>\\n<td class=\\'firstrow\\'></td>\\n<td class=\\'firstrow\\'></td>\\n<td class=\\'firstrow\\'></td>\\n<td class=\\'firstrow\\'></td>\\n<td class=\\'firstrow\\'></td>\\n<td class=\\'firstrow\\'></td>\\n<td class=\\'firstrow\\'></td>\\n<td class=\\'firstrow\\'></td>\\n<td class=\\'firstrow\\'></td>\\n<td class=\\'firstrow\\'></td>\\n<td class=\\'firstrow\\'></td>\\n<td class=\\'firstrow\\'></td>\\n<td class=\\'firstrow\\'></td>\\n<td class=\\'firstrow\\'></td>\\n<td class=\\'firstrow\\'></td>\\n<td class=\\'firstrow\\'></td>\\n<td class=\\'firstrow\\'></td>\\n<td class=\\'firstrow\\'></td>\\n<td class=\\'firstrow\\'></td>\\n<td class=\\'firstrow\\'></td>\\n<td class=\\'firstrow\\'></td>\\n<td class=\\'firstrow\\'></td>\\n<td class=\\'firstrow\\'></td>\\n<td class=\\'firstrow\\'></td>\\n<td class=\\'firstrow\\'></td>\\n<td class=\\'firstrow\\'></td>\\n<td class=\\'firstrow\\'></td>\\n</tr>\\n<tr>\\n<td class=\\'rowlabel lastrow\\'>Mean (SD)</td>\\n<td class=\\'lastrow\\'>4.0 (&plusmn; NA)</td>\\n<td class=\\'lastrow\\'>3.1 (&plusmn; 0.48)</td>\\n<td class=\\'lastrow\\'>3.3 (&plusmn; NA)</td>\\n<td class=\\'lastrow\\'>3.8 (&plusmn; 0.35)</td>\\n<td class=\\'lastrow\\'>3.6 (&plusmn; 0.18)</td>\\n<td class=\\'lastrow\\'>3.6 (&plusmn; 0.52)</td>\\n<td class=\\'lastrow\\'>3.4 (&plusmn; 0.56)</td>\\n<td class=\\'lastrow\\'>3.8 (&plusmn; NA)</td>\\n<td class=\\'lastrow\\'>3.5 (&plusmn; NA)</td>\\n<td class=\\'lastrow\\'>3.2 (&plusmn; 0.46)</td>\\n<td class=\\'lastrow\\'>3.3 (&plusmn; 0.35)</td>\\n<td class=\\'lastrow\\'>3.3 (&plusmn; 0)</td>\\n<td class=\\'lastrow\\'>4.0 (&plusmn; NA)</td>\\n<td class=\\'lastrow\\'>3.8 (&plusmn; NA)</td>\\n<td class=\\'lastrow\\'>3.0 (&plusmn; NA)</td>\\n<td class=\\'lastrow\\'>3.4 (&plusmn; 0.50)</td>\\n<td class=\\'lastrow\\'>4.0 (&plusmn; NA)</td>\\n<td class=\\'lastrow\\'>3.5 (&plusmn; NA)</td>\\n<td class=\\'lastrow\\'>3.6 (&plusmn; 0.18)</td>\\n<td class=\\'lastrow\\'>3.3 (&plusmn; 0.40)</td>\\n<td class=\\'lastrow\\'>3.6 (&plusmn; 0.14)</td>\\n<td class=\\'lastrow\\'>3.1 (&plusmn; 0.54)</td>\\n<td class=\\'lastrow\\'>3.8 (&plusmn; NA)</td>\\n<td class=\\'lastrow\\'>2.8 (&plusmn; 0.60)</td>\\n<td class=\\'lastrow\\'>2.9 (&plusmn; 0.54)</td>\\n<td class=\\'lastrow\\'>3.5 (&plusmn; NA)</td>\\n<td class=\\'lastrow\\'>3.4 (&plusmn; 0.18)</td>\\n<td class=\\'lastrow\\'>3.3 (&plusmn; 0.44)</td>\\n<td class=\\'lastrow\\'>3.5 (&plusmn; 0.36)</td>\\n<td class=\\'lastrow\\'>3.0 (&plusmn; NA)</td>\\n<td class=\\'lastrow\\'>3.0 (&plusmn; NA)</td>\\n<td class=\\'lastrow\\'>3.4 (&plusmn; 0.38)</td>\\n<td class=\\'lastrow\\'>3.0 (&plusmn; 0.71)</td>\\n<td class=\\'lastrow\\'>3.3 (&plusmn; 0.50)</td>\\n<td class=\\'lastrow\\'>3.2 (&plusmn; 0.42)</td>\\n<td class=\\'lastrow\\'>3.9 (&plusmn; 0.18)</td>\\n<td class=\\'lastrow\\'>3.0 (&plusmn; NA)</td>\\n<td class=\\'lastrow\\'>3.5 (&plusmn; NA)</td>\\n<td class=\\'lastrow\\'>3.0 (&plusmn; 0.52)</td>\\n<td class=\\'lastrow\\'>3.0 (&plusmn; 0)</td>\\n<td class=\\'lastrow\\'>3.8 (&plusmn; NA)</td>\\n<td class=\\'lastrow\\'>3.8 (&plusmn; NA)</td>\\n<td class=\\'lastrow\\'>3.2 (&plusmn; 0.48)</td>\\n</tr>\\n</tbody>\\n</table>\\n'"],"text/markdown":"'<table class=\"Rtable1\">\\n<thead>\\n<tr>\\n<th class=\\'rowlabel firstrow lastrow\\'></th>\\n<th class=\\'firstrow lastrow\\'><span class=\\'stratlabel\\'>NA<br><span class=\\'stratn\\'>(N=1)</span></span></th>\\n<th class=\\'firstrow lastrow\\'><span class=\\'stratlabel\\'> <br><span class=\\'stratn\\'>(N=887)</span></span></th>\\n<th class=\\'firstrow lastrow\\'><span class=\\'stratlabel\\'>Amazon<br><span class=\\'stratn\\'>(N=1)</span></span></th>\\n<th class=\\'firstrow lastrow\\'><span class=\\'stratlabel\\'>Amazon mix<br><span class=\\'stratn\\'>(N=2)</span></span></th>\\n<th class=\\'firstrow lastrow\\'><span class=\\'stratlabel\\'>Amazon, ICS<br><span class=\\'stratn\\'>(N=2)</span></span></th>\\n<th class=\\'firstrow lastrow\\'><span class=\\'stratlabel\\'>Beniano<br><span class=\\'stratn\\'>(N=3)</span></span></th>\\n<th class=\\'firstrow lastrow\\'><span class=\\'stratlabel\\'>Blend<br><span class=\\'stratn\\'>(N=41)</span></span></th>\\n<th class=\\'firstrow lastrow\\'><span class=\\'stratlabel\\'>Blend-Forastero,Criollo<br><span class=\\'stratn\\'>(N=1)</span></span></th>\\n<th class=\\'firstrow lastrow\\'><span class=\\'stratlabel\\'>CCN51<br><span class=\\'stratn\\'>(N=1)</span></span></th>\\n<th class=\\'firstrow lastrow\\'><span class=\\'stratlabel\\'>Criollo<br><span class=\\'stratn\\'>(N=153)</span></span></th>\\n<th class=\\'firstrow lastrow\\'><span class=\\'stratlabel\\'>Criollo (Amarru)<br><span class=\\'stratn\\'>(N=2)</span></span></th>\\n<th class=\\'firstrow lastrow\\'><span class=\\'stratlabel\\'>Criollo (Ocumare 61)<br><span class=\\'stratn\\'>(N=2)</span></span></th>\\n<th class=\\'firstrow lastrow\\'><span class=\\'stratlabel\\'>Criollo (Ocumare 67)<br><span class=\\'stratn\\'>(N=1)</span></span></th>\\n<th class=\\'firstrow lastrow\\'><span class=\\'stratlabel\\'>Criollo (Ocumare 77)<br><span class=\\'stratn\\'>(N=1)</span></span></th>\\n<th class=\\'firstrow lastrow\\'><span class=\\'stratlabel\\'>Criollo (Ocumare)<br><span class=\\'stratn\\'>(N=1)</span></span></th>\\n<th class=\\'firstrow lastrow\\'><span class=\\'stratlabel\\'>Criollo (Porcelana)<br><span class=\\'stratn\\'>(N=10)</span></span></th>\\n<th class=\\'firstrow lastrow\\'><span class=\\'stratlabel\\'>Criollo (Wild)<br><span class=\\'stratn\\'>(N=1)</span></span></th>\\n<th class=\\'firstrow lastrow\\'><span class=\\'stratlabel\\'>Criollo, +<br><span class=\\'stratn\\'>(N=1)</span></span></th>\\n<th class=\\'firstrow lastrow\\'><span class=\\'stratlabel\\'>Criollo, Forastero<br><span class=\\'stratn\\'>(N=2)</span></span></th>\\n<th class=\\'firstrow lastrow\\'><span class=\\'stratlabel\\'>Criollo, Trinitario<br><span class=\\'stratn\\'>(N=39)</span></span></th>\\n<th class=\\'firstrow lastrow\\'><span class=\\'stratlabel\\'>EET<br><span class=\\'stratn\\'>(N=3)</span></span></th>\\n<th class=\\'firstrow lastrow\\'><span class=\\'stratlabel\\'>Forastero<br><span class=\\'stratn\\'>(N=87)</span></span></th>\\n<th class=\\'firstrow lastrow\\'><span class=\\'stratlabel\\'>Forastero (Amelonado)<br><span class=\\'stratn\\'>(N=1)</span></span></th>\\n<th class=\\'firstrow lastrow\\'><span class=\\'stratlabel\\'>Forastero (Arriba)<br><span class=\\'stratn\\'>(N=37)</span></span></th>\\n<th class=\\'firstrow lastrow\\'><span class=\\'stratlabel\\'>Forastero (Arriba) ASS<br><span class=\\'stratn\\'>(N=6)</span></span></th>\\n<th class=\\'firstrow lastrow\\'><span class=\\'stratlabel\\'>Forastero (Arriba) ASSS<br><span class=\\'stratn\\'>(N=1)</span></span></th>\\n<th class=\\'firstrow lastrow\\'><span class=\\'stratlabel\\'>Forastero (Catongo)<br><span class=\\'stratn\\'>(N=2)</span></span></th>\\n<th class=\\'firstrow lastrow\\'><span class=\\'stratlabel\\'>Forastero (Nacional)<br><span class=\\'stratn\\'>(N=52)</span></span></th>\\n<th class=\\'firstrow lastrow\\'><span class=\\'stratlabel\\'>Forastero (Parazinho)<br><span class=\\'stratn\\'>(N=8)</span></span></th>\\n<th class=\\'firstrow lastrow\\'><span class=\\'stratlabel\\'>Forastero, Trinitario<br><span class=\\'stratn\\'>(N=1)</span></span></th>\\n<th class=\\'firstrow lastrow\\'><span class=\\'stratlabel\\'>Forastero(Arriba, CCN)<br><span class=\\'stratn\\'>(N=1)</span></span></th>\\n<th class=\\'firstrow lastrow\\'><span class=\\'stratlabel\\'>Matina<br><span class=\\'stratn\\'>(N=3)</span></span></th>\\n<th class=\\'firstrow lastrow\\'><span class=\\'stratlabel\\'>Nacional<br><span class=\\'stratn\\'>(N=2)</span></span></th>\\n<th class=\\'firstrow lastrow\\'><span class=\\'stratlabel\\'>Nacional (Arriba)<br><span class=\\'stratn\\'>(N=3)</span></span></th>\\n<th class=\\'firstrow lastrow\\'><span class=\\'stratlabel\\'>Trinitario<br><span class=\\'stratn\\'>(N=419)</span></span></th>\\n<th class=\\'firstrow lastrow\\'><span class=\\'stratlabel\\'>Trinitario (85% Criollo)<br><span class=\\'stratn\\'>(N=2)</span></span></th>\\n<th class=\\'firstrow lastrow\\'><span class=\\'stratlabel\\'>Trinitario (Amelonado)<br><span class=\\'stratn\\'>(N=1)</span></span></th>\\n<th class=\\'firstrow lastrow\\'><span class=\\'stratlabel\\'>Trinitario (Scavina)<br><span class=\\'stratn\\'>(N=1)</span></span></th>\\n<th class=\\'firstrow lastrow\\'><span class=\\'stratlabel\\'>Trinitario, Criollo<br><span class=\\'stratn\\'>(N=9)</span></span></th>\\n<th class=\\'firstrow lastrow\\'><span class=\\'stratlabel\\'>Trinitario, Forastero<br><span class=\\'stratn\\'>(N=2)</span></span></th>\\n<th class=\\'firstrow lastrow\\'><span class=\\'stratlabel\\'>Trinitario, Nacional<br><span class=\\'stratn\\'>(N=1)</span></span></th>\\n<th class=\\'firstrow lastrow\\'><span class=\\'stratlabel\\'>Trinitario, TCGA<br><span class=\\'stratn\\'>(N=1)</span></span></th>\\n<th class=\\'firstrow lastrow\\'><span class=\\'stratlabel\\'>Overall<br><span class=\\'stratn\\'>(N=1795)</span></span></th>\\n</tr>\\n</thead>\\n<tbody>\\n<tr>\\n<td class=\\'rowlabel firstrow\\'>Cocoa.Percent</td>\\n<td class=\\'firstrow\\'></td>\\n<td class=\\'firstrow\\'></td>\\n<td class=\\'firstrow\\'></td>\\n<td class=\\'firstrow\\'></td>\\n<td class=\\'firstrow\\'></td>\\n<td class=\\'firstrow\\'></td>\\n<td class=\\'firstrow\\'></td>\\n<td class=\\'firstrow\\'></td>\\n<td class=\\'firstrow\\'></td>\\n<td class=\\'firstrow\\'></td>\\n<td class=\\'firstrow\\'></td>\\n<td class=\\'firstrow\\'></td>\\n<td class=\\'firstrow\\'></td>\\n<td class=\\'firstrow\\'></td>\\n<td class=\\'firstrow\\'></td>\\n<td class=\\'firstrow\\'></td>\\n<td class=\\'firstrow\\'></td>\\n<td class=\\'firstrow\\'></td>\\n<td class=\\'firstrow\\'></td>\\n<td class=\\'firstrow\\'></td>\\n<td class=\\'firstrow\\'></td>\\n<td class=\\'firstrow\\'></td>\\n<td class=\\'firstrow\\'></td>\\n<td class=\\'firstrow\\'></td>\\n<td class=\\'firstrow\\'></td>\\n<td class=\\'firstrow\\'></td>\\n<td class=\\'firstrow\\'></td>\\n<td class=\\'firstrow\\'></td>\\n<td class=\\'firstrow\\'></td>\\n<td class=\\'firstrow\\'></td>\\n<td class=\\'firstrow\\'></td>\\n<td class=\\'firstrow\\'></td>\\n<td class=\\'firstrow\\'></td>\\n<td class=\\'firstrow\\'></td>\\n<td class=\\'firstrow\\'></td>\\n<td class=\\'firstrow\\'></td>\\n<td class=\\'firstrow\\'></td>\\n<td class=\\'firstrow\\'></td>\\n<td class=\\'firstrow\\'></td>\\n<td class=\\'firstrow\\'></td>\\n<td class=\\'firstrow\\'></td>\\n<td class=\\'firstrow\\'></td>\\n<td class=\\'firstrow\\'></td>\\n</tr>\\n<tr>\\n<td class=\\'rowlabel\\'>Mean (SD)</td>\\n<td>70 (&plusmn; NA)</td>\\n<td>72 (&plusmn; 6.6)</td>\\n<td>70 (&plusmn; NA)</td>\\n<td>74 (&plusmn; 0)</td>\\n<td>68 (&plusmn; 0.71)</td>\\n<td>71 (&plusmn; 3.6)</td>\\n<td>71 (&plusmn; 6.5)</td>\\n<td>70 (&plusmn; NA)</td>\\n<td>65 (&plusmn; NA)</td>\\n<td>72 (&plusmn; 5.8)</td>\\n<td>70 (&plusmn; 0)</td>\\n<td>74 (&plusmn; 2.1)</td>\\n<td>70 (&plusmn; NA)</td>\\n<td>70 (&plusmn; NA)</td>\\n<td>80 (&plusmn; NA)</td>\\n<td>70 (&plusmn; 4.0)</td>\\n<td>68 (&plusmn; NA)</td>\\n<td>65 (&plusmn; NA)</td>\\n<td>77 (&plusmn; 2.1)</td>\\n<td>72 (&plusmn; 4.6)</td>\\n<td>71 (&plusmn; 5.1)</td>\\n<td>72 (&plusmn; 6.6)</td>\\n<td>70 (&plusmn; NA)</td>\\n<td>73 (&plusmn; 12)</td>\\n<td>69 (&plusmn; 10)</td>\\n<td>70 (&plusmn; NA)</td>\\n<td>73 (&plusmn; 3.5)</td>\\n<td>73 (&plusmn; 7.5)</td>\\n<td>69 (&plusmn; 10)</td>\\n<td>70 (&plusmn; NA)</td>\\n<td>75 (&plusmn; NA)</td>\\n<td>74 (&plusmn; 6.9)</td>\\n<td>71 (&plusmn; 1.4)</td>\\n<td>73 (&plusmn; 2.9)</td>\\n<td>72 (&plusmn; 5.3)</td>\\n<td>70 (&plusmn; 0)</td>\\n<td>70 (&plusmn; NA)</td>\\n<td>70 (&plusmn; NA)</td>\\n<td>68 (&plusmn; 3.2)</td>\\n<td>71 (&plusmn; 6.4)</td>\\n<td>70 (&plusmn; NA)</td>\\n<td>72 (&plusmn; NA)</td>\\n<td>72 (&plusmn; 6.4)</td>\\n</tr>\\n<tr>\\n<td class=\\'rowlabel lastrow\\'>Missing</td>\\n<td class=\\'lastrow\\'>0 (0%)</td>\\n<td class=\\'lastrow\\'>15 (1.7%)</td>\\n<td class=\\'lastrow\\'>0 (0%)</td>\\n<td class=\\'lastrow\\'>0 (0%)</td>\\n<td class=\\'lastrow\\'>0 (0%)</td>\\n<td class=\\'lastrow\\'>0 (0%)</td>\\n<td class=\\'lastrow\\'>0 (0%)</td>\\n<td class=\\'lastrow\\'>0 (0%)</td>\\n<td class=\\'lastrow\\'>0 (0%)</td>\\n<td class=\\'lastrow\\'>4 (2.6%)</td>\\n<td class=\\'lastrow\\'>0 (0%)</td>\\n<td class=\\'lastrow\\'>0 (0%)</td>\\n<td class=\\'lastrow\\'>0 (0%)</td>\\n<td class=\\'lastrow\\'>0 (0%)</td>\\n<td class=\\'lastrow\\'>0 (0%)</td>\\n<td class=\\'lastrow\\'>0 (0%)</td>\\n<td class=\\'lastrow\\'>0 (0%)</td>\\n<td class=\\'lastrow\\'>0 (0%)</td>\\n<td class=\\'lastrow\\'>0 (0%)</td>\\n<td class=\\'lastrow\\'>0 (0%)</td>\\n<td class=\\'lastrow\\'>0 (0%)</td>\\n<td class=\\'lastrow\\'>0 (0%)</td>\\n<td class=\\'lastrow\\'>0 (0%)</td>\\n<td class=\\'lastrow\\'>0 (0%)</td>\\n<td class=\\'lastrow\\'>0 (0%)</td>\\n<td class=\\'lastrow\\'>0 (0%)</td>\\n<td class=\\'lastrow\\'>0 (0%)</td>\\n<td class=\\'lastrow\\'>0 (0%)</td>\\n<td class=\\'lastrow\\'>0 (0%)</td>\\n<td class=\\'lastrow\\'>0 (0%)</td>\\n<td class=\\'lastrow\\'>0 (0%)</td>\\n<td class=\\'lastrow\\'>0 (0%)</td>\\n<td class=\\'lastrow\\'>0 (0%)</td>\\n<td class=\\'lastrow\\'>0 (0%)</td>\\n<td class=\\'lastrow\\'>1 (0.2%)</td>\\n<td class=\\'lastrow\\'>0 (0%)</td>\\n<td class=\\'lastrow\\'>0 (0%)</td>\\n<td class=\\'lastrow\\'>0 (0%)</td>\\n<td class=\\'lastrow\\'>0 (0%)</td>\\n<td class=\\'lastrow\\'>0 (0%)</td>\\n<td class=\\'lastrow\\'>0 (0%)</td>\\n<td class=\\'lastrow\\'>0 (0%)</td>\\n<td class=\\'lastrow\\'>20 (1.1%)</td>\\n</tr>\\n<tr>\\n<td class=\\'rowlabel firstrow\\'>choco$Rating</td>\\n<td class=\\'firstrow\\'></td>\\n<td class=\\'firstrow\\'></td>\\n<td class=\\'firstrow\\'></td>\\n<td class=\\'firstrow\\'></td>\\n<td class=\\'firstrow\\'></td>\\n<td class=\\'firstrow\\'></td>\\n<td class=\\'firstrow\\'></td>\\n<td class=\\'firstrow\\'></td>\\n<td class=\\'firstrow\\'></td>\\n<td class=\\'firstrow\\'></td>\\n<td class=\\'firstrow\\'></td>\\n<td class=\\'firstrow\\'></td>\\n<td class=\\'firstrow\\'></td>\\n<td class=\\'firstrow\\'></td>\\n<td class=\\'firstrow\\'></td>\\n<td class=\\'firstrow\\'></td>\\n<td class=\\'firstrow\\'></td>\\n<td class=\\'firstrow\\'></td>\\n<td class=\\'firstrow\\'></td>\\n<td class=\\'firstrow\\'></td>\\n<td class=\\'firstrow\\'></td>\\n<td class=\\'firstrow\\'></td>\\n<td class=\\'firstrow\\'></td>\\n<td class=\\'firstrow\\'></td>\\n<td class=\\'firstrow\\'></td>\\n<td class=\\'firstrow\\'></td>\\n<td class=\\'firstrow\\'></td>\\n<td class=\\'firstrow\\'></td>\\n<td class=\\'firstrow\\'></td>\\n<td class=\\'firstrow\\'></td>\\n<td class=\\'firstrow\\'></td>\\n<td class=\\'firstrow\\'></td>\\n<td class=\\'firstrow\\'></td>\\n<td class=\\'firstrow\\'></td>\\n<td class=\\'firstrow\\'></td>\\n<td class=\\'firstrow\\'></td>\\n<td class=\\'firstrow\\'></td>\\n<td class=\\'firstrow\\'></td>\\n<td class=\\'firstrow\\'></td>\\n<td class=\\'firstrow\\'></td>\\n<td class=\\'firstrow\\'></td>\\n<td class=\\'firstrow\\'></td>\\n<td class=\\'firstrow\\'></td>\\n</tr>\\n<tr>\\n<td class=\\'rowlabel lastrow\\'>Mean (SD)</td>\\n<td class=\\'lastrow\\'>4.0 (&plusmn; NA)</td>\\n<td class=\\'lastrow\\'>3.1 (&plusmn; 0.48)</td>\\n<td class=\\'lastrow\\'>3.3 (&plusmn; NA)</td>\\n<td class=\\'lastrow\\'>3.8 (&plusmn; 0.35)</td>\\n<td class=\\'lastrow\\'>3.6 (&plusmn; 0.18)</td>\\n<td class=\\'lastrow\\'>3.6 (&plusmn; 0.52)</td>\\n<td class=\\'lastrow\\'>3.4 (&plusmn; 0.56)</td>\\n<td class=\\'lastrow\\'>3.8 (&plusmn; NA)</td>\\n<td class=\\'lastrow\\'>3.5 (&plusmn; NA)</td>\\n<td class=\\'lastrow\\'>3.2 (&plusmn; 0.46)</td>\\n<td class=\\'lastrow\\'>3.3 (&plusmn; 0.35)</td>\\n<td class=\\'lastrow\\'>3.3 (&plusmn; 0)</td>\\n<td class=\\'lastrow\\'>4.0 (&plusmn; NA)</td>\\n<td class=\\'lastrow\\'>3.8 (&plusmn; NA)</td>\\n<td class=\\'lastrow\\'>3.0 (&plusmn; NA)</td>\\n<td class=\\'lastrow\\'>3.4 (&plusmn; 0.50)</td>\\n<td class=\\'lastrow\\'>4.0 (&plusmn; NA)</td>\\n<td class=\\'lastrow\\'>3.5 (&plusmn; NA)</td>\\n<td class=\\'lastrow\\'>3.6 (&plusmn; 0.18)</td>\\n<td class=\\'lastrow\\'>3.3 (&plusmn; 0.40)</td>\\n<td class=\\'lastrow\\'>3.6 (&plusmn; 0.14)</td>\\n<td class=\\'lastrow\\'>3.1 (&plusmn; 0.54)</td>\\n<td class=\\'lastrow\\'>3.8 (&plusmn; NA)</td>\\n<td class=\\'lastrow\\'>2.8 (&plusmn; 0.60)</td>\\n<td class=\\'lastrow\\'>2.9 (&plusmn; 0.54)</td>\\n<td class=\\'lastrow\\'>3.5 (&plusmn; NA)</td>\\n<td class=\\'lastrow\\'>3.4 (&plusmn; 0.18)</td>\\n<td class=\\'lastrow\\'>3.3 (&plusmn; 0.44)</td>\\n<td class=\\'lastrow\\'>3.5 (&plusmn; 0.36)</td>\\n<td class=\\'lastrow\\'>3.0 (&plusmn; NA)</td>\\n<td class=\\'lastrow\\'>3.0 (&plusmn; NA)</td>\\n<td class=\\'lastrow\\'>3.4 (&plusmn; 0.38)</td>\\n<td class=\\'lastrow\\'>3.0 (&plusmn; 0.71)</td>\\n<td class=\\'lastrow\\'>3.3 (&plusmn; 0.50)</td>\\n<td class=\\'lastrow\\'>3.2 (&plusmn; 0.42)</td>\\n<td class=\\'lastrow\\'>3.9 (&plusmn; 0.18)</td>\\n<td class=\\'lastrow\\'>3.0 (&plusmn; NA)</td>\\n<td class=\\'lastrow\\'>3.5 (&plusmn; NA)</td>\\n<td class=\\'lastrow\\'>3.0 (&plusmn; 0.52)</td>\\n<td class=\\'lastrow\\'>3.0 (&plusmn; 0)</td>\\n<td class=\\'lastrow\\'>3.8 (&plusmn; NA)</td>\\n<td class=\\'lastrow\\'>3.8 (&plusmn; NA)</td>\\n<td class=\\'lastrow\\'>3.2 (&plusmn; 0.48)</td>\\n</tr>\\n</tbody>\\n</table>\\n'","text/latex":"'\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n\\textbackslash{}n
NA
(N=1)		 
(N=887)		Amazon
(N=1)		Amazon mix
(N=2)		Amazon, ICS
(N=2)		Beniano
(N=3)		Blend
(N=41)		Blend-Forastero,Criollo
(N=1)		CCN51
(N=1)		Criollo
(N=153)		Criollo (Amarru)
(N=2)		Criollo (Ocumare 61)
(N=2)		Criollo (Ocumare 67)
(N=1)		Criollo (Ocumare 77)
(N=1)		Criollo (Ocumare)
(N=1)		Criollo (Porcelana)
(N=10)		Criollo (Wild)
(N=1)		Criollo, +
(N=1)		Criollo, Forastero
(N=2)		Criollo, Trinitario
(N=39)		EET
(N=3)		Forastero
(N=87)		Forastero (Amelonado)
(N=1)		Forastero (Arriba)
(N=37)		Forastero (Arriba) ASS
(N=6)		Forastero (Arriba) ASSS
(N=1)		Forastero (Catongo)
(N=2)		Forastero (Nacional)
(N=52)		Forastero (Parazinho)
(N=8)		Forastero, Trinitario
(N=1)		Forastero(Arriba, CCN)
(N=1)		Matina
(N=3)		Nacional
(N=2)		Nacional (Arriba)
(N=3)		Trinitario
(N=419)		Trinitario (85\\% Criollo)
(N=2)		Trinitario (Amelonado)
(N=1)		Trinitario (Scavina)
(N=1)		Trinitario, Criollo
(N=9)		Trinitario, Forastero
(N=2)		Trinitario, Nacional
(N=1)		Trinitario, TCGA
(N=1)		Overall
(N=1795)
Cocoa.Percent
Mean (SD)70 (\\± NA)72 (\\± 6.6)70 (\\± NA)74 (\\± 0)68 (\\± 0.71)71 (\\± 3.6)71 (\\± 6.5)70 (\\± NA)65 (\\± NA)72 (\\± 5.8)70 (\\± 0)74 (\\± 2.1)70 (\\± NA)70 (\\± NA)80 (\\± NA)70 (\\± 4.0)68 (\\± NA)65 (\\± NA)77 (\\± 2.1)72 (\\± 4.6)71 (\\± 5.1)72 (\\± 6.6)70 (\\± NA)73 (\\± 12)69 (\\± 10)70 (\\± NA)73 (\\± 3.5)73 (\\± 7.5)69 (\\± 10)70 (\\± NA)75 (\\± NA)74 (\\± 6.9)71 (\\± 1.4)73 (\\± 2.9)72 (\\± 5.3)70 (\\± 0)70 (\\± NA)70 (\\± NA)68 (\\± 3.2)71 (\\± 6.4)70 (\\± NA)72 (\\± NA)72 (\\± 6.4)
Missing0 (0\\%)15 (1.7\\%)0 (0\\%)0 (0\\%)0 (0\\%)0 (0\\%)0 (0\\%)0 (0\\%)0 (0\\%)4 (2.6\\%)0 (0\\%)0 (0\\%)0 (0\\%)0 (0\\%)0 (0\\%)0 (0\\%)0 (0\\%)0 (0\\%)0 (0\\%)0 (0\\%)0 (0\\%)0 (0\\%)0 (0\\%)0 (0\\%)0 (0\\%)0 (0\\%)0 (0\\%)0 (0\\%)0 (0\\%)0 (0\\%)0 (0\\%)0 (0\\%)0 (0\\%)0 (0\\%)1 (0.2\\%)0 (0\\%)0 (0\\%)0 (0\\%)0 (0\\%)0 (0\\%)0 (0\\%)0 (0\\%)20 (1.1\\%)
choco\\$Rating
Mean (SD)4.0 (\\± NA)3.1 (\\± 0.48)3.3 (\\± NA)3.8 (\\± 0.35)3.6 (\\± 0.18)3.6 (\\± 0.52)3.4 (\\± 0.56)3.8 (\\± NA)3.5 (\\± NA)3.2 (\\± 0.46)3.3 (\\± 0.35)3.3 (\\± 0)4.0 (\\± NA)3.8 (\\± NA)3.0 (\\± NA)3.4 (\\± 0.50)4.0 (\\± NA)3.5 (\\± NA)3.6 (\\± 0.18)3.3 (\\± 0.40)3.6 (\\± 0.14)3.1 (\\± 0.54)3.8 (\\± NA)2.8 (\\± 0.60)2.9 (\\± 0.54)3.5 (\\± NA)3.4 (\\± 0.18)3.3 (\\± 0.44)3.5 (\\± 0.36)3.0 (\\± NA)3.0 (\\± NA)3.4 (\\± 0.38)3.0 (\\± 0.71)3.3 (\\± 0.50)3.2 (\\± 0.42)3.9 (\\± 0.18)3.0 (\\± NA)3.5 (\\± NA)3.0 (\\± 0.52)3.0 (\\± 0)3.8 (\\± NA)3.8 (\\± NA)3.2 (\\± 0.48)
\\textbackslash{}n'","text/plain":["\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","
NA
(N=1)		 
(N=887)		Amazon
(N=1)		Amazon mix
(N=2)		Amazon, ICS
(N=2)		Beniano
(N=3)		Blend
(N=41)		Blend-Forastero,Criollo
(N=1)		CCN51
(N=1)		Criollo
(N=153)		Criollo (Amarru)
(N=2)		Criollo (Ocumare 61)
(N=2)		Criollo (Ocumare 67)
(N=1)		Criollo (Ocumare 77)
(N=1)		Criollo (Ocumare)
(N=1)		Criollo (Porcelana)
(N=10)		Criollo (Wild)
(N=1)		Criollo, +
(N=1)		Criollo, Forastero
(N=2)		Criollo, Trinitario
(N=39)		EET
(N=3)		Forastero
(N=87)		Forastero (Amelonado)
(N=1)		Forastero (Arriba)
(N=37)		Forastero (Arriba) ASS
(N=6)		Forastero (Arriba) ASSS
(N=1)		Forastero (Catongo)
(N=2)		Forastero (Nacional)
(N=52)		Forastero (Parazinho)
(N=8)		Forastero, Trinitario
(N=1)		Forastero(Arriba, CCN)
(N=1)		Matina
(N=3)		Nacional
(N=2)		Nacional (Arriba)
(N=3)		Trinitario
(N=419)		Trinitario (85% Criollo)
(N=2)		Trinitario (Amelonado)
(N=1)		Trinitario (Scavina)
(N=1)		Trinitario, Criollo
(N=9)		Trinitario, Forastero
(N=2)		Trinitario, Nacional
(N=1)		Trinitario, TCGA
(N=1)		Overall
(N=1795)
Cocoa.Percent
Mean (SD)70 (± NA)72 (± 6.6)70 (± NA)74 (± 0)68 (± 0.71)71 (± 3.6)71 (± 6.5)70 (± NA)65 (± NA)72 (± 5.8)70 (± 0)74 (± 2.1)70 (± NA)70 (± NA)80 (± NA)70 (± 4.0)68 (± NA)65 (± NA)77 (± 2.1)72 (± 4.6)71 (± 5.1)72 (± 6.6)70 (± NA)73 (± 12)69 (± 10)70 (± NA)73 (± 3.5)73 (± 7.5)69 (± 10)70 (± NA)75 (± NA)74 (± 6.9)71 (± 1.4)73 (± 2.9)72 (± 5.3)70 (± 0)70 (± NA)70 (± NA)68 (± 3.2)71 (± 6.4)70 (± NA)72 (± NA)72 (± 6.4)
Missing0 (0%)15 (1.7%)0 (0%)0 (0%)0 (0%)0 (0%)0 (0%)0 (0%)0 (0%)4 (2.6%)0 (0%)0 (0%)0 (0%)0 (0%)0 (0%)0 (0%)0 (0%)0 (0%)0 (0%)0 (0%)0 (0%)0 (0%)0 (0%)0 (0%)0 (0%)0 (0%)0 (0%)0 (0%)0 (0%)0 (0%)0 (0%)0 (0%)0 (0%)0 (0%)1 (0.2%)0 (0%)0 (0%)0 (0%)0 (0%)0 (0%)0 (0%)0 (0%)20 (1.1%)
choco$Rating
Mean (SD)4.0 (± NA)3.1 (± 0.48)3.3 (± NA)3.8 (± 0.35)3.6 (± 0.18)3.6 (± 0.52)3.4 (± 0.56)3.8 (± NA)3.5 (± NA)3.2 (± 0.46)3.3 (± 0.35)3.3 (± 0)4.0 (± NA)3.8 (± NA)3.0 (± NA)3.4 (± 0.50)4.0 (± NA)3.5 (± NA)3.6 (± 0.18)3.3 (± 0.40)3.6 (± 0.14)3.1 (± 0.54)3.8 (± NA)2.8 (± 0.60)2.9 (± 0.54)3.5 (± NA)3.4 (± 0.18)3.3 (± 0.44)3.5 (± 0.36)3.0 (± NA)3.0 (± NA)3.4 (± 0.38)3.0 (± 0.71)3.3 (± 0.50)3.2 (± 0.42)3.9 (± 0.18)3.0 (± NA)3.5 (± NA)3.0 (± 0.52)3.0 (± 0)3.8 (± NA)3.8 (± NA)3.2 (± 0.48)
"]},"metadata":{}}]},{"cell_type":"markdown","metadata":{"id":"30tWjtbumRf5"},"source":["# Bivariate descriptive analysis\n","\n","Now lets to relate: choco$Bean.Type by choco$Rating (categorized in 3 types using perc_rate)"]},{"cell_type":"code","execution_count":51,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":52},"executionInfo":{"elapsed":256,"status":"ok","timestamp":1716799687905,"user":{"displayName":"toni Monleon","userId":"05269440626861479105"},"user_tz":-120},"id":"wm6YxXhQa5AD","outputId":"a102ee04-b2cd-4261-8346-b2122492b97d"},"outputs":[{"output_type":"display_data","data":{"text/plain":[" Min. 1st Qu. Median Mean 3rd Qu. Max. \n"," 1.000 2.875 3.250 3.186 3.500 5.000 "]},"metadata":{}}],"source":["summary(choco$Rating)\n"]},{"cell_type":"code","execution_count":55,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":607},"executionInfo":{"elapsed":262,"status":"ok","timestamp":1716799771681,"user":{"displayName":"toni Monleon","userId":"05269440626861479105"},"user_tz":-120},"id":"S5Y4lwj2m8HK","outputId":"e326c53a-1774-43b8-9df3-11cfc737d0bb"},"outputs":[{"output_type":"display_data","data":{"text/html":["\n","
  1. [3.5,5.0]
  2. [1.0,3.0)
  3. [3.0,3.5)
  4. [3.5,5.0]
  5. [3.5,5.0]
  6. [1.0,3.0)
  7. [3.5,5.0]
  8. [3.5,5.0]
  9. [3.5,5.0]
  10. [3.5,5.0]
  11. [1.0,3.0)
  12. [3.0,3.5)
  13. [3.0,3.5)
  14. [3.5,5.0]
  15. [1.0,3.0)
  16. [3.0,3.5)
  17. [3.0,3.5)
  18. [3.5,5.0]
  19. [3.0,3.5)
  20. [3.5,5.0]
  21. [3.5,5.0]
  22. [3.5,5.0]
  23. [3.5,5.0]
  24. [3.5,5.0]
  25. [3.5,5.0]
  26. [1.0,3.0)
  27. [3.0,3.5)
  28. [3.5,5.0]
  29. [3.5,5.0]
  30. [1.0,3.0)
  31. [3.0,3.5)
  32. [3.0,3.5)
  33. [3.5,5.0]
  34. [1.0,3.0)
  35. [1.0,3.0)
  36. [1.0,3.0)
  37. [1.0,3.0)
  38. [3.0,3.5)
  39. [1.0,3.0)
  40. [1.0,3.0)
  41. [3.5,5.0]
  42. [3.5,5.0]
  43. [3.5,5.0]
  44. [3.5,5.0]
  45. [1.0,3.0)
  46. [3.0,3.5)
  47. [1.0,3.0)
  48. [1.0,3.0)
  49. [1.0,3.0)
  50. [1.0,3.0)
  51. [3.0,3.5)
  52. [3.0,3.5)
  53. [3.0,3.5)
  54. [3.0,3.5)
  55. [3.5,5.0]
  56. [3.5,5.0]
  57. [3.5,5.0]
  58. [3.0,3.5)
  59. [3.0,3.5)
  60. [1.0,3.0)
  61. [3.5,5.0]
  62. [3.0,3.5)
  63. [3.5,5.0]
  64. [3.0,3.5)
  65. [1.0,3.0)
  66. [1.0,3.0)
  67. [3.0,3.5)
  68. [3.0,3.5)
  69. [3.5,5.0]
  70. [3.0,3.5)
  71. [3.0,3.5)
  72. [3.0,3.5)
  73. [3.0,3.5)
  74. [3.0,3.5)
  75. [3.5,5.0]
  76. [3.5,5.0]
  77. [3.5,5.0]
  78. [3.5,5.0]
  79. [3.5,5.0]
  80. [3.0,3.5)
  81. [3.0,3.5)
  82. [3.5,5.0]
  83. [3.5,5.0]
  84. [3.5,5.0]
  85. [3.5,5.0]
  86. [3.5,5.0]
  87. [3.5,5.0]
  88. [3.5,5.0]
  89. [3.0,3.5)
  90. [1.0,3.0)
  91. [3.5,5.0]
  92. [3.5,5.0]
  93. [3.5,5.0]
  94. [3.0,3.5)
  95. [3.5,5.0]
  96. [3.0,3.5)
  97. [1.0,3.0)
  98. [1.0,3.0)
  99. [3.0,3.5)
  100. [3.5,5.0]
  101. [3.5,5.0]
  102. [3.5,5.0]
  103. [3.0,3.5)
  104. [3.0,3.5)
  105. [3.5,5.0]
  106. [3.5,5.0]
  107. [3.5,5.0]
  108. [3.5,5.0]
  109. [3.5,5.0]
  110. [3.5,5.0]
  111. [3.0,3.5)
  112. [3.0,3.5)
  113. [3.5,5.0]
  114. [3.5,5.0]
  115. [3.5,5.0]
  116. [3.5,5.0]
  117. [3.5,5.0]
  118. [3.5,5.0]
  119. [1.0,3.0)
  120. [3.0,3.5)
  121. [3.5,5.0]
  122. [3.0,3.5)
  123. [3.5,5.0]
  124. [3.5,5.0]
  125. [1.0,3.0)
  126. [1.0,3.0)
  127. [3.5,5.0]
  128. [3.0,3.5)
  129. [3.0,3.5)
  130. [3.5,5.0]
  131. [1.0,3.0)
  132. [1.0,3.0)
  133. [1.0,3.0)
  134. [3.0,3.5)
  135. [3.0,3.5)
  136. [3.0,3.5)
  137. [3.5,5.0]
  138. [1.0,3.0)
  139. [3.5,5.0]
  140. [3.5,5.0]
  141. [3.5,5.0]
  142. [3.5,5.0]
  143. [1.0,3.0)
  144. [3.0,3.5)
  145. [3.0,3.5)
  146. [3.5,5.0]
  147. [3.0,3.5)
  148. [1.0,3.0)
  149. [1.0,3.0)
  150. [1.0,3.0)
  151. [3.5,5.0]
  152. [3.5,5.0]
  153. [3.5,5.0]
  154. [3.0,3.5)
  155. [3.5,5.0]
  156. [3.5,5.0]
  157. [1.0,3.0)
  158. [3.5,5.0]
  159. [3.5,5.0]
  160. [3.0,3.5)
  161. [3.0,3.5)
  162. [3.0,3.5)
  163. [1.0,3.0)
  164. [1.0,3.0)
  165. [1.0,3.0)
  166. [1.0,3.0)
  167. [1.0,3.0)
  168. [1.0,3.0)
  169. [1.0,3.0)
  170. [3.5,5.0]
  171. [3.5,5.0]
  172. [3.0,3.5)
  173. [3.5,5.0]
  174. [1.0,3.0)
  175. [3.0,3.5)
  176. [3.5,5.0]
  177. [3.5,5.0]
  178. [3.5,5.0]
  179. [3.5,5.0]
  180. [3.5,5.0]
  181. [3.5,5.0]
  182. [3.5,5.0]
  183. [1.0,3.0)
  184. [3.0,3.5)
  185. [3.0,3.5)
  186. [3.5,5.0]
  187. [3.5,5.0]
  188. [3.0,3.5)
  189. [3.0,3.5)
  190. [3.5,5.0]
  191. [3.5,5.0]
  192. [1.0,3.0)
  193. [1.0,3.0)
  194. [1.0,3.0)
  195. [3.0,3.5)
  196. [3.5,5.0]
  197. [3.0,3.5)
  198. [3.5,5.0]
  199. [1.0,3.0)
  200. [3.0,3.5)
  201. ⋯
  202. [3.0,3.5)
  203. [3.0,3.5)
  204. [3.5,5.0]
  205. [3.0,3.5)
  206. [3.5,5.0]
  207. [3.5,5.0]
  208. [3.5,5.0]
  209. [3.5,5.0]
  210. [3.5,5.0]
  211. [3.0,3.5)
  212. [3.0,3.5)
  213. [3.0,3.5)
  214. [3.5,5.0]
  215. [3.0,3.5)
  216. [1.0,3.0)
  217. [1.0,3.0)
  218. [1.0,3.0)
  219. [3.0,3.5)
  220. [3.0,3.5)
  221. [3.0,3.5)
  222. [3.0,3.5)
  223. [1.0,3.0)
  224. [3.0,3.5)
  225. [3.0,3.5)
  226. [3.0,3.5)
  227. [1.0,3.0)
  228. [3.0,3.5)
  229. [1.0,3.0)
  230. [3.0,3.5)
  231. [1.0,3.0)
  232. [3.0,3.5)
  233. [1.0,3.0)
  234. [1.0,3.0)
  235. [1.0,3.0)
  236. [1.0,3.0)
  237. [1.0,3.0)
  238. [3.0,3.5)
  239. [3.0,3.5)
  240. [3.5,5.0]
  241. [3.0,3.5)
  242. [1.0,3.0)
  243. [3.0,3.5)
  244. [1.0,3.0)
  245. [3.0,3.5)
  246. [3.0,3.5)
  247. [1.0,3.0)
  248. [3.0,3.5)
  249. [3.0,3.5)
  250. [3.5,5.0]
  251. [1.0,3.0)
  252. [3.5,5.0]
  253. [3.5,5.0]
  254. [3.0,3.5)
  255. [3.5,5.0]
  256. [3.5,5.0]
  257. [3.0,3.5)
  258. [3.5,5.0]
  259. [3.5,5.0]
  260. [3.0,3.5)
  261. [3.0,3.5)
  262. [3.5,5.0]
  263. [1.0,3.0)
  264. [3.0,3.5)
  265. [1.0,3.0)
  266. [3.0,3.5)
  267. [3.0,3.5)
  268. [3.5,5.0]
  269. [3.0,3.5)
  270. [3.5,5.0]
  271. [3.0,3.5)
  272. [1.0,3.0)
  273. [3.0,3.5)
  274. [3.0,3.5)
  275. [3.5,5.0]
  276. [1.0,3.0)
  277. [3.5,5.0]
  278. [3.5,5.0]
  279. [3.0,3.5)
  280. [1.0,3.0)
  281. [1.0,3.0)
  282. [3.0,3.5)
  283. [3.5,5.0]
  284. [1.0,3.0)
  285. [3.0,3.5)
  286. [3.0,3.5)
  287. [3.0,3.5)
  288. [3.0,3.5)
  289. [3.5,5.0]
  290. [3.5,5.0]
  291. [3.5,5.0]
  292. [3.5,5.0]
  293. [3.0,3.5)
  294. [3.5,5.0]
  295. [3.5,5.0]
  296. [3.5,5.0]
  297. [3.5,5.0]
  298. [3.0,3.5)
  299. [3.5,5.0]
  300. [3.5,5.0]
  301. [1.0,3.0)
  302. [1.0,3.0)
  303. [1.0,3.0)
  304. [3.0,3.5)
  305. [3.0,3.5)
  306. [3.5,5.0]
  307. [1.0,3.0)
  308. [3.0,3.5)
  309. [3.5,5.0]
  310. [1.0,3.0)
  311. [1.0,3.0)
  312. [1.0,3.0)
  313. [1.0,3.0)
  314. [1.0,3.0)
  315. [3.0,3.5)
  316. [3.0,3.5)
  317. [3.0,3.5)
  318. [3.0,3.5)
  319. [3.0,3.5)
  320. [3.0,3.5)
  321. [3.5,5.0]
  322. [3.5,5.0]
  323. [3.5,5.0]
  324. [3.0,3.5)
  325. [3.5,5.0]
  326. [3.0,3.5)
  327. [1.0,3.0)
  328. [1.0,3.0)
  329. [3.0,3.5)
  330. [3.0,3.5)
  331. [1.0,3.0)
  332. [3.0,3.5)
  333. [3.0,3.5)
  334. [3.0,3.5)
  335. [1.0,3.0)
  336. [1.0,3.0)
  337. [1.0,3.0)
  338. [3.0,3.5)
  339. [3.5,5.0]
  340. [3.5,5.0]
  341. [1.0,3.0)
  342. [1.0,3.0)
  343. [1.0,3.0)
  344. [3.0,3.5)
  345. [1.0,3.0)
  346. [3.5,5.0]
  347. [3.0,3.5)
  348. [1.0,3.0)
  349. [3.0,3.5)
  350. [3.5,5.0]
  351. [3.5,5.0]
  352. [3.0,3.5)
  353. [3.0,3.5)
  354. [3.0,3.5)
  355. [3.5,5.0]
  356. [3.5,5.0]
  357. [3.0,3.5)
  358. [3.0,3.5)
  359. [3.5,5.0]
  360. [1.0,3.0)
  361. [3.0,3.5)
  362. [3.0,3.5)
  363. [3.5,5.0]
  364. [3.5,5.0]
  365. [3.0,3.5)
  366. [1.0,3.0)
  367. [1.0,3.0)
  368. [3.0,3.5)
  369. [3.0,3.5)
  370. [3.0,3.5)
  371. [3.0,3.5)
  372. [3.5,5.0]
  373. [3.0,3.5)
  374. [3.5,5.0]
  375. [1.0,3.0)
  376. [1.0,3.0)
  377. [3.0,3.5)
  378. [3.5,5.0]
  379. [3.5,5.0]
  380. [3.5,5.0]
  381. [3.0,3.5)
  382. [3.5,5.0]
  383. [3.5,5.0]
  384. [3.5,5.0]
  385. [1.0,3.0)
  386. [3.5,5.0]
  387. [3.5,5.0]
  388. [3.0,3.5)
  389. [3.5,5.0]
  390. [3.5,5.0]
  391. [3.0,3.5)
  392. [3.0,3.5)
  393. [3.0,3.5)
  394. [3.5,5.0]
  395. [3.0,3.5)
  396. [3.5,5.0]
  397. [3.5,5.0]
  398. [3.0,3.5)
  399. [3.5,5.0]
  400. [3.0,3.5)
  401. [3.0,3.5)
\n","\n","
\n","\t\n","\t\tLevels:\n","\t\n","\t\n","\t
  1. '[1.0,3.0)'
  2. '[3.0,3.5)'
  3. '[3.5,5.0]'
\n","
"],"text/markdown":"1. [3.5,5.0]\n2. [1.0,3.0)\n3. [3.0,3.5)\n4. [3.5,5.0]\n5. [3.5,5.0]\n6. [1.0,3.0)\n7. [3.5,5.0]\n8. [3.5,5.0]\n9. [3.5,5.0]\n10. [3.5,5.0]\n11. [1.0,3.0)\n12. [3.0,3.5)\n13. [3.0,3.5)\n14. [3.5,5.0]\n15. [1.0,3.0)\n16. [3.0,3.5)\n17. [3.0,3.5)\n18. [3.5,5.0]\n19. [3.0,3.5)\n20. [3.5,5.0]\n21. [3.5,5.0]\n22. [3.5,5.0]\n23. [3.5,5.0]\n24. [3.5,5.0]\n25. [3.5,5.0]\n26. [1.0,3.0)\n27. [3.0,3.5)\n28. [3.5,5.0]\n29. [3.5,5.0]\n30. [1.0,3.0)\n31. [3.0,3.5)\n32. [3.0,3.5)\n33. [3.5,5.0]\n34. [1.0,3.0)\n35. [1.0,3.0)\n36. [1.0,3.0)\n37. [1.0,3.0)\n38. [3.0,3.5)\n39. [1.0,3.0)\n40. [1.0,3.0)\n41. [3.5,5.0]\n42. [3.5,5.0]\n43. [3.5,5.0]\n44. [3.5,5.0]\n45. [1.0,3.0)\n46. [3.0,3.5)\n47. [1.0,3.0)\n48. [1.0,3.0)\n49. [1.0,3.0)\n50. [1.0,3.0)\n51. [3.0,3.5)\n52. [3.0,3.5)\n53. [3.0,3.5)\n54. [3.0,3.5)\n55. [3.5,5.0]\n56. [3.5,5.0]\n57. [3.5,5.0]\n58. [3.0,3.5)\n59. [3.0,3.5)\n60. [1.0,3.0)\n61. [3.5,5.0]\n62. [3.0,3.5)\n63. [3.5,5.0]\n64. [3.0,3.5)\n65. [1.0,3.0)\n66. [1.0,3.0)\n67. [3.0,3.5)\n68. [3.0,3.5)\n69. [3.5,5.0]\n70. [3.0,3.5)\n71. [3.0,3.5)\n72. [3.0,3.5)\n73. [3.0,3.5)\n74. [3.0,3.5)\n75. [3.5,5.0]\n76. [3.5,5.0]\n77. [3.5,5.0]\n78. [3.5,5.0]\n79. [3.5,5.0]\n80. [3.0,3.5)\n81. [3.0,3.5)\n82. [3.5,5.0]\n83. [3.5,5.0]\n84. [3.5,5.0]\n85. [3.5,5.0]\n86. [3.5,5.0]\n87. [3.5,5.0]\n88. [3.5,5.0]\n89. [3.0,3.5)\n90. [1.0,3.0)\n91. [3.5,5.0]\n92. [3.5,5.0]\n93. [3.5,5.0]\n94. [3.0,3.5)\n95. [3.5,5.0]\n96. [3.0,3.5)\n97. [1.0,3.0)\n98. [1.0,3.0)\n99. [3.0,3.5)\n100. [3.5,5.0]\n101. [3.5,5.0]\n102. [3.5,5.0]\n103. [3.0,3.5)\n104. [3.0,3.5)\n105. [3.5,5.0]\n106. [3.5,5.0]\n107. [3.5,5.0]\n108. [3.5,5.0]\n109. [3.5,5.0]\n110. [3.5,5.0]\n111. [3.0,3.5)\n112. [3.0,3.5)\n113. [3.5,5.0]\n114. [3.5,5.0]\n115. [3.5,5.0]\n116. [3.5,5.0]\n117. [3.5,5.0]\n118. [3.5,5.0]\n119. [1.0,3.0)\n120. [3.0,3.5)\n121. [3.5,5.0]\n122. [3.0,3.5)\n123. [3.5,5.0]\n124. [3.5,5.0]\n125. [1.0,3.0)\n126. [1.0,3.0)\n127. [3.5,5.0]\n128. [3.0,3.5)\n129. [3.0,3.5)\n130. [3.5,5.0]\n131. [1.0,3.0)\n132. [1.0,3.0)\n133. [1.0,3.0)\n134. [3.0,3.5)\n135. [3.0,3.5)\n136. [3.0,3.5)\n137. [3.5,5.0]\n138. [1.0,3.0)\n139. [3.5,5.0]\n140. [3.5,5.0]\n141. [3.5,5.0]\n142. [3.5,5.0]\n143. [1.0,3.0)\n144. [3.0,3.5)\n145. [3.0,3.5)\n146. [3.5,5.0]\n147. [3.0,3.5)\n148. [1.0,3.0)\n149. [1.0,3.0)\n150. [1.0,3.0)\n151. [3.5,5.0]\n152. [3.5,5.0]\n153. [3.5,5.0]\n154. [3.0,3.5)\n155. [3.5,5.0]\n156. [3.5,5.0]\n157. [1.0,3.0)\n158. [3.5,5.0]\n159. [3.5,5.0]\n160. [3.0,3.5)\n161. [3.0,3.5)\n162. [3.0,3.5)\n163. [1.0,3.0)\n164. [1.0,3.0)\n165. [1.0,3.0)\n166. [1.0,3.0)\n167. [1.0,3.0)\n168. [1.0,3.0)\n169. [1.0,3.0)\n170. [3.5,5.0]\n171. [3.5,5.0]\n172. [3.0,3.5)\n173. [3.5,5.0]\n174. [1.0,3.0)\n175. [3.0,3.5)\n176. [3.5,5.0]\n177. [3.5,5.0]\n178. [3.5,5.0]\n179. [3.5,5.0]\n180. [3.5,5.0]\n181. [3.5,5.0]\n182. [3.5,5.0]\n183. [1.0,3.0)\n184. [3.0,3.5)\n185. [3.0,3.5)\n186. [3.5,5.0]\n187. [3.5,5.0]\n188. [3.0,3.5)\n189. [3.0,3.5)\n190. [3.5,5.0]\n191. [3.5,5.0]\n192. [1.0,3.0)\n193. [1.0,3.0)\n194. [1.0,3.0)\n195. [3.0,3.5)\n196. [3.5,5.0]\n197. [3.0,3.5)\n198. [3.5,5.0]\n199. [1.0,3.0)\n200. [3.0,3.5)\n201. ⋯\n202. [3.0,3.5)\n203. [3.0,3.5)\n204. [3.5,5.0]\n205. [3.0,3.5)\n206. [3.5,5.0]\n207. [3.5,5.0]\n208. [3.5,5.0]\n209. [3.5,5.0]\n210. [3.5,5.0]\n211. [3.0,3.5)\n212. [3.0,3.5)\n213. [3.0,3.5)\n214. [3.5,5.0]\n215. [3.0,3.5)\n216. [1.0,3.0)\n217. [1.0,3.0)\n218. [1.0,3.0)\n219. [3.0,3.5)\n220. [3.0,3.5)\n221. [3.0,3.5)\n222. [3.0,3.5)\n223. [1.0,3.0)\n224. [3.0,3.5)\n225. [3.0,3.5)\n226. [3.0,3.5)\n227. [1.0,3.0)\n228. [3.0,3.5)\n229. [1.0,3.0)\n230. [3.0,3.5)\n231. [1.0,3.0)\n232. [3.0,3.5)\n233. [1.0,3.0)\n234. [1.0,3.0)\n235. [1.0,3.0)\n236. [1.0,3.0)\n237. [1.0,3.0)\n238. [3.0,3.5)\n239. [3.0,3.5)\n240. [3.5,5.0]\n241. [3.0,3.5)\n242. [1.0,3.0)\n243. [3.0,3.5)\n244. [1.0,3.0)\n245. [3.0,3.5)\n246. [3.0,3.5)\n247. [1.0,3.0)\n248. [3.0,3.5)\n249. [3.0,3.5)\n250. [3.5,5.0]\n251. [1.0,3.0)\n252. [3.5,5.0]\n253. [3.5,5.0]\n254. [3.0,3.5)\n255. [3.5,5.0]\n256. [3.5,5.0]\n257. [3.0,3.5)\n258. [3.5,5.0]\n259. [3.5,5.0]\n260. [3.0,3.5)\n261. [3.0,3.5)\n262. [3.5,5.0]\n263. [1.0,3.0)\n264. [3.0,3.5)\n265. [1.0,3.0)\n266. [3.0,3.5)\n267. [3.0,3.5)\n268. [3.5,5.0]\n269. [3.0,3.5)\n270. [3.5,5.0]\n271. [3.0,3.5)\n272. [1.0,3.0)\n273. [3.0,3.5)\n274. [3.0,3.5)\n275. [3.5,5.0]\n276. [1.0,3.0)\n277. [3.5,5.0]\n278. [3.5,5.0]\n279. [3.0,3.5)\n280. [1.0,3.0)\n281. [1.0,3.0)\n282. [3.0,3.5)\n283. [3.5,5.0]\n284. [1.0,3.0)\n285. [3.0,3.5)\n286. [3.0,3.5)\n287. [3.0,3.5)\n288. [3.0,3.5)\n289. [3.5,5.0]\n290. [3.5,5.0]\n291. [3.5,5.0]\n292. [3.5,5.0]\n293. [3.0,3.5)\n294. [3.5,5.0]\n295. [3.5,5.0]\n296. [3.5,5.0]\n297. [3.5,5.0]\n298. [3.0,3.5)\n299. [3.5,5.0]\n300. [3.5,5.0]\n301. [1.0,3.0)\n302. [1.0,3.0)\n303. [1.0,3.0)\n304. [3.0,3.5)\n305. [3.0,3.5)\n306. [3.5,5.0]\n307. [1.0,3.0)\n308. [3.0,3.5)\n309. [3.5,5.0]\n310. [1.0,3.0)\n311. [1.0,3.0)\n312. [1.0,3.0)\n313. [1.0,3.0)\n314. [1.0,3.0)\n315. [3.0,3.5)\n316. [3.0,3.5)\n317. [3.0,3.5)\n318. [3.0,3.5)\n319. [3.0,3.5)\n320. [3.0,3.5)\n321. [3.5,5.0]\n322. [3.5,5.0]\n323. [3.5,5.0]\n324. [3.0,3.5)\n325. [3.5,5.0]\n326. [3.0,3.5)\n327. [1.0,3.0)\n328. [1.0,3.0)\n329. [3.0,3.5)\n330. [3.0,3.5)\n331. [1.0,3.0)\n332. [3.0,3.5)\n333. [3.0,3.5)\n334. [3.0,3.5)\n335. [1.0,3.0)\n336. [1.0,3.0)\n337. [1.0,3.0)\n338. [3.0,3.5)\n339. [3.5,5.0]\n340. [3.5,5.0]\n341. [1.0,3.0)\n342. [1.0,3.0)\n343. [1.0,3.0)\n344. [3.0,3.5)\n345. [1.0,3.0)\n346. [3.5,5.0]\n347. [3.0,3.5)\n348. [1.0,3.0)\n349. [3.0,3.5)\n350. [3.5,5.0]\n351. [3.5,5.0]\n352. [3.0,3.5)\n353. [3.0,3.5)\n354. [3.0,3.5)\n355. [3.5,5.0]\n356. [3.5,5.0]\n357. [3.0,3.5)\n358. [3.0,3.5)\n359. [3.5,5.0]\n360. [1.0,3.0)\n361. [3.0,3.5)\n362. [3.0,3.5)\n363. [3.5,5.0]\n364. [3.5,5.0]\n365. [3.0,3.5)\n366. [1.0,3.0)\n367. [1.0,3.0)\n368. [3.0,3.5)\n369. [3.0,3.5)\n370. [3.0,3.5)\n371. [3.0,3.5)\n372. [3.5,5.0]\n373. [3.0,3.5)\n374. [3.5,5.0]\n375. [1.0,3.0)\n376. [1.0,3.0)\n377. [3.0,3.5)\n378. [3.5,5.0]\n379. [3.5,5.0]\n380. [3.5,5.0]\n381. [3.0,3.5)\n382. [3.5,5.0]\n383. [3.5,5.0]\n384. [3.5,5.0]\n385. [1.0,3.0)\n386. [3.5,5.0]\n387. [3.5,5.0]\n388. [3.0,3.5)\n389. [3.5,5.0]\n390. [3.5,5.0]\n391. [3.0,3.5)\n392. [3.0,3.5)\n393. [3.0,3.5)\n394. [3.5,5.0]\n395. [3.0,3.5)\n396. [3.5,5.0]\n397. [3.5,5.0]\n398. [3.0,3.5)\n399. [3.5,5.0]\n400. [3.0,3.5)\n401. [3.0,3.5)\n\n\n\n**Levels**: 1. '[1.0,3.0)'\n2. '[3.0,3.5)'\n3. '[3.5,5.0]'\n\n\n","text/latex":"\\begin{enumerate*}\n\\item {[}3.5,5.0{]}\n\\item {[}1.0,3.0)\n\\item {[}3.0,3.5)\n\\item {[}3.5,5.0{]}\n\\item {[}3.5,5.0{]}\n\\item {[}1.0,3.0)\n\\item {[}3.5,5.0{]}\n\\item {[}3.5,5.0{]}\n\\item {[}3.5,5.0{]}\n\\item {[}3.5,5.0{]}\n\\item {[}1.0,3.0)\n\\item {[}3.0,3.5)\n\\item {[}3.0,3.5)\n\\item {[}3.5,5.0{]}\n\\item {[}1.0,3.0)\n\\item {[}3.0,3.5)\n\\item {[}3.0,3.5)\n\\item {[}3.5,5.0{]}\n\\item {[}3.0,3.5)\n\\item {[}3.5,5.0{]}\n\\item {[}3.5,5.0{]}\n\\item {[}3.5,5.0{]}\n\\item {[}3.5,5.0{]}\n\\item {[}3.5,5.0{]}\n\\item {[}3.5,5.0{]}\n\\item {[}1.0,3.0)\n\\item {[}3.0,3.5)\n\\item {[}3.5,5.0{]}\n\\item {[}3.5,5.0{]}\n\\item {[}1.0,3.0)\n\\item {[}3.0,3.5)\n\\item {[}3.0,3.5)\n\\item {[}3.5,5.0{]}\n\\item {[}1.0,3.0)\n\\item {[}1.0,3.0)\n\\item {[}1.0,3.0)\n\\item {[}1.0,3.0)\n\\item {[}3.0,3.5)\n\\item {[}1.0,3.0)\n\\item {[}1.0,3.0)\n\\item {[}3.5,5.0{]}\n\\item {[}3.5,5.0{]}\n\\item {[}3.5,5.0{]}\n\\item {[}3.5,5.0{]}\n\\item {[}1.0,3.0)\n\\item {[}3.0,3.5)\n\\item {[}1.0,3.0)\n\\item {[}1.0,3.0)\n\\item {[}1.0,3.0)\n\\item {[}1.0,3.0)\n\\item {[}3.0,3.5)\n\\item {[}3.0,3.5)\n\\item {[}3.0,3.5)\n\\item {[}3.0,3.5)\n\\item {[}3.5,5.0{]}\n\\item {[}3.5,5.0{]}\n\\item {[}3.5,5.0{]}\n\\item {[}3.0,3.5)\n\\item {[}3.0,3.5)\n\\item {[}1.0,3.0)\n\\item {[}3.5,5.0{]}\n\\item {[}3.0,3.5)\n\\item {[}3.5,5.0{]}\n\\item {[}3.0,3.5)\n\\item {[}1.0,3.0)\n\\item {[}1.0,3.0)\n\\item {[}3.0,3.5)\n\\item {[}3.0,3.5)\n\\item {[}3.5,5.0{]}\n\\item {[}3.0,3.5)\n\\item {[}3.0,3.5)\n\\item {[}3.0,3.5)\n\\item {[}3.0,3.5)\n\\item {[}3.0,3.5)\n\\item {[}3.5,5.0{]}\n\\item {[}3.5,5.0{]}\n\\item {[}3.5,5.0{]}\n\\item {[}3.5,5.0{]}\n\\item {[}3.5,5.0{]}\n\\item {[}3.0,3.5)\n\\item {[}3.0,3.5)\n\\item {[}3.5,5.0{]}\n\\item {[}3.5,5.0{]}\n\\item {[}3.5,5.0{]}\n\\item {[}3.5,5.0{]}\n\\item {[}3.5,5.0{]}\n\\item {[}3.5,5.0{]}\n\\item {[}3.5,5.0{]}\n\\item {[}3.0,3.5)\n\\item {[}1.0,3.0)\n\\item {[}3.5,5.0{]}\n\\item {[}3.5,5.0{]}\n\\item {[}3.5,5.0{]}\n\\item {[}3.0,3.5)\n\\item {[}3.5,5.0{]}\n\\item {[}3.0,3.5)\n\\item {[}1.0,3.0)\n\\item {[}1.0,3.0)\n\\item {[}3.0,3.5)\n\\item {[}3.5,5.0{]}\n\\item {[}3.5,5.0{]}\n\\item {[}3.5,5.0{]}\n\\item {[}3.0,3.5)\n\\item {[}3.0,3.5)\n\\item {[}3.5,5.0{]}\n\\item {[}3.5,5.0{]}\n\\item {[}3.5,5.0{]}\n\\item {[}3.5,5.0{]}\n\\item {[}3.5,5.0{]}\n\\item {[}3.5,5.0{]}\n\\item {[}3.0,3.5)\n\\item {[}3.0,3.5)\n\\item {[}3.5,5.0{]}\n\\item {[}3.5,5.0{]}\n\\item {[}3.5,5.0{]}\n\\item {[}3.5,5.0{]}\n\\item {[}3.5,5.0{]}\n\\item {[}3.5,5.0{]}\n\\item {[}1.0,3.0)\n\\item {[}3.0,3.5)\n\\item {[}3.5,5.0{]}\n\\item {[}3.0,3.5)\n\\item {[}3.5,5.0{]}\n\\item {[}3.5,5.0{]}\n\\item {[}1.0,3.0)\n\\item {[}1.0,3.0)\n\\item {[}3.5,5.0{]}\n\\item {[}3.0,3.5)\n\\item {[}3.0,3.5)\n\\item {[}3.5,5.0{]}\n\\item {[}1.0,3.0)\n\\item {[}1.0,3.0)\n\\item {[}1.0,3.0)\n\\item {[}3.0,3.5)\n\\item {[}3.0,3.5)\n\\item {[}3.0,3.5)\n\\item {[}3.5,5.0{]}\n\\item {[}1.0,3.0)\n\\item {[}3.5,5.0{]}\n\\item {[}3.5,5.0{]}\n\\item {[}3.5,5.0{]}\n\\item {[}3.5,5.0{]}\n\\item {[}1.0,3.0)\n\\item {[}3.0,3.5)\n\\item {[}3.0,3.5)\n\\item {[}3.5,5.0{]}\n\\item {[}3.0,3.5)\n\\item {[}1.0,3.0)\n\\item {[}1.0,3.0)\n\\item {[}1.0,3.0)\n\\item {[}3.5,5.0{]}\n\\item {[}3.5,5.0{]}\n\\item {[}3.5,5.0{]}\n\\item {[}3.0,3.5)\n\\item {[}3.5,5.0{]}\n\\item {[}3.5,5.0{]}\n\\item {[}1.0,3.0)\n\\item {[}3.5,5.0{]}\n\\item {[}3.5,5.0{]}\n\\item {[}3.0,3.5)\n\\item {[}3.0,3.5)\n\\item {[}3.0,3.5)\n\\item {[}1.0,3.0)\n\\item {[}1.0,3.0)\n\\item {[}1.0,3.0)\n\\item {[}1.0,3.0)\n\\item {[}1.0,3.0)\n\\item {[}1.0,3.0)\n\\item {[}1.0,3.0)\n\\item {[}3.5,5.0{]}\n\\item {[}3.5,5.0{]}\n\\item {[}3.0,3.5)\n\\item {[}3.5,5.0{]}\n\\item {[}1.0,3.0)\n\\item {[}3.0,3.5)\n\\item {[}3.5,5.0{]}\n\\item {[}3.5,5.0{]}\n\\item {[}3.5,5.0{]}\n\\item {[}3.5,5.0{]}\n\\item {[}3.5,5.0{]}\n\\item {[}3.5,5.0{]}\n\\item {[}3.5,5.0{]}\n\\item {[}1.0,3.0)\n\\item {[}3.0,3.5)\n\\item {[}3.0,3.5)\n\\item {[}3.5,5.0{]}\n\\item {[}3.5,5.0{]}\n\\item {[}3.0,3.5)\n\\item {[}3.0,3.5)\n\\item {[}3.5,5.0{]}\n\\item {[}3.5,5.0{]}\n\\item {[}1.0,3.0)\n\\item {[}1.0,3.0)\n\\item {[}1.0,3.0)\n\\item {[}3.0,3.5)\n\\item {[}3.5,5.0{]}\n\\item {[}3.0,3.5)\n\\item {[}3.5,5.0{]}\n\\item {[}1.0,3.0)\n\\item {[}3.0,3.5)\n\\item ⋯\n\\item {[}3.0,3.5)\n\\item {[}3.0,3.5)\n\\item {[}3.5,5.0{]}\n\\item {[}3.0,3.5)\n\\item {[}3.5,5.0{]}\n\\item {[}3.5,5.0{]}\n\\item {[}3.5,5.0{]}\n\\item {[}3.5,5.0{]}\n\\item {[}3.5,5.0{]}\n\\item {[}3.0,3.5)\n\\item {[}3.0,3.5)\n\\item {[}3.0,3.5)\n\\item {[}3.5,5.0{]}\n\\item {[}3.0,3.5)\n\\item {[}1.0,3.0)\n\\item {[}1.0,3.0)\n\\item {[}1.0,3.0)\n\\item {[}3.0,3.5)\n\\item {[}3.0,3.5)\n\\item {[}3.0,3.5)\n\\item {[}3.0,3.5)\n\\item {[}1.0,3.0)\n\\item {[}3.0,3.5)\n\\item {[}3.0,3.5)\n\\item {[}3.0,3.5)\n\\item {[}1.0,3.0)\n\\item {[}3.0,3.5)\n\\item {[}1.0,3.0)\n\\item {[}3.0,3.5)\n\\item {[}1.0,3.0)\n\\item {[}3.0,3.5)\n\\item {[}1.0,3.0)\n\\item {[}1.0,3.0)\n\\item {[}1.0,3.0)\n\\item {[}1.0,3.0)\n\\item {[}1.0,3.0)\n\\item {[}3.0,3.5)\n\\item {[}3.0,3.5)\n\\item {[}3.5,5.0{]}\n\\item {[}3.0,3.5)\n\\item {[}1.0,3.0)\n\\item {[}3.0,3.5)\n\\item {[}1.0,3.0)\n\\item {[}3.0,3.5)\n\\item {[}3.0,3.5)\n\\item {[}1.0,3.0)\n\\item {[}3.0,3.5)\n\\item {[}3.0,3.5)\n\\item {[}3.5,5.0{]}\n\\item {[}1.0,3.0)\n\\item {[}3.5,5.0{]}\n\\item {[}3.5,5.0{]}\n\\item {[}3.0,3.5)\n\\item {[}3.5,5.0{]}\n\\item {[}3.5,5.0{]}\n\\item {[}3.0,3.5)\n\\item {[}3.5,5.0{]}\n\\item {[}3.5,5.0{]}\n\\item {[}3.0,3.5)\n\\item {[}3.0,3.5)\n\\item {[}3.5,5.0{]}\n\\item {[}1.0,3.0)\n\\item {[}3.0,3.5)\n\\item {[}1.0,3.0)\n\\item {[}3.0,3.5)\n\\item {[}3.0,3.5)\n\\item {[}3.5,5.0{]}\n\\item {[}3.0,3.5)\n\\item {[}3.5,5.0{]}\n\\item {[}3.0,3.5)\n\\item {[}1.0,3.0)\n\\item {[}3.0,3.5)\n\\item {[}3.0,3.5)\n\\item {[}3.5,5.0{]}\n\\item {[}1.0,3.0)\n\\item {[}3.5,5.0{]}\n\\item {[}3.5,5.0{]}\n\\item {[}3.0,3.5)\n\\item {[}1.0,3.0)\n\\item {[}1.0,3.0)\n\\item {[}3.0,3.5)\n\\item {[}3.5,5.0{]}\n\\item {[}1.0,3.0)\n\\item {[}3.0,3.5)\n\\item {[}3.0,3.5)\n\\item {[}3.0,3.5)\n\\item {[}3.0,3.5)\n\\item {[}3.5,5.0{]}\n\\item {[}3.5,5.0{]}\n\\item {[}3.5,5.0{]}\n\\item {[}3.5,5.0{]}\n\\item {[}3.0,3.5)\n\\item {[}3.5,5.0{]}\n\\item {[}3.5,5.0{]}\n\\item {[}3.5,5.0{]}\n\\item {[}3.5,5.0{]}\n\\item {[}3.0,3.5)\n\\item {[}3.5,5.0{]}\n\\item {[}3.5,5.0{]}\n\\item {[}1.0,3.0)\n\\item {[}1.0,3.0)\n\\item {[}1.0,3.0)\n\\item {[}3.0,3.5)\n\\item {[}3.0,3.5)\n\\item {[}3.5,5.0{]}\n\\item {[}1.0,3.0)\n\\item {[}3.0,3.5)\n\\item {[}3.5,5.0{]}\n\\item {[}1.0,3.0)\n\\item {[}1.0,3.0)\n\\item {[}1.0,3.0)\n\\item {[}1.0,3.0)\n\\item {[}1.0,3.0)\n\\item {[}3.0,3.5)\n\\item {[}3.0,3.5)\n\\item {[}3.0,3.5)\n\\item {[}3.0,3.5)\n\\item {[}3.0,3.5)\n\\item {[}3.0,3.5)\n\\item {[}3.5,5.0{]}\n\\item {[}3.5,5.0{]}\n\\item {[}3.5,5.0{]}\n\\item {[}3.0,3.5)\n\\item {[}3.5,5.0{]}\n\\item {[}3.0,3.5)\n\\item {[}1.0,3.0)\n\\item {[}1.0,3.0)\n\\item {[}3.0,3.5)\n\\item {[}3.0,3.5)\n\\item {[}1.0,3.0)\n\\item {[}3.0,3.5)\n\\item {[}3.0,3.5)\n\\item {[}3.0,3.5)\n\\item {[}1.0,3.0)\n\\item {[}1.0,3.0)\n\\item {[}1.0,3.0)\n\\item {[}3.0,3.5)\n\\item {[}3.5,5.0{]}\n\\item {[}3.5,5.0{]}\n\\item {[}1.0,3.0)\n\\item {[}1.0,3.0)\n\\item {[}1.0,3.0)\n\\item {[}3.0,3.5)\n\\item {[}1.0,3.0)\n\\item {[}3.5,5.0{]}\n\\item {[}3.0,3.5)\n\\item {[}1.0,3.0)\n\\item {[}3.0,3.5)\n\\item {[}3.5,5.0{]}\n\\item {[}3.5,5.0{]}\n\\item {[}3.0,3.5)\n\\item {[}3.0,3.5)\n\\item {[}3.0,3.5)\n\\item {[}3.5,5.0{]}\n\\item {[}3.5,5.0{]}\n\\item {[}3.0,3.5)\n\\item {[}3.0,3.5)\n\\item {[}3.5,5.0{]}\n\\item {[}1.0,3.0)\n\\item {[}3.0,3.5)\n\\item {[}3.0,3.5)\n\\item {[}3.5,5.0{]}\n\\item {[}3.5,5.0{]}\n\\item {[}3.0,3.5)\n\\item {[}1.0,3.0)\n\\item {[}1.0,3.0)\n\\item {[}3.0,3.5)\n\\item {[}3.0,3.5)\n\\item {[}3.0,3.5)\n\\item {[}3.0,3.5)\n\\item {[}3.5,5.0{]}\n\\item {[}3.0,3.5)\n\\item {[}3.5,5.0{]}\n\\item {[}1.0,3.0)\n\\item {[}1.0,3.0)\n\\item {[}3.0,3.5)\n\\item {[}3.5,5.0{]}\n\\item {[}3.5,5.0{]}\n\\item {[}3.5,5.0{]}\n\\item {[}3.0,3.5)\n\\item {[}3.5,5.0{]}\n\\item {[}3.5,5.0{]}\n\\item {[}3.5,5.0{]}\n\\item {[}1.0,3.0)\n\\item {[}3.5,5.0{]}\n\\item {[}3.5,5.0{]}\n\\item {[}3.0,3.5)\n\\item {[}3.5,5.0{]}\n\\item {[}3.5,5.0{]}\n\\item {[}3.0,3.5)\n\\item {[}3.0,3.5)\n\\item {[}3.0,3.5)\n\\item {[}3.5,5.0{]}\n\\item {[}3.0,3.5)\n\\item {[}3.5,5.0{]}\n\\item {[}3.5,5.0{]}\n\\item {[}3.0,3.5)\n\\item {[}3.5,5.0{]}\n\\item {[}3.0,3.5)\n\\item {[}3.0,3.5)\n\\end{enumerate*}\n\n\\emph{Levels}: \\begin{enumerate*}\n\\item '{[}1.0,3.0)'\n\\item '{[}3.0,3.5)'\n\\item '{[}3.5,5.0{]}'\n\\end{enumerate*}\n","text/plain":[" [1] [3.5,5.0] [1.0,3.0) [3.0,3.5) [3.5,5.0] [3.5,5.0] [1.0,3.0) [3.5,5.0]\n"," [8] [3.5,5.0] [3.5,5.0] [3.5,5.0] [1.0,3.0) [3.0,3.5) [3.0,3.5) [3.5,5.0]\n"," [15] [1.0,3.0) [3.0,3.5) [3.0,3.5) [3.5,5.0] [3.0,3.5) [3.5,5.0] [3.5,5.0]\n"," [22] [3.5,5.0] [3.5,5.0] [3.5,5.0] [3.5,5.0] [1.0,3.0) [3.0,3.5) [3.5,5.0]\n"," [29] [3.5,5.0] [1.0,3.0) [3.0,3.5) [3.0,3.5) [3.5,5.0] [1.0,3.0) [1.0,3.0)\n"," [36] [1.0,3.0) [1.0,3.0) [3.0,3.5) [1.0,3.0) [1.0,3.0) [3.5,5.0] [3.5,5.0]\n"," [43] [3.5,5.0] [3.5,5.0] [1.0,3.0) [3.0,3.5) [1.0,3.0) [1.0,3.0) [1.0,3.0)\n"," [50] [1.0,3.0) [3.0,3.5) [3.0,3.5) [3.0,3.5) [3.0,3.5) [3.5,5.0] [3.5,5.0]\n"," [57] [3.5,5.0] [3.0,3.5) [3.0,3.5) [1.0,3.0) [3.5,5.0] [3.0,3.5) [3.5,5.0]\n"," [64] [3.0,3.5) [1.0,3.0) [1.0,3.0) [3.0,3.5) [3.0,3.5) [3.5,5.0] [3.0,3.5)\n"," [71] [3.0,3.5) [3.0,3.5) [3.0,3.5) [3.0,3.5) [3.5,5.0] [3.5,5.0] [3.5,5.0]\n"," [78] [3.5,5.0] [3.5,5.0] [3.0,3.5) [3.0,3.5) [3.5,5.0] [3.5,5.0] [3.5,5.0]\n"," [85] [3.5,5.0] [3.5,5.0] [3.5,5.0] [3.5,5.0] [3.0,3.5) [1.0,3.0) [3.5,5.0]\n"," [92] [3.5,5.0] [3.5,5.0] [3.0,3.5) [3.5,5.0] [3.0,3.5) [1.0,3.0) [1.0,3.0)\n"," [99] [3.0,3.5) [3.5,5.0] [3.5,5.0] [3.5,5.0] [3.0,3.5) [3.0,3.5) [3.5,5.0]\n"," [106] [3.5,5.0] [3.5,5.0] [3.5,5.0] [3.5,5.0] [3.5,5.0] [3.0,3.5) [3.0,3.5)\n"," [113] [3.5,5.0] [3.5,5.0] [3.5,5.0] [3.5,5.0] [3.5,5.0] [3.5,5.0] [1.0,3.0)\n"," [120] [3.0,3.5) [3.5,5.0] [3.0,3.5) [3.5,5.0] [3.5,5.0] [1.0,3.0) [1.0,3.0)\n"," [127] [3.5,5.0] [3.0,3.5) [3.0,3.5) [3.5,5.0] [1.0,3.0) [1.0,3.0) [1.0,3.0)\n"," [134] [3.0,3.5) [3.0,3.5) [3.0,3.5) [3.5,5.0] [1.0,3.0) [3.5,5.0] [3.5,5.0]\n"," [141] [3.5,5.0] [3.5,5.0] [1.0,3.0) [3.0,3.5) [3.0,3.5) [3.5,5.0] [3.0,3.5)\n"," [148] [1.0,3.0) [1.0,3.0) [1.0,3.0) [3.5,5.0] [3.5,5.0] [3.5,5.0] [3.0,3.5)\n"," [155] [3.5,5.0] [3.5,5.0] [1.0,3.0) [3.5,5.0] [3.5,5.0] [3.0,3.5) [3.0,3.5)\n"," [162] [3.0,3.5) [1.0,3.0) [1.0,3.0) [1.0,3.0) [1.0,3.0) [1.0,3.0) [1.0,3.0)\n"," [169] [1.0,3.0) [3.5,5.0] [3.5,5.0] [3.0,3.5) [3.5,5.0] [1.0,3.0) [3.0,3.5)\n"," [176] [3.5,5.0] [3.5,5.0] [3.5,5.0] [3.5,5.0] [3.5,5.0] [3.5,5.0] [3.5,5.0]\n"," [183] [1.0,3.0) [3.0,3.5) [3.0,3.5) [3.5,5.0] [3.5,5.0] [3.0,3.5) [3.0,3.5)\n"," [190] [3.5,5.0] [3.5,5.0] [1.0,3.0) [1.0,3.0) [1.0,3.0) [3.0,3.5) [3.5,5.0]\n"," [197] [3.0,3.5) [3.5,5.0] [1.0,3.0) [3.0,3.5) [3.0,3.5) [3.0,3.5) [3.5,5.0]\n"," [204] [3.0,3.5) [3.5,5.0] [3.0,3.5) [3.5,5.0] [3.5,5.0] [3.0,3.5) [1.0,3.0)\n"," [211] [1.0,3.0) [3.0,3.5) [1.0,3.0) [3.5,5.0] [1.0,3.0) [3.5,5.0] [1.0,3.0)\n"," [218] [3.5,5.0] [3.0,3.5) [3.5,5.0] [3.5,5.0] [3.5,5.0] [3.5,5.0] [3.0,3.5)\n"," [225] [3.5,5.0] [3.5,5.0] [3.0,3.5) [3.5,5.0] [3.5,5.0] [3.5,5.0] [3.5,5.0]\n"," [232] [3.5,5.0] [3.5,5.0] [3.5,5.0] [3.0,3.5) [3.0,3.5) [1.0,3.0) [3.0,3.5)\n"," [239] [3.0,3.5) [3.5,5.0] [1.0,3.0) [3.5,5.0] [3.5,5.0] [3.5,5.0] [3.5,5.0]\n"," [246] [1.0,3.0) [3.0,3.5) [3.5,5.0] [3.0,3.5) [1.0,3.0) [1.0,3.0) [1.0,3.0)\n"," [253] [3.5,5.0] [3.5,5.0] [3.5,5.0] [3.5,5.0] [3.5,5.0] [3.0,3.5) [3.0,3.5)\n"," [260] [3.5,5.0] [3.5,5.0] [3.0,3.5) [3.0,3.5) [3.5,5.0] [1.0,3.0) [3.0,3.5)\n"," [267] [3.0,3.5) [3.0,3.5) [3.0,3.5) [3.5,5.0] [3.5,5.0] [1.0,3.0) [3.0,3.5)\n"," [274] [1.0,3.0) [3.0,3.5) [3.5,5.0] [3.0,3.5) [3.0,3.5) [1.0,3.0) [1.0,3.0)\n"," [281] [1.0,3.0) [1.0,3.0) [1.0,3.0) [1.0,3.0) [3.5,5.0] [3.0,3.5) [1.0,3.0)\n"," [288] [1.0,3.0) [3.0,3.5) [3.5,5.0] [3.0,3.5) [1.0,3.0) [3.0,3.5) [3.5,5.0]\n"," [295] [3.5,5.0] [3.0,3.5) [3.5,5.0] [3.5,5.0] [3.5,5.0] [3.5,5.0] [3.5,5.0]\n"," [302] [1.0,3.0) [3.0,3.5) [3.5,5.0] [3.5,5.0] [3.0,3.5) [3.5,5.0] [3.0,3.5)\n"," [309] [3.5,5.0] [3.5,5.0] [3.5,5.0] [3.5,5.0] [3.0,3.5) [3.5,5.0] [3.5,5.0]\n"," [316] [3.5,5.0] [3.5,5.0] [3.0,3.5) [3.0,3.5) [3.5,5.0] [3.0,3.5) [3.5,5.0]\n"," [323] [3.5,5.0] [1.0,3.0) [1.0,3.0) [1.0,3.0) [1.0,3.0) [1.0,3.0) [3.0,3.5)\n"," [330] [3.5,5.0] [3.5,5.0] [3.0,3.5) [1.0,3.0) [1.0,3.0) [1.0,3.0) [1.0,3.0)\n"," [337] [3.0,3.5) [3.0,3.5) [3.0,3.5) [1.0,3.0) [1.0,3.0) [1.0,3.0) [3.0,3.5)\n"," [344] [3.0,3.5) [3.5,5.0] [3.5,5.0] [3.5,5.0] [3.5,5.0] [3.0,3.5) [3.5,5.0]\n"," [351] [3.0,3.5) [1.0,3.0) [3.0,3.5) [3.0,3.5) [3.0,3.5) [3.5,5.0] [3.5,5.0]\n"," [358] [3.5,5.0] [3.5,5.0] [3.5,5.0] [3.5,5.0] [3.5,5.0] [3.5,5.0] [1.0,3.0)\n"," [365] [3.0,3.5) [3.5,5.0] [1.0,3.0) [3.0,3.5) [3.0,3.5) [1.0,3.0) [3.0,3.5)\n"," [372] [1.0,3.0) [1.0,3.0) [1.0,3.0) [3.0,3.5) [1.0,3.0) [1.0,3.0) [3.0,3.5)\n"," [379] [1.0,3.0) [3.0,3.5) [3.5,5.0] [3.5,5.0] [3.0,3.5) [3.0,3.5) [3.0,3.5)\n"," [386] [1.0,3.0) [3.5,5.0] [1.0,3.0) [1.0,3.0) [1.0,3.0) [3.5,5.0] [3.5,5.0]\n"," [393] [1.0,3.0) [1.0,3.0) [1.0,3.0) [1.0,3.0) [3.0,3.5) [3.0,3.5) [3.0,3.5)\n"," [400] [3.0,3.5) [1.0,3.0) [1.0,3.0) [3.0,3.5) [3.5,5.0] [3.5,5.0] [3.5,5.0]\n"," [407] [3.5,5.0] [3.5,5.0] [1.0,3.0) [1.0,3.0) [3.0,3.5) [1.0,3.0) [3.0,3.5)\n"," [414] [3.0,3.5) [3.0,3.5) [3.5,5.0] [3.0,3.5) [3.0,3.5) [3.5,5.0] [1.0,3.0)\n"," [421] [1.0,3.0) [3.0,3.5) [1.0,3.0) [1.0,3.0) [1.0,3.0) [1.0,3.0) [3.0,3.5)\n"," [428] [3.0,3.5) [3.0,3.5) [1.0,3.0) [3.0,3.5) [3.5,5.0] [3.0,3.5) [1.0,3.0)\n"," [435] [3.5,5.0] [3.0,3.5) [3.5,5.0] [1.0,3.0) [3.5,5.0] [3.0,3.5) [3.0,3.5)\n"," [442] [1.0,3.0) [3.0,3.5) [3.5,5.0] [1.0,3.0) [3.5,5.0] [1.0,3.0) [3.0,3.5)\n"," [449] [3.5,5.0] [1.0,3.0) [3.5,5.0] [3.5,5.0] [3.0,3.5) [3.0,3.5) [1.0,3.0)\n"," [456] [3.0,3.5) [3.0,3.5) [3.0,3.5) [3.0,3.5) [3.0,3.5) [3.5,5.0] [3.5,5.0]\n"," [463] [3.0,3.5) [3.0,3.5) [3.0,3.5) [1.0,3.0) [1.0,3.0) [1.0,3.0) [3.0,3.5)\n"," [470] [3.0,3.5) [3.0,3.5) [3.0,3.5) [3.5,5.0] [3.0,3.5) [3.5,5.0] [1.0,3.0)\n"," [477] [3.0,3.5) [1.0,3.0) [3.0,3.5) [3.5,5.0] [1.0,3.0) [1.0,3.0) [3.5,5.0]\n"," [484] [3.0,3.5) [3.5,5.0] [3.5,5.0] [1.0,3.0) [3.5,5.0] [1.0,3.0) [3.0,3.5)\n"," [491] [3.5,5.0] [3.5,5.0] [3.0,3.5) [3.0,3.5) [3.5,5.0] [3.0,3.5) [3.0,3.5)\n"," [498] [3.0,3.5) [3.0,3.5) [3.0,3.5) [3.5,5.0] [3.5,5.0] [3.5,5.0] [3.0,3.5)\n"," [505] [3.5,5.0] [3.0,3.5) [3.0,3.5) [1.0,3.0) [3.0,3.5) [3.0,3.5) [3.0,3.5)\n"," [512] [3.0,3.5) [3.0,3.5) [3.0,3.5) [3.0,3.5) [1.0,3.0) [1.0,3.0) [1.0,3.0)\n"," [519] [3.0,3.5) [3.5,5.0] [3.5,5.0] [1.0,3.0) [3.0,3.5) [1.0,3.0) [3.0,3.5)\n"," [526] [3.0,3.5) [3.0,3.5) [3.0,3.5) [3.0,3.5) [3.0,3.5) [3.5,5.0] [1.0,3.0)\n"," [533] [1.0,3.0) [3.5,5.0] [3.5,5.0] [3.5,5.0] [3.5,5.0] [3.5,5.0] [3.5,5.0]\n"," [540] [3.0,3.5) [3.5,5.0] [1.0,3.0) [1.0,3.0) [1.0,3.0) [3.0,3.5) [1.0,3.0)\n"," [547] [3.0,3.5) [3.0,3.5) [3.0,3.5) [3.0,3.5) [3.0,3.5) [3.5,5.0] [3.5,5.0]\n"," [554] [3.0,3.5) [1.0,3.0) [3.5,5.0] [3.5,5.0] [3.0,3.5) [3.5,5.0] [3.5,5.0]\n"," [561] [3.5,5.0] [3.0,3.5) [3.0,3.5) [3.0,3.5) [3.5,5.0] [3.5,5.0] [3.5,5.0]\n"," [568] [3.0,3.5) [3.0,3.5) [3.5,5.0] [3.5,5.0] [3.5,5.0] [3.5,5.0] [3.5,5.0]\n"," [575] [3.0,3.5) [3.0,3.5) [3.5,5.0] [1.0,3.0) [1.0,3.0) [1.0,3.0) [1.0,3.0)\n"," [582] [3.5,5.0] [3.5,5.0] [3.5,5.0] [3.5,5.0] [3.5,5.0] [3.5,5.0] [3.0,3.5)\n"," [589] [3.0,3.5) [3.5,5.0] [3.5,5.0] [3.5,5.0] [1.0,3.0) [3.5,5.0] [3.0,3.5)\n"," [596] [1.0,3.0) [3.5,5.0] [3.5,5.0] [3.0,3.5) [3.0,3.5) [3.5,5.0] [3.0,3.5)\n"," [603] [3.5,5.0] [3.5,5.0] [3.0,3.5) [3.0,3.5) [1.0,3.0) [3.0,3.5) [3.5,5.0]\n"," [610] [1.0,3.0) [3.5,5.0] [1.0,3.0) [3.0,3.5) [1.0,3.0) [1.0,3.0) [3.0,3.5)\n"," [617] [1.0,3.0) [1.0,3.0) [3.0,3.5) [3.0,3.5) [3.5,5.0] [3.0,3.5) [3.0,3.5)\n"," [624] [3.0,3.5) [3.0,3.5) [3.0,3.5) [1.0,3.0) [1.0,3.0) [3.5,5.0] [1.0,3.0)\n"," [631] [3.0,3.5) [1.0,3.0) [1.0,3.0) [1.0,3.0) [1.0,3.0) [1.0,3.0) [3.0,3.5)\n"," [638] [3.5,5.0] [3.5,5.0] [1.0,3.0) [3.0,3.5) [1.0,3.0) [1.0,3.0) [3.5,5.0]\n"," [645] [1.0,3.0) [3.0,3.5) [3.0,3.5) [3.0,3.5) [3.5,5.0] [3.5,5.0] [1.0,3.0)\n"," [652] [3.0,3.5) [3.0,3.5) [1.0,3.0) [1.0,3.0) [1.0,3.0) [1.0,3.0) [1.0,3.0)\n"," [659] [3.5,5.0] [3.5,5.0] [3.5,5.0] [3.5,5.0] [3.5,5.0] [3.5,5.0] [3.5,5.0]\n"," [666] [3.5,5.0] [3.0,3.5) [3.5,5.0] [3.5,5.0] [3.5,5.0] [3.5,5.0] [3.0,3.5)\n"," [673] [3.0,3.5) [3.5,5.0] [1.0,3.0) [3.5,5.0] [1.0,3.0) [3.5,5.0] [3.5,5.0]\n"," [680] [3.0,3.5) [3.5,5.0] [3.5,5.0] [3.5,5.0] [3.0,3.5) [3.5,5.0] [3.5,5.0]\n"," [687] [3.5,5.0] [3.5,5.0] [3.5,5.0] [3.5,5.0] [3.5,5.0] [3.0,3.5) [1.0,3.0)\n"," [694] [3.0,3.5) [3.5,5.0] [3.5,5.0] [1.0,3.0) [3.0,3.5) [3.0,3.5) [3.0,3.5)\n"," [701] [3.5,5.0] [1.0,3.0) [3.0,3.5) [3.0,3.5) [3.5,5.0] [3.5,5.0] [3.0,3.5)\n"," [708] [3.0,3.5) [3.5,5.0] [3.0,3.5) [3.5,5.0] [3.0,3.5) [3.0,3.5) [3.0,3.5)\n"," [715] [3.5,5.0] [3.0,3.5) [3.5,5.0] [3.5,5.0] [3.5,5.0] [3.5,5.0] [3.5,5.0]\n"," [722] [3.0,3.5) [3.5,5.0] [3.0,3.5) [1.0,3.0) [3.0,3.5) [3.0,3.5) [1.0,3.0)\n"," [729] [3.0,3.5) [3.5,5.0] [3.5,5.0] [3.5,5.0] [3.5,5.0] [3.5,5.0] [3.0,3.5)\n"," [736] [1.0,3.0) [3.0,3.5) [3.0,3.5) [3.0,3.5) [1.0,3.0) [3.5,5.0] [3.0,3.5)\n"," [743] [1.0,3.0) [1.0,3.0) [3.0,3.5) [3.0,3.5) [3.0,3.5) [3.0,3.5) [3.0,3.5)\n"," [750] [3.5,5.0] [1.0,3.0) [1.0,3.0) [3.0,3.5) [3.5,5.0] [1.0,3.0) [3.0,3.5)\n"," [757] [3.0,3.5) [3.0,3.5) [3.0,3.5) [3.0,3.5) [3.5,5.0] [3.0,3.5) [3.0,3.5)\n"," [764] [3.5,5.0] [3.5,5.0] [3.0,3.5) [1.0,3.0) [3.5,5.0] [3.5,5.0] [3.5,5.0]\n"," [771] [3.0,3.5) [1.0,3.0) [1.0,3.0) [3.0,3.5) [3.5,5.0] [1.0,3.0) [3.0,3.5)\n"," [778] [3.0,3.5) [3.0,3.5) [1.0,3.0) [1.0,3.0) [1.0,3.0) [3.0,3.5) [3.0,3.5)\n"," [785] [3.0,3.5) [3.0,3.5) [3.5,5.0] [3.5,5.0] [1.0,3.0) [3.0,3.5) [3.5,5.0]\n"," [792] [3.5,5.0] [3.0,3.5) [3.5,5.0] [3.5,5.0] [3.5,5.0] [3.5,5.0] [3.5,5.0]\n"," [799] [3.5,5.0] [3.5,5.0] [1.0,3.0) [3.5,5.0] [1.0,3.0) [3.0,3.5) [3.0,3.5)\n"," [806] [3.0,3.5) [3.0,3.5) [1.0,3.0) [1.0,3.0) [3.0,3.5) [3.5,5.0] [1.0,3.0)\n"," [813] [1.0,3.0) [3.0,3.5) [3.0,3.5) [3.5,5.0] [1.0,3.0) [3.5,5.0] [3.5,5.0]\n"," [820] [3.5,5.0] [3.5,5.0] [1.0,3.0) [1.0,3.0) [3.5,5.0] [3.5,5.0] [1.0,3.0)\n"," [827] [1.0,3.0) [3.5,5.0] [1.0,3.0) [3.0,3.5) [3.5,5.0] [3.0,3.5) [3.0,3.5)\n"," [834] [3.5,5.0] [1.0,3.0) [1.0,3.0) [1.0,3.0) [3.5,5.0] [3.0,3.5) [3.0,3.5)\n"," [841] [3.0,3.5) [1.0,3.0) [3.0,3.5) [1.0,3.0) [3.0,3.5) [1.0,3.0) [1.0,3.0)\n"," [848] [3.0,3.5) [3.5,5.0] [3.0,3.5) [3.0,3.5) [3.0,3.5) [3.5,5.0] [3.5,5.0]\n"," [855] [3.0,3.5) [3.5,5.0] [3.5,5.0] [3.5,5.0] [3.5,5.0] [3.0,3.5) [3.5,5.0]\n"," [862] [3.5,5.0] [3.5,5.0] [3.5,5.0] [3.5,5.0] [3.5,5.0] [1.0,3.0) [3.0,3.5)\n"," [869] [1.0,3.0) [3.0,3.5) [1.0,3.0) [3.0,3.5) [3.5,5.0] [1.0,3.0) [3.0,3.5)\n"," [876] [3.0,3.5) [3.5,5.0] [1.0,3.0) [1.0,3.0) [1.0,3.0) [3.5,5.0] [3.5,5.0]\n"," [883] [3.5,5.0] [3.5,5.0] [3.0,3.5) [3.0,3.5) [3.0,3.5) [1.0,3.0) [3.5,5.0]\n"," [890] [3.0,3.5) [3.5,5.0] [1.0,3.0) [1.0,3.0) [1.0,3.0) [3.0,3.5) [1.0,3.0)\n"," [897] [3.0,3.5) [1.0,3.0) [1.0,3.0) [3.5,5.0] [1.0,3.0) [3.0,3.5) [1.0,3.0)\n"," [904] [1.0,3.0) [3.0,3.5) [3.0,3.5) [3.0,3.5) [3.0,3.5) [3.0,3.5) [3.0,3.5)\n"," [911] [1.0,3.0) [3.5,5.0] [3.5,5.0] [1.0,3.0) [3.0,3.5) [1.0,3.0) [3.0,3.5)\n"," [918] [3.0,3.5) [3.5,5.0] [3.5,5.0] [3.5,5.0] [3.0,3.5) [3.5,5.0] [3.0,3.5)\n"," [925] [3.5,5.0] [3.0,3.5) [1.0,3.0) [1.0,3.0) [3.0,3.5) [3.0,3.5) [1.0,3.0)\n"," [932] [3.0,3.5) [3.0,3.5) [3.5,5.0] [3.5,5.0] [3.0,3.5) [3.5,5.0] [3.0,3.5)\n"," [939] [3.5,5.0] [3.5,5.0] [1.0,3.0) [3.0,3.5) [1.0,3.0) [1.0,3.0) [3.0,3.5)\n"," [946] [3.0,3.5) [3.0,3.5) [3.5,5.0] [3.0,3.5) [3.5,5.0] [1.0,3.0) [1.0,3.0)\n"," [953] [3.5,5.0] [1.0,3.0) [3.5,5.0] [3.0,3.5) [3.0,3.5) [3.0,3.5) [3.5,5.0]\n"," [960] [3.0,3.5) [3.0,3.5) [3.0,3.5) [3.5,5.0] [3.0,3.5) [3.0,3.5) [3.5,5.0]\n"," [967] [3.5,5.0] [3.5,5.0] [3.0,3.5) [3.5,5.0] [3.5,5.0] [3.5,5.0] [1.0,3.0)\n"," [974] [3.0,3.5) [3.5,5.0] [3.0,3.5) [1.0,3.0) [3.5,5.0] [3.0,3.5) [3.5,5.0]\n"," [981] [3.0,3.5) [3.0,3.5) [3.5,5.0] [1.0,3.0) [1.0,3.0) [3.0,3.5) [3.0,3.5)\n"," [988] [3.5,5.0] [1.0,3.0) [1.0,3.0) [3.5,5.0] [3.5,5.0] [3.5,5.0] [3.5,5.0]\n"," [995] [3.5,5.0] [1.0,3.0) [3.5,5.0] [1.0,3.0) [1.0,3.0) [3.0,3.5) [3.0,3.5)\n","[1002] [3.5,5.0] [1.0,3.0) [1.0,3.0) [1.0,3.0) [1.0,3.0) [3.0,3.5) [3.0,3.5)\n","[1009] [3.5,5.0] [1.0,3.0) [1.0,3.0) [3.5,5.0] [3.5,5.0] [3.5,5.0] [1.0,3.0)\n","[1016] [3.0,3.5) [1.0,3.0) [1.0,3.0) [1.0,3.0) [3.5,5.0] [1.0,3.0) [3.5,5.0]\n","[1023] [3.5,5.0] [3.5,5.0] [3.0,3.5) [1.0,3.0) [3.0,3.5) [3.0,3.5) [3.5,5.0]\n","[1030] [3.5,5.0] [3.5,5.0] [3.5,5.0] [3.0,3.5) [3.5,5.0] [3.0,3.5) [3.0,3.5)\n","[1037] [3.5,5.0] [3.5,5.0] [3.5,5.0] [1.0,3.0) [3.5,5.0] [3.5,5.0] [1.0,3.0)\n","[1044] [3.0,3.5) [3.5,5.0] [3.0,3.5) [1.0,3.0) [3.0,3.5) [3.0,3.5) [3.0,3.5)\n","[1051] [3.5,5.0] [3.5,5.0] [3.5,5.0] [3.5,5.0] [3.5,5.0] [3.5,5.0] [3.5,5.0]\n","[1058] [3.0,3.5) [3.5,5.0] [3.0,3.5) [3.0,3.5) [3.0,3.5) [3.5,5.0] [1.0,3.0)\n","[1065] [1.0,3.0) [1.0,3.0) [3.0,3.5) [3.0,3.5) [3.0,3.5) [3.0,3.5) [3.5,5.0]\n","[1072] [3.5,5.0] [1.0,3.0) [1.0,3.0) [3.5,5.0] [3.0,3.5) [3.0,3.5) [1.0,3.0)\n","[1079] [3.5,5.0] [1.0,3.0) [3.0,3.5) [3.0,3.5) [3.5,5.0] [1.0,3.0) [3.0,3.5)\n","[1086] [3.0,3.5) [3.5,5.0] [3.5,5.0] [3.5,5.0] [3.5,5.0] [3.0,3.5) [3.0,3.5)\n","[1093] [3.0,3.5) [3.5,5.0] [3.0,3.5) [1.0,3.0) [1.0,3.0) [3.0,3.5) [3.0,3.5)\n","[1100] [3.5,5.0] [1.0,3.0) [1.0,3.0) [1.0,3.0) [3.0,3.5) [3.0,3.5) [3.5,5.0]\n","[1107] [3.0,3.5) [3.5,5.0] [3.0,3.5) [3.5,5.0] [3.5,5.0] [3.5,5.0] [3.5,5.0]\n","[1114] [3.5,5.0] [1.0,3.0) [1.0,3.0) [3.0,3.5) [3.5,5.0] [3.5,5.0] [3.5,5.0]\n","[1121] [1.0,3.0) [3.5,5.0] [3.5,5.0] [3.5,5.0] [3.5,5.0] [3.0,3.5) [3.5,5.0]\n","[1128] [1.0,3.0) [3.0,3.5) [1.0,3.0) [1.0,3.0) [1.0,3.0) [1.0,3.0) [3.5,5.0]\n","[1135] [1.0,3.0) [1.0,3.0) [3.5,5.0] [3.5,5.0] [3.5,5.0] [1.0,3.0) [3.0,3.5)\n","[1142] [3.0,3.5) [1.0,3.0) [3.0,3.5) [1.0,3.0) [3.0,3.5) [1.0,3.0) [1.0,3.0)\n","[1149] [3.0,3.5) [3.5,5.0] [3.0,3.5) [3.0,3.5) [3.5,5.0] [3.5,5.0] [3.5,5.0]\n","[1156] [3.5,5.0] [1.0,3.0) [1.0,3.0) [1.0,3.0) [3.0,3.5) [1.0,3.0) [3.0,3.5)\n","[1163] [3.5,5.0] [3.5,5.0] [1.0,3.0) [3.0,3.5) [1.0,3.0) [3.0,3.5) [3.5,5.0]\n","[1170] [1.0,3.0) [1.0,3.0) [1.0,3.0) [3.5,5.0] [1.0,3.0) [3.5,5.0] [1.0,3.0)\n","[1177] [1.0,3.0) [1.0,3.0) [3.0,3.5) [1.0,3.0) [1.0,3.0) [3.0,3.5) [3.0,3.5)\n","[1184] [1.0,3.0) [3.5,5.0] [3.0,3.5) [3.0,3.5) [3.0,3.5) [3.0,3.5) [3.0,3.5)\n","[1191] [3.0,3.5) [1.0,3.0) [3.0,3.5) [3.5,5.0] [1.0,3.0) [3.0,3.5) [3.5,5.0]\n","[1198] [3.5,5.0] [3.5,5.0] [1.0,3.0) [3.5,5.0] [3.5,5.0] [3.5,5.0] [3.5,5.0]\n","[1205] [1.0,3.0) [3.5,5.0] [1.0,3.0) [3.0,3.5) [3.0,3.5) [3.5,5.0] [1.0,3.0)\n","[1212] [3.0,3.5) [3.5,5.0] [3.0,3.5) [3.0,3.5) [1.0,3.0) [3.0,3.5) [1.0,3.0)\n","[1219] [1.0,3.0) [1.0,3.0) [3.0,3.5) [1.0,3.0) [3.5,5.0] [3.0,3.5) [3.5,5.0]\n","[1226] [1.0,3.0) [1.0,3.0) [1.0,3.0) [3.0,3.5) [3.0,3.5) [1.0,3.0) [3.0,3.5)\n","[1233] [3.5,5.0] [3.0,3.5) [3.0,3.5) [3.0,3.5) [3.0,3.5) [3.0,3.5) [3.0,3.5)\n","[1240] [3.0,3.5) [3.5,5.0] [3.0,3.5) [3.5,5.0] [3.5,5.0] [3.5,5.0] [1.0,3.0)\n","[1247] [3.5,5.0] [3.5,5.0] [1.0,3.0) [1.0,3.0) [3.0,3.5) [1.0,3.0) [3.5,5.0]\n","[1254] [1.0,3.0) [3.0,3.5) [3.0,3.5) [3.0,3.5) [3.5,5.0] [3.5,5.0] [1.0,3.0)\n","[1261] [3.5,5.0] [1.0,3.0) [3.5,5.0] [3.5,5.0] [3.0,3.5) [3.5,5.0] [3.0,3.5)\n","[1268] [3.5,5.0] [3.0,3.5) [3.5,5.0] [1.0,3.0) [1.0,3.0) [3.5,5.0] [3.5,5.0]\n","[1275] [3.5,5.0] [3.5,5.0] [3.5,5.0] [3.5,5.0] [1.0,3.0) [1.0,3.0) [3.0,3.5)\n","[1282] [3.0,3.5) [3.0,3.5) [3.0,3.5) [3.5,5.0] [3.0,3.5) [3.0,3.5) [3.0,3.5)\n","[1289] [3.5,5.0] [3.5,5.0] [3.5,5.0] [3.5,5.0] [3.5,5.0] [3.0,3.5) [3.5,5.0]\n","[1296] [1.0,3.0) [1.0,3.0) [1.0,3.0) [1.0,3.0) [1.0,3.0) [3.0,3.5) [3.0,3.5)\n","[1303] [1.0,3.0) [3.0,3.5) [3.5,5.0] [1.0,3.0) [1.0,3.0) [3.5,5.0] [3.5,5.0]\n","[1310] [3.5,5.0] [3.0,3.5) [1.0,3.0) [3.5,5.0] [3.5,5.0] [3.0,3.5) [3.0,3.5)\n","[1317] [3.5,5.0] [1.0,3.0) [3.0,3.5) [3.0,3.5) [1.0,3.0) [3.5,5.0] [3.5,5.0]\n","[1324] [3.0,3.5) [3.5,5.0] [3.5,5.0] [3.5,5.0] [3.5,5.0] [3.5,5.0] [3.5,5.0]\n","[1331] [3.5,5.0] [3.0,3.5) [3.0,3.5) [3.0,3.5) [3.5,5.0] [3.5,5.0] [3.0,3.5)\n","[1338] [3.0,3.5) [3.5,5.0] [3.5,5.0] [3.5,5.0] [3.5,5.0] [1.0,3.0) [1.0,3.0)\n","[1345] [3.0,3.5) [3.0,3.5) [1.0,3.0) [1.0,3.0) [3.0,3.5) [3.5,5.0] [1.0,3.0)\n","[1352] [3.0,3.5) [3.0,3.5) [3.0,3.5) [1.0,3.0) [3.0,3.5) [3.0,3.5) [1.0,3.0)\n","[1359] [3.0,3.5) [3.5,5.0] [1.0,3.0) [3.0,3.5) [1.0,3.0) [3.0,3.5) [3.5,5.0]\n","[1366] [3.0,3.5) [1.0,3.0) [3.0,3.5) [1.0,3.0) [3.0,3.5) [3.5,5.0] [3.5,5.0]\n","[1373] [3.5,5.0] [3.5,5.0] [3.5,5.0] [3.5,5.0] [3.0,3.5) [3.5,5.0] [3.0,3.5)\n","[1380] [3.5,5.0] [3.5,5.0] [3.0,3.5) [3.0,3.5) [3.0,3.5) [3.0,3.5) [3.0,3.5)\n","[1387] [3.5,5.0] [3.5,5.0] [3.5,5.0] [3.0,3.5) [3.5,5.0] [3.5,5.0] [3.5,5.0]\n","[1394] [3.5,5.0] [3.5,5.0] [3.5,5.0] [3.0,3.5) [3.5,5.0] [3.5,5.0] [1.0,3.0)\n","[1401] [1.0,3.0) [1.0,3.0) [3.0,3.5) [1.0,3.0) [1.0,3.0) [1.0,3.0) [3.5,5.0]\n","[1408] [1.0,3.0) [3.5,5.0] [1.0,3.0) [3.5,5.0] [1.0,3.0) [3.0,3.5) [3.5,5.0]\n","[1415] [3.5,5.0] [1.0,3.0) [3.0,3.5) [3.5,5.0] [3.5,5.0] [3.5,5.0] [3.5,5.0]\n","[1422] [1.0,3.0) [3.0,3.5) [1.0,3.0) [3.0,3.5) [3.5,5.0] [1.0,3.0) [3.5,5.0]\n","[1429] [3.5,5.0] [3.5,5.0] [3.5,5.0] [3.5,5.0] [3.5,5.0] [3.5,5.0] [3.0,3.5)\n","[1436] [3.0,3.5) [3.0,3.5) [3.5,5.0] [3.0,3.5) [1.0,3.0) [3.5,5.0] [1.0,3.0)\n","[1443] [1.0,3.0) [3.0,3.5) [1.0,3.0) [1.0,3.0) [3.0,3.5) [3.5,5.0] [1.0,3.0)\n","[1450] [3.5,5.0] [3.0,3.5) [3.0,3.5) [1.0,3.0) [1.0,3.0) [3.0,3.5) [3.5,5.0]\n","[1457] [3.0,3.5) [3.5,5.0] [1.0,3.0) [3.0,3.5) [3.0,3.5) [3.0,3.5) [3.0,3.5)\n","[1464] [3.5,5.0] [3.5,5.0] [3.5,5.0] [3.0,3.5) [3.0,3.5) [3.0,3.5) [3.0,3.5)\n","[1471] [3.0,3.5) [3.5,5.0] [3.5,5.0] [3.5,5.0] [1.0,3.0) [3.0,3.5) [3.5,5.0]\n","[1478] [3.5,5.0] [3.5,5.0] [3.5,5.0] [3.0,3.5) [3.5,5.0] [3.0,3.5) [3.0,3.5)\n","[1485] [3.5,5.0] [3.5,5.0] [3.5,5.0] [3.5,5.0] [1.0,3.0) [3.5,5.0] [3.0,3.5)\n","[1492] [3.5,5.0] [1.0,3.0) [3.0,3.5) [3.0,3.5) [3.5,5.0] [3.0,3.5) [1.0,3.0)\n","[1499] [1.0,3.0) [3.0,3.5) [3.0,3.5) [3.0,3.5) [3.0,3.5) [3.0,3.5) [3.0,3.5)\n","[1506] [3.0,3.5) [3.5,5.0] [3.5,5.0] [3.5,5.0] [3.5,5.0] [3.5,5.0] [3.5,5.0]\n","[1513] [3.5,5.0] [3.0,3.5) [3.5,5.0] [3.5,5.0] [3.5,5.0] [3.5,5.0] [3.0,3.5)\n","[1520] [3.5,5.0] [3.5,5.0] [3.0,3.5) [3.5,5.0] [3.0,3.5) [1.0,3.0) [3.5,5.0]\n","[1527] [3.0,3.5) [3.5,5.0] [3.5,5.0] [3.5,5.0] [3.5,5.0] [3.0,3.5) [3.5,5.0]\n","[1534] [3.0,3.5) [3.5,5.0] [3.5,5.0] [3.5,5.0] [3.5,5.0] [3.5,5.0] [3.5,5.0]\n","[1541] [3.5,5.0] [1.0,3.0) [3.5,5.0] [3.5,5.0] [3.5,5.0] [3.5,5.0] [3.5,5.0]\n","[1548] [3.5,5.0] [3.0,3.5) [3.5,5.0] [3.0,3.5) [3.5,5.0] [3.5,5.0] [3.5,5.0]\n","[1555] [3.0,3.5) [3.5,5.0] [3.0,3.5) [3.5,5.0] [3.5,5.0] [3.0,3.5) [3.0,3.5)\n","[1562] [3.5,5.0] [3.0,3.5) [3.0,3.5) [3.0,3.5) [3.5,5.0] [3.5,5.0] [1.0,3.0)\n","[1569] [3.5,5.0] [3.5,5.0] [3.5,5.0] [1.0,3.0) [3.0,3.5) [3.0,3.5) [1.0,3.0)\n","[1576] [3.0,3.5) [3.0,3.5) [3.0,3.5) [3.0,3.5) [3.0,3.5) [1.0,3.0) [3.0,3.5)\n","[1583] [3.5,5.0] [3.5,5.0] [1.0,3.0) [1.0,3.0) [1.0,3.0) [1.0,3.0) [3.0,3.5)\n","[1590] [1.0,3.0) [3.0,3.5) [1.0,3.0) [3.0,3.5) [1.0,3.0) [3.0,3.5) [3.0,3.5)\n","[1597] [3.0,3.5) [3.5,5.0] [3.0,3.5) [3.5,5.0] [3.5,5.0] [3.5,5.0] [3.5,5.0]\n","[1604] [3.5,5.0] [3.0,3.5) [3.0,3.5) [3.0,3.5) [3.5,5.0] [3.0,3.5) [1.0,3.0)\n","[1611] [1.0,3.0) [1.0,3.0) [3.0,3.5) [3.0,3.5) [3.0,3.5) [3.0,3.5) [1.0,3.0)\n","[1618] [3.0,3.5) [3.0,3.5) [3.0,3.5) [1.0,3.0) [3.0,3.5) [1.0,3.0) [3.0,3.5)\n","[1625] [1.0,3.0) [3.0,3.5) [1.0,3.0) [1.0,3.0) [1.0,3.0) [1.0,3.0) [1.0,3.0)\n","[1632] [3.0,3.5) [3.0,3.5) [3.5,5.0] [3.0,3.5) [1.0,3.0) [3.0,3.5) [1.0,3.0)\n","[1639] [3.0,3.5) [3.0,3.5) [1.0,3.0) [3.0,3.5) [3.0,3.5) [3.5,5.0] [1.0,3.0)\n","[1646] [3.5,5.0] [3.5,5.0] [3.0,3.5) [3.5,5.0] [3.5,5.0] [3.0,3.5) [3.5,5.0]\n","[1653] [3.5,5.0] [3.0,3.5) [3.0,3.5) [3.5,5.0] [1.0,3.0) [3.0,3.5) [1.0,3.0)\n","[1660] [3.0,3.5) [3.0,3.5) [3.5,5.0] [3.0,3.5) [3.5,5.0] [3.0,3.5) [1.0,3.0)\n","[1667] [3.0,3.5) [3.0,3.5) [3.5,5.0] [1.0,3.0) [3.5,5.0] [3.5,5.0] [3.0,3.5)\n","[1674] [1.0,3.0) [1.0,3.0) [3.0,3.5) [3.5,5.0] [1.0,3.0) [3.0,3.5) [3.0,3.5)\n","[1681] [3.0,3.5) [3.0,3.5) [3.5,5.0] [3.5,5.0] [3.5,5.0] [3.5,5.0] [3.0,3.5)\n","[1688] [3.5,5.0] [3.5,5.0] [3.5,5.0] [3.5,5.0] [3.0,3.5) [3.5,5.0] [3.5,5.0]\n","[1695] [1.0,3.0) [1.0,3.0) [1.0,3.0) [3.0,3.5) [3.0,3.5) [3.5,5.0] [1.0,3.0)\n","[1702] [3.0,3.5) [3.5,5.0] [1.0,3.0) [1.0,3.0) [1.0,3.0) [1.0,3.0) [1.0,3.0)\n","[1709] [3.0,3.5) [3.0,3.5) [3.0,3.5) [3.0,3.5) [3.0,3.5) [3.0,3.5) [3.5,5.0]\n","[1716] [3.5,5.0] [3.5,5.0] [3.0,3.5) [3.5,5.0] [3.0,3.5) [1.0,3.0) [1.0,3.0)\n","[1723] [3.0,3.5) [3.0,3.5) [1.0,3.0) [3.0,3.5) [3.0,3.5) [3.0,3.5) [1.0,3.0)\n","[1730] [1.0,3.0) [1.0,3.0) [3.0,3.5) [3.5,5.0] [3.5,5.0] [1.0,3.0) [1.0,3.0)\n","[1737] [1.0,3.0) [3.0,3.5) [1.0,3.0) [3.5,5.0] [3.0,3.5) [1.0,3.0) [3.0,3.5)\n","[1744] [3.5,5.0] [3.5,5.0] [3.0,3.5) [3.0,3.5) [3.0,3.5) [3.5,5.0] [3.5,5.0]\n","[1751] [3.0,3.5) [3.0,3.5) [3.5,5.0] [1.0,3.0) [3.0,3.5) [3.0,3.5) [3.5,5.0]\n","[1758] [3.5,5.0] [3.0,3.5) [1.0,3.0) [1.0,3.0) [3.0,3.5) [3.0,3.5) [3.0,3.5)\n","[1765] [3.0,3.5) [3.5,5.0] [3.0,3.5) [3.5,5.0] [1.0,3.0) [1.0,3.0) [3.0,3.5)\n","[1772] [3.5,5.0] [3.5,5.0] [3.5,5.0] [3.0,3.5) [3.5,5.0] [3.5,5.0] [3.5,5.0]\n","[1779] [1.0,3.0) [3.5,5.0] [3.5,5.0] [3.0,3.5) [3.5,5.0] [3.5,5.0] [3.0,3.5)\n","[1786] [3.0,3.5) [3.0,3.5) [3.5,5.0] [3.0,3.5) [3.5,5.0] [3.5,5.0] [3.0,3.5)\n","[1793] [3.5,5.0] [3.0,3.5) [3.0,3.5)\n","Levels: [1.0,3.0) [3.0,3.5) [3.5,5.0]"]},"metadata":{}},{"output_type":"stream","name":"stdout","text":["[1] \"----------------\"\n","[1] \"Rating variable in three intervals of qualification: poor, medium, optimum \"\n"]},{"output_type":"display_data","data":{"text/html":["
[1.0,3.0)
449
[3.0,3.5)
644
[3.5,5.0]
702
\n"],"text/markdown":"[1.0,3.0)\n: 449[3.0,3.5)\n: 644[3.5,5.0]\n: 702\n\n","text/latex":"\\begin{description*}\n\\item[\\{{[}\\}1.0,3.0)] 449\n\\item[\\{{[}\\}3.0,3.5)] 644\n\\item[\\{{[}\\}3.5,5.0\\{{]}\\}] 702\n\\end{description*}\n","text/plain":["[1.0,3.0) [3.0,3.5) [3.5,5.0] \n"," 449 644 702 "]},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["perc_rate \n"," n missing distinct \n"," 1795 0 3 \n"," \n","Value [1.0,3.0) [3.0,3.5) [3.5,5.0]\n","Frequency 449 644 702\n","Proportion 0.250 0.359 0.391"]},"metadata":{}}],"source":["#recoding Rating variable in three intervals of qualification: \"poor\", \"medium\", \"optimum\"\n","library(Hmisc)\n","perc_rate <- cut2(choco$Rating, c(3, 3.5))\n","\n","perc_rate\n","\n","print(\"----------------\")\n","print(\"Rating variable in three intervals of qualification: poor, medium, optimum \")\n","#basic summary\n","summary(perc_rate)\n","\n","#more description\n","describe(perc_rate)\n","\n","#see also\n","#skim(perc_rate)\n"]},{"cell_type":"code","source":["#levels for this variable\n","levels(perc_rate)\n","\n","#new variable name\n","label(perc_rate) <- \"Chocolat qualification\"\n","\n","#change levels names\n","levels(perc_rate)[c(1, 2, 3)] <- c(\"poor\", \"medium\", \"optimum\")\n","levels(perc_rate)\n","\n"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":52},"id":"OhHoDXYWFG28","executionInfo":{"status":"ok","timestamp":1716799777437,"user_tz":-120,"elapsed":257,"user":{"displayName":"toni Monleon","userId":"05269440626861479105"}},"outputId":"d94bb664-a467-4f6f-91e4-e670b87c7c63"},"execution_count":56,"outputs":[{"output_type":"display_data","data":{"text/html":["\n","
  1. '[1.0,3.0)'
  2. '[3.0,3.5)'
  3. '[3.5,5.0]'
\n"],"text/markdown":"1. '[1.0,3.0)'\n2. '[3.0,3.5)'\n3. '[3.5,5.0]'\n\n\n","text/latex":"\\begin{enumerate*}\n\\item '{[}1.0,3.0)'\n\\item '{[}3.0,3.5)'\n\\item '{[}3.5,5.0{]}'\n\\end{enumerate*}\n","text/plain":["[1] \"[1.0,3.0)\" \"[3.0,3.5)\" \"[3.5,5.0]\""]},"metadata":{}},{"output_type":"display_data","data":{"text/html":["\n","
  1. 'poor'
  2. 'medium'
  3. 'optimum'
\n"],"text/markdown":"1. 'poor'\n2. 'medium'\n3. 'optimum'\n\n\n","text/latex":"\\begin{enumerate*}\n\\item 'poor'\n\\item 'medium'\n\\item 'optimum'\n\\end{enumerate*}\n","text/plain":["[1] \"poor\" \"medium\" \"optimum\""]},"metadata":{}}]},{"cell_type":"code","source":["#describe variable using a frequency table\n","describe(perc_rate)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":138},"id":"8otuklcAFRYt","executionInfo":{"status":"ok","timestamp":1716653630387,"user_tz":-120,"elapsed":233,"user":{"displayName":"toni Monleon","userId":"05269440626861479105"}},"outputId":"570e0191-5a03-45fd-bcce-7b6669f78ec6"},"execution_count":null,"outputs":[{"output_type":"display_data","data":{"text/plain":["perc_rate \n"," n missing distinct \n"," 1795 0 3 \n"," \n","Value [1.0,3.0) [3.0,3.5) [3.5,5.0]\n","Frequency 449 644 702\n","Proportion 0.250 0.359 0.391"]},"metadata":{}}]},{"cell_type":"markdown","metadata":{"id":"omcJnm2MrKI4"},"source":["Bivariate descriptive analysis between choco$Bean.Type by perc_rate"]},{"cell_type":"code","execution_count":57,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":780},"executionInfo":{"elapsed":254,"status":"ok","timestamp":1716799789070,"user":{"displayName":"toni Monleon","userId":"05269440626861479105"},"user_tz":-120},"id":"OBlgYKyYrJZP","outputId":"94cfd545-5f19-4049-e919-06474961522f"},"outputs":[{"output_type":"display_data","data":{"text/plain":[" perc_rate\n"," poor medium optimum\n"," 0 0 1\n","   257 328 302\n"," Amazon 0 1 0\n"," Amazon mix 0 0 2\n"," Amazon, ICS 0 0 2\n"," Beniano 0 1 2\n"," Blend 5 12 24\n"," Blend-Forastero,Criollo 0 0 1\n"," CCN51 0 0 1\n"," Criollo 33 52 68\n"," Criollo (Amarru) 0 1 1\n"," Criollo (Ocumare 61) 0 2 0\n"," Criollo (Ocumare 67) 0 0 1\n"," Criollo (Ocumare 77) 0 0 1\n"," Criollo (Ocumare) 0 1 0\n"," Criollo (Porcelana) 1 5 4\n"," Criollo (Wild) 0 0 1\n"," Criollo, + 0 0 1\n"," Criollo, Forastero 0 0 2\n"," Criollo, Trinitario 6 14 19\n"," EET 0 0 3\n"," Forastero 27 28 32\n"," Forastero (Amelonado) 0 0 1\n"," Forastero (Arriba) 18 10 9\n"," Forastero (Arriba) ASS 2 3 1\n"," Forastero (Arriba) ASSS 0 0 1\n"," Forastero (Catongo) 0 1 1\n"," Forastero (Nacional) 9 18 25\n"," Forastero (Parazinho) 1 0 7\n"," Forastero, Trinitario 0 1 0\n"," Forastero(Arriba, CCN) 0 1 0\n"," Matina 0 1 2\n"," Nacional 1 0 1\n"," Nacional (Arriba) 1 1 1\n"," Trinitario 85 157 177\n"," Trinitario (85% Criollo) 0 0 2\n"," Trinitario (Amelonado) 0 1 0\n"," Trinitario (Scavina) 0 0 1\n"," Trinitario, Criollo 3 3 3\n"," Trinitario, Forastero 0 2 0\n"," Trinitario, Nacional 0 0 1\n"," Trinitario, TCGA 0 0 1"]},"metadata":{}}],"source":["## Both variables are categorical: contingency tables\n","\n"," #Very simple contingency tables\n","\n","\n","with(choco, table(choco$Bean.Type, perc_rate)) #frequencies\n","\n"]},{"cell_type":"code","execution_count":58,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":780},"executionInfo":{"elapsed":249,"status":"ok","timestamp":1716799818464,"user":{"displayName":"toni Monleon","userId":"05269440626861479105"},"user_tz":-120},"id":"77HFn3t6rwXz","outputId":"7024b3e2-2f12-44fe-dd66-a4132edbda0e"},"outputs":[{"output_type":"display_data","data":{"text/plain":[" perc_rate\n"," poor medium optimum\n"," 0.0 0.0 100.0\n","   29.0 37.0 34.0\n"," Amazon 0.0 100.0 0.0\n"," Amazon mix 0.0 0.0 100.0\n"," Amazon, ICS 0.0 0.0 100.0\n"," Beniano 0.0 33.3 66.7\n"," Blend 12.2 29.3 58.5\n"," Blend-Forastero,Criollo 0.0 0.0 100.0\n"," CCN51 0.0 0.0 100.0\n"," Criollo 21.6 34.0 44.4\n"," Criollo (Amarru) 0.0 50.0 50.0\n"," Criollo (Ocumare 61) 0.0 100.0 0.0\n"," Criollo (Ocumare 67) 0.0 0.0 100.0\n"," Criollo (Ocumare 77) 0.0 0.0 100.0\n"," Criollo (Ocumare) 0.0 100.0 0.0\n"," Criollo (Porcelana) 10.0 50.0 40.0\n"," Criollo (Wild) 0.0 0.0 100.0\n"," Criollo, + 0.0 0.0 100.0\n"," Criollo, Forastero 0.0 0.0 100.0\n"," Criollo, Trinitario 15.4 35.9 48.7\n"," EET 0.0 0.0 100.0\n"," Forastero 31.0 32.2 36.8\n"," Forastero (Amelonado) 0.0 0.0 100.0\n"," Forastero (Arriba) 48.6 27.0 24.3\n"," Forastero (Arriba) ASS 33.3 50.0 16.7\n"," Forastero (Arriba) ASSS 0.0 0.0 100.0\n"," Forastero (Catongo) 0.0 50.0 50.0\n"," Forastero (Nacional) 17.3 34.6 48.1\n"," Forastero (Parazinho) 12.5 0.0 87.5\n"," Forastero, Trinitario 0.0 100.0 0.0\n"," Forastero(Arriba, CCN) 0.0 100.0 0.0\n"," Matina 0.0 33.3 66.7\n"," Nacional 50.0 0.0 50.0\n"," Nacional (Arriba) 33.3 33.3 33.3\n"," Trinitario 20.3 37.5 42.2\n"," Trinitario (85% Criollo) 0.0 0.0 100.0\n"," Trinitario (Amelonado) 0.0 100.0 0.0\n"," Trinitario (Scavina) 0.0 0.0 100.0\n"," Trinitario, Criollo 33.3 33.3 33.3\n"," Trinitario, Forastero 0.0 100.0 0.0\n"," Trinitario, Nacional 0.0 0.0 100.0\n"," Trinitario, TCGA 0.0 0.0 100.0"]},"metadata":{}}],"source":[" #Relative (conditional) frequencies\n","\n","#prop.table(tab)\n","\n","tab <- with(choco, table(choco$Bean.Type, perc_rate))\n","#tab\n","round(prop.table(tab, 1)*100, 1)"]},{"cell_type":"code","execution_count":59,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":1000},"executionInfo":{"elapsed":257,"status":"ok","timestamp":1716799824066,"user":{"displayName":"toni Monleon","userId":"05269440626861479105"},"user_tz":-120},"id":"K8zr0poesBoc","outputId":"c6962aa1-8e58-45a7-c325-bc81feb3cc23"},"outputs":[{"output_type":"display_data","data":{"text/plain":[" ----------------------------------------------------------- \n"," ------------perc_rate------------ \n"," Bean_Type poor medium optimum Total \n"," ----------------------------------------------------------- \n"," 0 0 1 1 \n"," 0.0 0.0 100.0 100.0 \n"," \n","   257 328 302 887 \n"," 29.0 37.0 34.0 100.0 \n"," \n"," Amazon 0 1 0 1 \n"," 0.0 100.0 0.0 100.0 \n"," \n"," Amazon mix 0 0 2 2 \n"," 0.0 0.0 100.0 100.0 \n"," \n"," Amazon, ICS 0 0 2 2 \n"," 0.0 0.0 100.0 100.0 \n"," \n"," Beniano 0 1 2 3 \n"," 0.0 33.3 66.7 100.0 \n"," \n"," Blend 5 12 24 41 \n"," 12.2 29.3 58.5 100.0 \n"," \n"," Blend-Forastero,Criollo 0 0 1 1 \n"," 0.0 0.0 100.0 100.0 \n"," \n"," CCN51 0 0 1 1 \n"," 0.0 0.0 100.0 100.0 \n"," \n"," Criollo 33 52 68 153 \n"," 21.6 34.0 44.4 100.0 \n"," \n"," Criollo (Amarru) 0 1 1 2 \n"," 0.0 50.0 50.0 100.0 \n"," \n"," Criollo (Ocumare 61) 0 2 0 2 \n"," 0.0 100.0 0.0 100.0 \n"," \n"," Criollo (Ocumare 67) 0 0 1 1 \n"," 0.0 0.0 100.0 100.0 \n"," \n"," Criollo (Ocumare 77) 0 0 1 1 \n"," 0.0 0.0 100.0 100.0 \n"," \n"," Criollo (Ocumare) 0 1 0 1 \n"," 0.0 100.0 0.0 100.0 \n"," \n"," Criollo (Porcelana) 1 5 4 10 \n"," 10.0 50.0 40.0 100.0 \n"," \n"," Criollo (Wild) 0 0 1 1 \n"," 0.0 0.0 100.0 100.0 \n"," \n"," Criollo, + 0 0 1 1 \n"," 0.0 0.0 100.0 100.0 \n"," \n"," Criollo, Forastero 0 0 2 2 \n"," 0.0 0.0 100.0 100.0 \n"," \n"," Criollo, Trinitario 6 14 19 39 \n"," 15.4 35.9 48.7 100.0 \n"," \n"," EET 0 0 3 3 \n"," 0.0 0.0 100.0 100.0 \n"," \n"," Forastero 27 28 32 87 \n"," 31.0 32.2 36.8 100.0 \n"," \n"," Forastero (Amelonado) 0 0 1 1 \n"," 0.0 0.0 100.0 100.0 \n"," \n"," Forastero (Arriba) 18 10 9 37 \n"," 48.6 27.0 24.3 100.0 \n"," \n"," Forastero (Arriba) ASS 2 3 1 6 \n"," 33.3 50.0 16.7 100.0 \n"," \n"," Forastero (Arriba) ASSS 0 0 1 1 \n"," 0.0 0.0 100.0 100.0 \n"," \n"," Forastero (Catongo) 0 1 1 2 \n"," 0.0 50.0 50.0 100.0 \n"," \n"," Forastero (Nacional) 9 18 25 52 \n"," 17.3 34.6 48.1 100.0 \n"," \n"," Forastero (Parazinho) 1 0 7 8 \n"," 12.5 0.0 87.5 100.0 \n"," \n"," Forastero, Trinitario 0 1 0 1 \n"," 0.0 100.0 0.0 100.0 \n"," \n"," Forastero(Arriba, CCN) 0 1 0 1 \n"," 0.0 100.0 0.0 100.0 \n"," \n"," Matina 0 1 2 3 \n"," 0.0 33.3 66.7 100.0 \n"," \n"," Nacional 1 0 1 2 \n"," 50.0 0.0 50.0 100.0 \n"," \n"," Nacional (Arriba) 1 1 1 3 \n"," 33.3 33.3 33.3 100.0 \n"," \n"," Trinitario 85 157 177 419 \n"," 20.3 37.5 42.2 100.0 \n"," \n"," Trinitario (85% Criollo) 0 0 2 2 \n"," 0.0 0.0 100.0 100.0 \n"," \n"," Trinitario (Amelonado) 0 1 0 1 \n"," 0.0 100.0 0.0 100.0 \n"," \n"," Trinitario (Scavina) 0 0 1 1 \n"," 0.0 0.0 100.0 100.0 \n"," \n"," Trinitario, Criollo 3 3 3 9 \n"," 33.3 33.3 33.3 100.0 \n"," \n"," Trinitario, Forastero 0 2 0 2 \n"," 0.0 100.0 0.0 100.0 \n"," \n"," Trinitario, Nacional 0 0 1 1 \n"," 0.0 0.0 100.0 100.0 \n"," \n"," Trinitario, TCGA 0 0 1 1 \n"," 0.0 0.0 100.0 100.0 \n"," \n"," \n"," Total 449 644 702 1795 \n"," 25.0 35.9 39.1 100.0 \n"," ----------------------------------------------------------- "]},"metadata":{}}],"source":[" #The best is use Function stat.table (Epi)\n","\n","#choco$Bean.Type, perc_rate\n","\n","stat.table(list(Bean_Type = choco$Bean.Type, perc_rate = perc_rate), list(N = count(),\n"," \"%\" = percent(perc_rate)), data = choco, margins = T)"]},{"cell_type":"code","execution_count":60,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":1000},"executionInfo":{"elapsed":242,"status":"ok","timestamp":1716799830657,"user":{"displayName":"toni Monleon","userId":"05269440626861479105"},"user_tz":-120},"id":"VPHXVGY0tL7W","outputId":"a333dba2-0d13-4651-ac29-aa29066e2fcf"},"outputs":[{"output_type":"display_data","data":{"text/plain":[" Cell Contents \n","|-------------------------|\n","| Count | \n","| Row Percent | \n","|-------------------------|\n","\n","============================================================\n"," % rate\n","Bean Type poor medium optimum Total\n","------------------------------------------------------------\n"," 0 0 1 1 \n"," 0.0% 0.0% 100.0% 0.1%\n","------------------------------------------------------------\n","  257 328 302 887 \n"," 29.0% 37.0% 34.0% 49.4%\n","------------------------------------------------------------\n","Amazon 0 1 0 1 \n"," 0.0% 100.0% 0.0% 0.1%\n","------------------------------------------------------------\n","Amazon mix 0 0 2 2 \n"," 0.0% 0.0% 100.0% 0.1%\n","------------------------------------------------------------\n","Amazon, ICS 0 0 2 2 \n"," 0.0% 0.0% 100.0% 0.1%\n","------------------------------------------------------------\n","Beniano 0 1 2 3 \n"," 0.0% 33.3% 66.7% 0.2%\n","------------------------------------------------------------\n","Blend 5 12 24 41 \n"," 12.2% 29.3% 58.5% 2.3%\n","------------------------------------------------------------\n","Blend-Forastero,Criollo 0 0 1 1 \n"," 0.0% 0.0% 100.0% 0.1%\n","------------------------------------------------------------\n","CCN51 0 0 1 1 \n"," 0.0% 0.0% 100.0% 0.1%\n","------------------------------------------------------------\n","Criollo 33 52 68 153 \n"," 21.6% 34.0% 44.4% 8.5%\n","------------------------------------------------------------\n","Criollo (Amarru) 0 1 1 2 \n"," 0.0% 50.0% 50.0% 0.1%\n","------------------------------------------------------------\n","Criollo (Ocumare 61) 0 2 0 2 \n"," 0.0% 100.0% 0.0% 0.1%\n","------------------------------------------------------------\n","Criollo (Ocumare 67) 0 0 1 1 \n"," 0.0% 0.0% 100.0% 0.1%\n","------------------------------------------------------------\n","Criollo (Ocumare 77) 0 0 1 1 \n"," 0.0% 0.0% 100.0% 0.1%\n","------------------------------------------------------------\n","Criollo (Ocumare) 0 1 0 1 \n"," 0.0% 100.0% 0.0% 0.1%\n","------------------------------------------------------------\n","Criollo (Porcelana) 1 5 4 10 \n"," 10.0% 50.0% 40.0% 0.6%\n","------------------------------------------------------------\n","Criollo (Wild) 0 0 1 1 \n"," 0.0% 0.0% 100.0% 0.1%\n","------------------------------------------------------------\n","Criollo, + 0 0 1 1 \n"," 0.0% 0.0% 100.0% 0.1%\n","------------------------------------------------------------\n","Criollo, Forastero 0 0 2 2 \n"," 0.0% 0.0% 100.0% 0.1%\n","------------------------------------------------------------\n","Criollo, Trinitario 6 14 19 39 \n"," 15.4% 35.9% 48.7% 2.2%\n","------------------------------------------------------------\n","EET 0 0 3 3 \n"," 0.0% 0.0% 100.0% 0.2%\n","------------------------------------------------------------\n","Forastero 27 28 32 87 \n"," 31.0% 32.2% 36.8% 4.8%\n","------------------------------------------------------------\n","Forastero (Amelonado) 0 0 1 1 \n"," 0.0% 0.0% 100.0% 0.1%\n","------------------------------------------------------------\n","Forastero (Arriba) 18 10 9 37 \n"," 48.6% 27.0% 24.3% 2.1%\n","------------------------------------------------------------\n","Forastero (Arriba) ASS 2 3 1 6 \n"," 33.3% 50.0% 16.7% 0.3%\n","------------------------------------------------------------\n","Forastero (Arriba) ASSS 0 0 1 1 \n"," 0.0% 0.0% 100.0% 0.1%\n","------------------------------------------------------------\n","Forastero (Catongo) 0 1 1 2 \n"," 0.0% 50.0% 50.0% 0.1%\n","------------------------------------------------------------\n","Forastero (Nacional) 9 18 25 52 \n"," 17.3% 34.6% 48.1% 2.9%\n","------------------------------------------------------------\n","Forastero (Parazinho) 1 0 7 8 \n"," 12.5% 0.0% 87.5% 0.4%\n","------------------------------------------------------------\n","Forastero, Trinitario 0 1 0 1 \n"," 0.0% 100.0% 0.0% 0.1%\n","------------------------------------------------------------\n","Forastero(Arriba, CCN) 0 1 0 1 \n"," 0.0% 100.0% 0.0% 0.1%\n","------------------------------------------------------------\n","Matina 0 1 2 3 \n"," 0.0% 33.3% 66.7% 0.2%\n","------------------------------------------------------------\n","Nacional 1 0 1 2 \n"," 50.0% 0.0% 50.0% 0.1%\n","------------------------------------------------------------\n","Nacional (Arriba) 1 1 1 3 \n"," 33.3% 33.3% 33.3% 0.2%\n","------------------------------------------------------------\n","Trinitario 85 157 177 419 \n"," 20.3% 37.5% 42.2% 23.3%\n","------------------------------------------------------------\n","Trinitario (85% Criollo) 0 0 2 2 \n"," 0.0% 0.0% 100.0% 0.1%\n","------------------------------------------------------------\n","Trinitario (Amelonado) 0 1 0 1 \n"," 0.0% 100.0% 0.0% 0.1%\n","------------------------------------------------------------\n","Trinitario (Scavina) 0 0 1 1 \n"," 0.0% 0.0% 100.0% 0.1%\n","------------------------------------------------------------\n","Trinitario, Criollo 3 3 3 9 \n"," 33.3% 33.3% 33.3% 0.5%\n","------------------------------------------------------------\n","Trinitario, Forastero 0 2 0 2 \n"," 0.0% 100.0% 0.0% 0.1%\n","------------------------------------------------------------\n","Trinitario, Nacional 0 0 1 1 \n"," 0.0% 0.0% 100.0% 0.1%\n","------------------------------------------------------------\n","Trinitario, TCGA 0 0 1 1 \n"," 0.0% 0.0% 100.0% 0.1%\n","------------------------------------------------------------\n","Total 449 644 702 1795 \n","============================================================"]},"metadata":{}}],"source":[" #Last possibility is use Function CrossTable (descr)\n"," #choco$Bean.Type, perc_rate\n","with(choco, CrossTable(choco$Bean.Type, perc_rate, prop.t = F, prop.c = F,\n"," prop.chisq = F, format = \"SPSS\", dnn = c(\"Bean Type\", \"% rate\")))\n"]},{"cell_type":"markdown","metadata":{"id":"XArPss52tq29"},"source":["## Tables for numeric variables vs. a categorical one\n","\n","cross choco$Bean.Type and choco$Cocoa.Percent"]},{"cell_type":"code","execution_count":61,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":156},"executionInfo":{"elapsed":285,"status":"ok","timestamp":1716799837919,"user":{"displayName":"toni Monleon","userId":"05269440626861479105"},"user_tz":-120},"id":"yM6KyJOpuAWc","outputId":"c9e2c023-0172-47e6-b896-af19b442d9f3"},"outputs":[{"output_type":"display_data","data":{"text/html":["
1
70
 
<NA>
Amazon
70
Amazon mix
74
Amazon, ICS
67.5
Beniano
71
Blend
71.390243902439
Blend-Forastero,Criollo
70
CCN51
65
Criollo
<NA>
Criollo (Amarru)
70
Criollo (Ocumare 61)
73.5
Criollo (Ocumare 67)
70
Criollo (Ocumare 77)
70
Criollo (Ocumare)
80
Criollo (Porcelana)
70.3
Criollo (Wild)
68
Criollo, +
65
Criollo, Forastero
76.5
Criollo, Trinitario
72.2051282051282
EET
71.3333333333333
Forastero
72.0574712643678
Forastero (Amelonado)
70
Forastero (Arriba)
73.1351351351351
Forastero (Arriba) ASS
68.6666666666667
Forastero (Arriba) ASSS
70
Forastero (Catongo)
72.5
Forastero (Nacional)
73.0192307692308
Forastero (Parazinho)
68.75
Forastero, Trinitario
70
Forastero(Arriba, CCN)
75
Matina
74
Nacional
71
Nacional (Arriba)
73.3333333333333
Trinitario
<NA>
Trinitario (85% Criollo)
70
Trinitario (Amelonado)
70
Trinitario (Scavina)
70
Trinitario, Criollo
68.3333333333333
Trinitario, Forastero
70.5
Trinitario, Nacional
70
Trinitario, TCGA
72
\n"],"text/markdown":"1\n: 70 \n: <NA>Amazon\n: 70Amazon mix\n: 74Amazon, ICS\n: 67.5Beniano\n: 71Blend\n: 71.390243902439Blend-Forastero,Criollo\n: 70CCN51\n: 65Criollo\n: <NA>Criollo (Amarru)\n: 70Criollo (Ocumare 61)\n: 73.5Criollo (Ocumare 67)\n: 70Criollo (Ocumare 77)\n: 70Criollo (Ocumare)\n: 80Criollo (Porcelana)\n: 70.3Criollo (Wild)\n: 68Criollo, +\n: 65Criollo, Forastero\n: 76.5Criollo, Trinitario\n: 72.2051282051282EET\n: 71.3333333333333Forastero\n: 72.0574712643678Forastero (Amelonado)\n: 70Forastero (Arriba)\n: 73.1351351351351Forastero (Arriba) ASS\n: 68.6666666666667Forastero (Arriba) ASSS\n: 70Forastero (Catongo)\n: 72.5Forastero (Nacional)\n: 73.0192307692308Forastero (Parazinho)\n: 68.75Forastero, Trinitario\n: 70Forastero(Arriba, CCN)\n: 75Matina\n: 74Nacional\n: 71Nacional (Arriba)\n: 73.3333333333333Trinitario\n: <NA>Trinitario (85% Criollo)\n: 70Trinitario (Amelonado)\n: 70Trinitario (Scavina)\n: 70Trinitario, Criollo\n: 68.3333333333333Trinitario, Forastero\n: 70.5Trinitario, Nacional\n: 70Trinitario, TCGA\n: 72\n\n","text/latex":"\\begin{description*}\n\\item[1] 70\n\\item[ ] \n\\item[Amazon] 70\n\\item[Amazon mix] 74\n\\item[Amazon, ICS] 67.5\n\\item[Beniano] 71\n\\item[Blend] 71.390243902439\n\\item[Blend-Forastero,Criollo] 70\n\\item[CCN51] 65\n\\item[Criollo] \n\\item[Criollo (Amarru)] 70\n\\item[Criollo (Ocumare 61)] 73.5\n\\item[Criollo (Ocumare 67)] 70\n\\item[Criollo (Ocumare 77)] 70\n\\item[Criollo (Ocumare)] 80\n\\item[Criollo (Porcelana)] 70.3\n\\item[Criollo (Wild)] 68\n\\item[Criollo, +] 65\n\\item[Criollo, Forastero] 76.5\n\\item[Criollo, Trinitario] 72.2051282051282\n\\item[EET] 71.3333333333333\n\\item[Forastero] 72.0574712643678\n\\item[Forastero (Amelonado)] 70\n\\item[Forastero (Arriba)] 73.1351351351351\n\\item[Forastero (Arriba) ASS] 68.6666666666667\n\\item[Forastero (Arriba) ASSS] 70\n\\item[Forastero (Catongo)] 72.5\n\\item[Forastero (Nacional)] 73.0192307692308\n\\item[Forastero (Parazinho)] 68.75\n\\item[Forastero, Trinitario] 70\n\\item[Forastero(Arriba, CCN)] 75\n\\item[Matina] 74\n\\item[Nacional] 71\n\\item[Nacional (Arriba)] 73.3333333333333\n\\item[Trinitario] \n\\item[Trinitario (85\\textbackslash{}\\% Criollo)] 70\n\\item[Trinitario (Amelonado)] 70\n\\item[Trinitario (Scavina)] 70\n\\item[Trinitario, Criollo] 68.3333333333333\n\\item[Trinitario, Forastero] 70.5\n\\item[Trinitario, Nacional] 70\n\\item[Trinitario, TCGA] 72\n\\end{description*}\n","text/plain":["   Amazon \n"," 70.00000 NA 70.00000 \n"," Amazon mix Amazon, ICS Beniano \n"," 74.00000 67.50000 71.00000 \n"," Blend Blend-Forastero,Criollo CCN51 \n"," 71.39024 70.00000 65.00000 \n"," Criollo Criollo (Amarru) Criollo (Ocumare 61) \n"," NA 70.00000 73.50000 \n"," Criollo (Ocumare 67) Criollo (Ocumare 77) Criollo (Ocumare) \n"," 70.00000 70.00000 80.00000 \n"," Criollo (Porcelana) Criollo (Wild) Criollo, + \n"," 70.30000 68.00000 65.00000 \n"," Criollo, Forastero Criollo, Trinitario EET \n"," 76.50000 72.20513 71.33333 \n"," Forastero Forastero (Amelonado) Forastero (Arriba) \n"," 72.05747 70.00000 73.13514 \n"," Forastero (Arriba) ASS Forastero (Arriba) ASSS Forastero (Catongo) \n"," 68.66667 70.00000 72.50000 \n"," Forastero (Nacional) Forastero (Parazinho) Forastero, Trinitario \n"," 73.01923 68.75000 70.00000 \n"," Forastero(Arriba, CCN) Matina Nacional \n"," 75.00000 74.00000 71.00000 \n"," Nacional (Arriba) Trinitario Trinitario (85% Criollo) \n"," 73.33333 NA 70.00000 \n"," Trinitario (Amelonado) Trinitario (Scavina) Trinitario, Criollo \n"," 70.00000 70.00000 68.33333 \n"," Trinitario, Forastero Trinitario, Nacional Trinitario, TCGA \n"," 70.50000 70.00000 72.00000 "]},"metadata":{}}],"source":["#choco$Bean.Type\n","#choco$Cocoa.Percent\n","\n","#compute mean for each Bean.Type using Function tapply\n","\n","with(choco, tapply(choco$Cocoa.Percent, choco$Bean.Type, mean))"]},{"cell_type":"code","execution_count":62,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":1000},"executionInfo":{"elapsed":256,"status":"ok","timestamp":1716799846391,"user":{"displayName":"toni Monleon","userId":"05269440626861479105"},"user_tz":-120},"id":"uz_I3YKMuE2N","outputId":"ba7eb433-88dd-4b15-ed55-970832bd17ff"},"outputs":[{"output_type":"display_data","data":{"text/plain":["[[1]]\n"," Min. 1st Qu. Median Mean 3rd Qu. Max. \n"," 70 70 70 70 70 70 \n","\n","$` `\n"," Min. 1st Qu. Median Mean 3rd Qu. Max. NA's \n"," 42.00 70.00 70.00 71.59 75.00 100.00 15 \n","\n","$Amazon\n"," Min. 1st Qu. Median Mean 3rd Qu. Max. \n"," 70 70 70 70 70 70 \n","\n","$`Amazon mix`\n"," Min. 1st Qu. Median Mean 3rd Qu. Max. \n"," 74 74 74 74 74 74 \n","\n","$`Amazon, ICS`\n"," Min. 1st Qu. Median Mean 3rd Qu. Max. \n"," 67.00 67.25 67.50 67.50 67.75 68.00 \n","\n","$Beniano\n"," Min. 1st Qu. Median Mean 3rd Qu. Max. \n"," 67.0 69.5 72.0 71.0 73.0 74.0 \n","\n","$Blend\n"," Min. 1st Qu. Median Mean 3rd Qu. Max. \n"," 55.00 70.00 70.00 71.39 74.00 91.00 \n","\n","$`Blend-Forastero,Criollo`\n"," Min. 1st Qu. Median Mean 3rd Qu. Max. \n"," 70 70 70 70 70 70 \n","\n","$CCN51\n"," Min. 1st Qu. Median Mean 3rd Qu. Max. \n"," 65 65 65 65 65 65 \n","\n","$Criollo\n"," Min. 1st Qu. Median Mean 3rd Qu. Max. NA's \n"," 58.00 70.00 71.00 72.07 75.00 100.00 4 \n","\n","$`Criollo (Amarru)`\n"," Min. 1st Qu. Median Mean 3rd Qu. Max. \n"," 70 70 70 70 70 70 \n","\n","$`Criollo (Ocumare 61)`\n"," Min. 1st Qu. Median Mean 3rd Qu. Max. \n"," 72.00 72.75 73.50 73.50 74.25 75.00 \n","\n","$`Criollo (Ocumare 67)`\n"," Min. 1st Qu. Median Mean 3rd Qu. Max. \n"," 70 70 70 70 70 70 \n","\n","$`Criollo (Ocumare 77)`\n"," Min. 1st Qu. Median Mean 3rd Qu. Max. \n"," 70 70 70 70 70 70 \n","\n","$`Criollo (Ocumare)`\n"," Min. 1st Qu. Median Mean 3rd Qu. Max. \n"," 80 80 80 80 80 80 \n","\n","$`Criollo (Porcelana)`\n"," Min. 1st Qu. Median Mean 3rd Qu. Max. \n"," 64.00 69.25 70.00 70.30 73.50 75.00 \n","\n","$`Criollo (Wild)`\n"," Min. 1st Qu. Median Mean 3rd Qu. Max. \n"," 68 68 68 68 68 68 \n","\n","$`Criollo, +`\n"," Min. 1st Qu. Median Mean 3rd Qu. Max. \n"," 65 65 65 65 65 65 \n","\n","$`Criollo, Forastero`\n"," Min. 1st Qu. Median Mean 3rd Qu. Max. \n"," 75.00 75.75 76.50 76.50 77.25 78.00 \n","\n","$`Criollo, Trinitario`\n"," Min. 1st Qu. Median Mean 3rd Qu. Max. \n"," 61.00 70.00 70.00 72.21 75.00 85.00 \n","\n","$EET\n"," Min. 1st Qu. Median Mean 3rd Qu. Max. \n"," 67.00 68.50 70.00 71.33 73.50 77.00 \n","\n","$Forastero\n"," Min. 1st Qu. Median Mean 3rd Qu. Max. \n"," 57.00 70.00 70.00 72.06 75.00 100.00 \n","\n","$`Forastero (Amelonado)`\n"," Min. 1st Qu. Median Mean 3rd Qu. Max. \n"," 70 70 70 70 70 70 \n","\n","$`Forastero (Arriba)`\n"," Min. 1st Qu. Median Mean 3rd Qu. Max. \n"," 55.00 65.00 72.00 73.14 77.00 100.00 \n","\n","$`Forastero (Arriba) ASS`\n"," Min. 1st Qu. Median Mean 3rd Qu. Max. \n"," 58.00 59.50 68.00 68.67 78.00 80.00 \n","\n","$`Forastero (Arriba) ASSS`\n"," Min. 1st Qu. Median Mean 3rd Qu. Max. \n"," 70 70 70 70 70 70 \n","\n","$`Forastero (Catongo)`\n"," Min. 1st Qu. Median Mean 3rd Qu. Max. \n"," 70.00 71.25 72.50 72.50 73.75 75.00 \n","\n","$`Forastero (Nacional)`\n"," Min. 1st Qu. Median Mean 3rd Qu. Max. \n"," 53.00 70.00 70.00 73.02 75.00 100.00 \n","\n","$`Forastero (Parazinho)`\n"," Min. 1st Qu. Median Mean 3rd Qu. Max. \n"," 50.00 67.50 70.00 68.75 71.25 85.00 \n","\n","$`Forastero, Trinitario`\n"," Min. 1st Qu. Median Mean 3rd Qu. Max. \n"," 70 70 70 70 70 70 \n","\n","$`Forastero(Arriba, CCN)`\n"," Min. 1st Qu. Median Mean 3rd Qu. Max. \n"," 75 75 75 75 75 75 \n","\n","$Matina\n"," Min. 1st Qu. Median Mean 3rd Qu. Max. \n"," 70 70 70 74 76 82 \n","\n","$Nacional\n"," Min. 1st Qu. Median Mean 3rd Qu. Max. \n"," 70.0 70.5 71.0 71.0 71.5 72.0 \n","\n","$`Nacional (Arriba)`\n"," Min. 1st Qu. Median Mean 3rd Qu. Max. \n"," 70.00 72.50 75.00 73.33 75.00 75.00 \n","\n","$Trinitario\n"," Min. 1st Qu. Median Mean 3rd Qu. Max. NA's \n"," 55.00 70.00 70.00 71.75 75.00 100.00 1 \n","\n","$`Trinitario (85% Criollo)`\n"," Min. 1st Qu. Median Mean 3rd Qu. Max. \n"," 70 70 70 70 70 70 \n","\n","$`Trinitario (Amelonado)`\n"," Min. 1st Qu. Median Mean 3rd Qu. Max. \n"," 70 70 70 70 70 70 \n","\n","$`Trinitario (Scavina)`\n"," Min. 1st Qu. Median Mean 3rd Qu. Max. \n"," 70 70 70 70 70 70 \n","\n","$`Trinitario, Criollo`\n"," Min. 1st Qu. Median Mean 3rd Qu. Max. \n"," 65.00 65.00 70.00 68.33 70.00 74.00 \n","\n","$`Trinitario, Forastero`\n"," Min. 1st Qu. Median Mean 3rd Qu. Max. \n"," 66.00 68.25 70.50 70.50 72.75 75.00 \n","\n","$`Trinitario, Nacional`\n"," Min. 1st Qu. Median Mean 3rd Qu. Max. \n"," 70 70 70 70 70 70 \n","\n","$`Trinitario, TCGA`\n"," Min. 1st Qu. Median Mean 3rd Qu. Max. \n"," 72 72 72 72 72 72 \n"]},"metadata":{}}],"source":["#compute summary for each Bean.Type using Function tapply\n","with(choco, tapply(choco$Cocoa.Percent, choco$Bean.Type, summary))"]},{"cell_type":"code","execution_count":63,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":1000},"executionInfo":{"elapsed":302,"status":"ok","timestamp":1716799860894,"user":{"displayName":"toni Monleon","userId":"05269440626861479105"},"user_tz":-120},"id":"WNDCryfPudZk","outputId":"f3cb64b5-44ba-42a4-a800-2e5085554f35"},"outputs":[{"output_type":"display_data","data":{"text/plain":["choco$Bean.Type: \n","[1] 70\n","------------------------------------------------------------ \n","choco$Bean.Type:  \n","[1] NA\n","------------------------------------------------------------ \n","choco$Bean.Type: Amazon\n","[1] 70\n","------------------------------------------------------------ \n","choco$Bean.Type: Amazon mix\n","[1] 74\n","------------------------------------------------------------ \n","choco$Bean.Type: Amazon, ICS\n","[1] 67.5\n","------------------------------------------------------------ \n","choco$Bean.Type: Beniano\n","[1] 71\n","------------------------------------------------------------ \n","choco$Bean.Type: Blend\n","[1] 71.39024\n","------------------------------------------------------------ \n","choco$Bean.Type: Blend-Forastero,Criollo\n","[1] 70\n","------------------------------------------------------------ \n","choco$Bean.Type: CCN51\n","[1] 65\n","------------------------------------------------------------ \n","choco$Bean.Type: Criollo\n","[1] NA\n","------------------------------------------------------------ \n","choco$Bean.Type: Criollo (Amarru)\n","[1] 70\n","------------------------------------------------------------ \n","choco$Bean.Type: Criollo (Ocumare 61)\n","[1] 73.5\n","------------------------------------------------------------ \n","choco$Bean.Type: Criollo (Ocumare 67)\n","[1] 70\n","------------------------------------------------------------ \n","choco$Bean.Type: Criollo (Ocumare 77)\n","[1] 70\n","------------------------------------------------------------ \n","choco$Bean.Type: Criollo (Ocumare)\n","[1] 80\n","------------------------------------------------------------ \n","choco$Bean.Type: Criollo (Porcelana)\n","[1] 70.3\n","------------------------------------------------------------ \n","choco$Bean.Type: Criollo (Wild)\n","[1] 68\n","------------------------------------------------------------ \n","choco$Bean.Type: Criollo, +\n","[1] 65\n","------------------------------------------------------------ \n","choco$Bean.Type: Criollo, Forastero\n","[1] 76.5\n","------------------------------------------------------------ \n","choco$Bean.Type: Criollo, Trinitario\n","[1] 72.20513\n","------------------------------------------------------------ \n","choco$Bean.Type: EET\n","[1] 71.33333\n","------------------------------------------------------------ \n","choco$Bean.Type: Forastero\n","[1] 72.05747\n","------------------------------------------------------------ \n","choco$Bean.Type: Forastero (Amelonado)\n","[1] 70\n","------------------------------------------------------------ \n","choco$Bean.Type: Forastero (Arriba)\n","[1] 73.13514\n","------------------------------------------------------------ \n","choco$Bean.Type: Forastero (Arriba) ASS\n","[1] 68.66667\n","------------------------------------------------------------ \n","choco$Bean.Type: Forastero (Arriba) ASSS\n","[1] 70\n","------------------------------------------------------------ \n","choco$Bean.Type: Forastero (Catongo)\n","[1] 72.5\n","------------------------------------------------------------ \n","choco$Bean.Type: Forastero (Nacional)\n","[1] 73.01923\n","------------------------------------------------------------ \n","choco$Bean.Type: Forastero (Parazinho)\n","[1] 68.75\n","------------------------------------------------------------ \n","choco$Bean.Type: Forastero, Trinitario\n","[1] 70\n","------------------------------------------------------------ \n","choco$Bean.Type: Forastero(Arriba, CCN)\n","[1] 75\n","------------------------------------------------------------ \n","choco$Bean.Type: Matina\n","[1] 74\n","------------------------------------------------------------ \n","choco$Bean.Type: Nacional\n","[1] 71\n","------------------------------------------------------------ \n","choco$Bean.Type: Nacional (Arriba)\n","[1] 73.33333\n","------------------------------------------------------------ \n","choco$Bean.Type: Trinitario\n","[1] NA\n","------------------------------------------------------------ \n","choco$Bean.Type: Trinitario (85% Criollo)\n","[1] 70\n","------------------------------------------------------------ \n","choco$Bean.Type: Trinitario (Amelonado)\n","[1] 70\n","------------------------------------------------------------ \n","choco$Bean.Type: Trinitario (Scavina)\n","[1] 70\n","------------------------------------------------------------ \n","choco$Bean.Type: Trinitario, Criollo\n","[1] 68.33333\n","------------------------------------------------------------ \n","choco$Bean.Type: Trinitario, Forastero\n","[1] 70.5\n","------------------------------------------------------------ \n","choco$Bean.Type: Trinitario, Nacional\n","[1] 70\n","------------------------------------------------------------ \n","choco$Bean.Type: Trinitario, TCGA\n","[1] 72"]},"metadata":{}}],"source":["# compute mean for each Bean.Type using Function by\n","\n","with(choco, by(choco$Cocoa.Percent, choco$Bean.Type, mean))"]},{"cell_type":"code","execution_count":64,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"Hb-umPkWvIM0","executionInfo":{"status":"ok","timestamp":1716800092841,"user_tz":-120,"elapsed":218372,"user":{"displayName":"toni Monleon","userId":"05269440626861479105"}},"outputId":"a500f17f-3121-429d-8592-ede40e1203a4"},"outputs":[{"output_type":"stream","name":"stderr","text":["Installing package into ‘/usr/local/lib/R/site-library’\n","(as ‘lib’ is unspecified)\n","\n","also installing the dependencies ‘minqa’, ‘nloptr’, ‘RcppEigen’, ‘lme4’, ‘cowplot’, ‘Deriv’, ‘microbenchmark’, ‘pbkrtest’\n","\n","\n","Warning message in install.packages(\"doBy\"):\n","“installation of package ‘lme4’ had non-zero exit status”\n","Warning message in install.packages(\"doBy\"):\n","“installation of package ‘pbkrtest’ had non-zero exit status”\n","Warning message in install.packages(\"doBy\"):\n","“installation of package ‘doBy’ had non-zero exit status”\n"]}],"source":["#Now using library(doBy)\n","install.packages(\"doBy\")"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":52},"id":"EqlQzOTUxS3X","executionInfo":{"status":"ok","timestamp":1716654159226,"user_tz":-120,"elapsed":259,"user":{"displayName":"toni Monleon","userId":"05269440626861479105"}},"outputId":"30c84b15-4fdd-47aa-ac7a-dab7f268e824"},"outputs":[{"output_type":"display_data","data":{"text/plain":[" Min. 1st Qu. Median Mean 3rd Qu. Max. NA's \n"," 42.00 70.00 70.00 71.72 75.00 100.00 20 "]},"metadata":{}}],"source":["#Remeber that there are a lot of missing in\n","summary(choco$Cocoa.Percent)"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":235,"status":"ok","timestamp":1716654162602,"user":{"displayName":"toni Monleon","userId":"05269440626861479105"},"user_tz":-120},"id":"u90q7a9lx5B9","outputId":"d4452a83-1077-4d35-c1eb-1277872be750"},"outputs":[{"output_type":"stream","name":"stdout","text":["'data.frame':\t1775 obs. of 9 variables:\n"," $ Company...Maker.if.known. : chr \"A. Morin\" \"A. Morin\" \"A. Morin\" \"Acalli\" ...\n"," $ Specific.Bean.Origin.or.Bar.Name: chr \"Chanchamayo Province\" \"Bolivia\" \"Peru\" \"Chulucanas, El Platanal\" ...\n"," $ REF : int 1019 797 797 1462 1470 705 705 705 705 370 ...\n"," $ Review.Date : chr \"2013\" \"2012\" \"2012\" \"2015\" ...\n"," $ Cocoa.Percent : num 63 70 63 70 70 60 80 88 72 55 ...\n"," $ Company.Location : chr \"France\" \"France\" \"France\" \"U.S.A.\" ...\n"," $ Rating : num 4 3.5 3.75 3.75 3.75 2.75 3.25 3.5 3.5 2.75 ...\n"," $ Bean.Type : chr \" \" \" \" \" \" \" \" ...\n"," $ Broad.Bean.Origin : chr \"Peru\" \"Bolivia\" \"Peru\" \"Peru\" ...\n"]}],"source":["#select only the non NA data in ch\n","choco.na<- data.frame(na.omit(choco))\n","str(choco.na)\n","\n"]},{"cell_type":"code","source":["summary(choco.na$Cocoa.Percent) #without missing data"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":52},"id":"v6BuHDEdIrhx","executionInfo":{"status":"ok","timestamp":1716654168627,"user_tz":-120,"elapsed":272,"user":{"displayName":"toni Monleon","userId":"05269440626861479105"}},"outputId":"9e49492c-b0d6-4ebe-c312-ad695eecccac"},"execution_count":null,"outputs":[{"output_type":"display_data","data":{"text/plain":[" Min. 1st Qu. Median Mean 3rd Qu. Max. \n"," 42.00 70.00 70.00 71.72 75.00 100.00 "]},"metadata":{}}]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":1000},"executionInfo":{"elapsed":2715,"status":"ok","timestamp":1716654173507,"user":{"displayName":"toni Monleon","userId":"05269440626861479105"},"user_tz":-120},"id":"Yot-9ufnu_Zk","outputId":"5bcd25bf-94ab-4cf0-97a1-ce619195d501"},"outputs":[{"output_type":"display_data","data":{"text/html":["\n","\n","\n","\t\n","\t\n","\n","\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\n","
A data.frame: 42 × 2
Bean.TypeCocoa.Percent.mean
<chr><dbl>
1 70.00000
2  71.59174
3Amazon 70.00000
4Amazon mix 74.00000
5Amazon, ICS 67.50000
6Beniano 71.00000
7Blend 71.39024
8Blend-Forastero,Criollo 70.00000
9CCN51 65.00000
10Criollo 72.06711
11Criollo (Amarru) 70.00000
12Criollo (Ocumare 61) 73.50000
13Criollo (Ocumare 67) 70.00000
14Criollo (Ocumare 77) 70.00000
15Criollo (Ocumare) 80.00000
16Criollo (Porcelana) 70.30000
17Criollo (Wild) 68.00000
18Criollo, + 65.00000
19Criollo, Forastero 76.50000
20Criollo, Trinitario 72.20513
21EET 71.33333
22Forastero 72.05747
23Forastero (Amelonado) 70.00000
24Forastero (Arriba) 73.13514
25Forastero (Arriba) ASS 68.66667
26Forastero (Arriba) ASSS 70.00000
27Forastero (Catongo) 72.50000
28Forastero (Nacional) 73.01923
29Forastero (Parazinho) 68.75000
30Forastero, Trinitario 70.00000
31Forastero(Arriba, CCN) 75.00000
32Matina 74.00000
33Nacional 71.00000
34Nacional (Arriba) 73.33333
35Trinitario 71.74522
36Trinitario (85% Criollo)70.00000
37Trinitario (Amelonado) 70.00000
38Trinitario (Scavina) 70.00000
39Trinitario, Criollo 68.33333
40Trinitario, Forastero 70.50000
41Trinitario, Nacional 70.00000
42Trinitario, TCGA 72.00000
\n"],"text/markdown":"\nA data.frame: 42 × 2\n\n| | Bean.Type <chr> | Cocoa.Percent.mean <dbl> |\n|---|---|---|\n| 1 | | 70.00000 |\n| 2 |   | 71.59174 |\n| 3 | Amazon | 70.00000 |\n| 4 | Amazon mix | 74.00000 |\n| 5 | Amazon, ICS | 67.50000 |\n| 6 | Beniano | 71.00000 |\n| 7 | Blend | 71.39024 |\n| 8 | Blend-Forastero,Criollo | 70.00000 |\n| 9 | CCN51 | 65.00000 |\n| 10 | Criollo | 72.06711 |\n| 11 | Criollo (Amarru) | 70.00000 |\n| 12 | Criollo (Ocumare 61) | 73.50000 |\n| 13 | Criollo (Ocumare 67) | 70.00000 |\n| 14 | Criollo (Ocumare 77) | 70.00000 |\n| 15 | Criollo (Ocumare) | 80.00000 |\n| 16 | Criollo (Porcelana) | 70.30000 |\n| 17 | Criollo (Wild) | 68.00000 |\n| 18 | Criollo, + | 65.00000 |\n| 19 | Criollo, Forastero | 76.50000 |\n| 20 | Criollo, Trinitario | 72.20513 |\n| 21 | EET | 71.33333 |\n| 22 | Forastero | 72.05747 |\n| 23 | Forastero (Amelonado) | 70.00000 |\n| 24 | Forastero (Arriba) | 73.13514 |\n| 25 | Forastero (Arriba) ASS | 68.66667 |\n| 26 | Forastero (Arriba) ASSS | 70.00000 |\n| 27 | Forastero (Catongo) | 72.50000 |\n| 28 | Forastero (Nacional) | 73.01923 |\n| 29 | Forastero (Parazinho) | 68.75000 |\n| 30 | Forastero, Trinitario | 70.00000 |\n| 31 | Forastero(Arriba, CCN) | 75.00000 |\n| 32 | Matina | 74.00000 |\n| 33 | Nacional | 71.00000 |\n| 34 | Nacional (Arriba) | 73.33333 |\n| 35 | Trinitario | 71.74522 |\n| 36 | Trinitario (85% Criollo) | 70.00000 |\n| 37 | Trinitario (Amelonado) | 70.00000 |\n| 38 | Trinitario (Scavina) | 70.00000 |\n| 39 | Trinitario, Criollo | 68.33333 |\n| 40 | Trinitario, Forastero | 70.50000 |\n| 41 | Trinitario, Nacional | 70.00000 |\n| 42 | Trinitario, TCGA | 72.00000 |\n\n","text/latex":"A data.frame: 42 × 2\n\\begin{tabular}{r|ll}\n & Bean.Type & Cocoa.Percent.mean\\\\\n & & \\\\\n\\hline\n\t1 & & 70.00000\\\\\n\t2 &   & 71.59174\\\\\n\t3 & Amazon & 70.00000\\\\\n\t4 & Amazon mix & 74.00000\\\\\n\t5 & Amazon, ICS & 67.50000\\\\\n\t6 & Beniano & 71.00000\\\\\n\t7 & Blend & 71.39024\\\\\n\t8 & Blend-Forastero,Criollo & 70.00000\\\\\n\t9 & CCN51 & 65.00000\\\\\n\t10 & Criollo & 72.06711\\\\\n\t11 & Criollo (Amarru) & 70.00000\\\\\n\t12 & Criollo (Ocumare 61) & 73.50000\\\\\n\t13 & Criollo (Ocumare 67) & 70.00000\\\\\n\t14 & Criollo (Ocumare 77) & 70.00000\\\\\n\t15 & Criollo (Ocumare) & 80.00000\\\\\n\t16 & Criollo (Porcelana) & 70.30000\\\\\n\t17 & Criollo (Wild) & 68.00000\\\\\n\t18 & Criollo, + & 65.00000\\\\\n\t19 & Criollo, Forastero & 76.50000\\\\\n\t20 & Criollo, Trinitario & 72.20513\\\\\n\t21 & EET & 71.33333\\\\\n\t22 & Forastero & 72.05747\\\\\n\t23 & Forastero (Amelonado) & 70.00000\\\\\n\t24 & Forastero (Arriba) & 73.13514\\\\\n\t25 & Forastero (Arriba) ASS & 68.66667\\\\\n\t26 & Forastero (Arriba) ASSS & 70.00000\\\\\n\t27 & Forastero (Catongo) & 72.50000\\\\\n\t28 & Forastero (Nacional) & 73.01923\\\\\n\t29 & Forastero (Parazinho) & 68.75000\\\\\n\t30 & Forastero, Trinitario & 70.00000\\\\\n\t31 & Forastero(Arriba, CCN) & 75.00000\\\\\n\t32 & Matina & 74.00000\\\\\n\t33 & Nacional & 71.00000\\\\\n\t34 & Nacional (Arriba) & 73.33333\\\\\n\t35 & Trinitario & 71.74522\\\\\n\t36 & Trinitario (85\\% Criollo) & 70.00000\\\\\n\t37 & Trinitario (Amelonado) & 70.00000\\\\\n\t38 & Trinitario (Scavina) & 70.00000\\\\\n\t39 & Trinitario, Criollo & 68.33333\\\\\n\t40 & Trinitario, Forastero & 70.50000\\\\\n\t41 & Trinitario, Nacional & 70.00000\\\\\n\t42 & Trinitario, TCGA & 72.00000\\\\\n\\end{tabular}\n","text/plain":[" Bean.Type Cocoa.Percent.mean\n","1 70.00000 \n","2   71.59174 \n","3 Amazon 70.00000 \n","4 Amazon mix 74.00000 \n","5 Amazon, ICS 67.50000 \n","6 Beniano 71.00000 \n","7 Blend 71.39024 \n","8 Blend-Forastero,Criollo 70.00000 \n","9 CCN51 65.00000 \n","10 Criollo 72.06711 \n","11 Criollo (Amarru) 70.00000 \n","12 Criollo (Ocumare 61) 73.50000 \n","13 Criollo (Ocumare 67) 70.00000 \n","14 Criollo (Ocumare 77) 70.00000 \n","15 Criollo (Ocumare) 80.00000 \n","16 Criollo (Porcelana) 70.30000 \n","17 Criollo (Wild) 68.00000 \n","18 Criollo, + 65.00000 \n","19 Criollo, Forastero 76.50000 \n","20 Criollo, Trinitario 72.20513 \n","21 EET 71.33333 \n","22 Forastero 72.05747 \n","23 Forastero (Amelonado) 70.00000 \n","24 Forastero (Arriba) 73.13514 \n","25 Forastero (Arriba) ASS 68.66667 \n","26 Forastero (Arriba) ASSS 70.00000 \n","27 Forastero (Catongo) 72.50000 \n","28 Forastero (Nacional) 73.01923 \n","29 Forastero (Parazinho) 68.75000 \n","30 Forastero, Trinitario 70.00000 \n","31 Forastero(Arriba, CCN) 75.00000 \n","32 Matina 74.00000 \n","33 Nacional 71.00000 \n","34 Nacional (Arriba) 73.33333 \n","35 Trinitario 71.74522 \n","36 Trinitario (85% Criollo) 70.00000 \n","37 Trinitario (Amelonado) 70.00000 \n","38 Trinitario (Scavina) 70.00000 \n","39 Trinitario, Criollo 68.33333 \n","40 Trinitario, Forastero 70.50000 \n","41 Trinitario, Nacional 70.00000 \n","42 Trinitario, TCGA 72.00000 "]},"metadata":{}}],"source":["#Function summaryBy(doBy) #mean and var for Cocoa.Percent ~ Bean.Type\n","library(doBy)\n","summaryBy(Cocoa.Percent ~ Bean.Type, choco.na, FUN = c(mean))"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":1000},"executionInfo":{"elapsed":260,"status":"ok","timestamp":1716654185165,"user":{"displayName":"toni Monleon","userId":"05269440626861479105"},"user_tz":-120},"id":"YXgZWyC9vNtU","outputId":"75a7c291-c63e-4a84-9649-21b83a92bcbd"},"outputs":[{"output_type":"display_data","data":{"text/html":["\n","\n","\n","\t\n","\t\n","\n","\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\n","
A data.frame: 42 × 6
Bean.TypeCocoa.Percent.meanCocoa.Percent.sdCocoa.Percent.medianCocoa.Percent.minCocoa.Percent.max
<chr><dbl><dbl><dbl><dbl><dbl>
1 70.00000 NA70.070 70
2  71.59174 6.593160170.042100
3Amazon 70.00000 NA70.070 70
4Amazon mix 74.00000 0.000000074.074 74
5Amazon, ICS 67.50000 0.707106867.567 68
6Beniano 71.00000 3.605551372.067 74
7Blend 71.39024 6.453208770.055 91
8Blend-Forastero,Criollo 70.00000 NA70.070 70
9CCN51 65.00000 NA65.065 65
10Criollo 72.06711 5.835196671.058100
11Criollo (Amarru) 70.00000 0.000000070.070 70
12Criollo (Ocumare 61) 73.50000 2.121320373.572 75
13Criollo (Ocumare 67) 70.00000 NA70.070 70
14Criollo (Ocumare 77) 70.00000 NA70.070 70
15Criollo (Ocumare) 80.00000 NA80.080 80
16Criollo (Porcelana) 70.30000 3.973523570.064 75
17Criollo (Wild) 68.00000 NA68.068 68
18Criollo, + 65.00000 NA65.065 65
19Criollo, Forastero 76.50000 2.121320376.575 78
20Criollo, Trinitario 72.20513 4.640664070.061 85
21EET 71.33333 5.131601470.067 77
22Forastero 72.05747 6.585054170.057100
23Forastero (Amelonado) 70.00000 NA70.070 70
24Forastero (Arriba) 73.1351412.051192172.055100
25Forastero (Arriba) ASS 68.6666710.171856668.058 80
26Forastero (Arriba) ASSS 70.00000 NA70.070 70
27Forastero (Catongo) 72.50000 3.535533972.570 75
28Forastero (Nacional) 73.01923 7.484599670.053100
29Forastero (Parazinho) 68.7500010.264362870.050 85
30Forastero, Trinitario 70.00000 NA70.070 70
31Forastero(Arriba, CCN) 75.00000 NA75.075 75
32Matina 74.00000 6.928203270.070 82
33Nacional 71.00000 1.414213671.070 72
34Nacional (Arriba) 73.33333 2.886751375.070 75
35Trinitario 71.74522 5.327773470.055100
36Trinitario (85% Criollo)70.00000 0.000000070.070 70
37Trinitario (Amelonado) 70.00000 NA70.070 70
38Trinitario (Scavina) 70.00000 NA70.070 70
39Trinitario, Criollo 68.33333 3.201562170.065 74
40Trinitario, Forastero 70.50000 6.363961070.566 75
41Trinitario, Nacional 70.00000 NA70.070 70
42Trinitario, TCGA 72.00000 NA72.072 72
\n"],"text/markdown":"\nA data.frame: 42 × 6\n\n| | Bean.Type <chr> | Cocoa.Percent.mean <dbl> | Cocoa.Percent.sd <dbl> | Cocoa.Percent.median <dbl> | Cocoa.Percent.min <dbl> | Cocoa.Percent.max <dbl> |\n|---|---|---|---|---|---|---|\n| 1 | | 70.00000 | NA | 70.0 | 70 | 70 |\n| 2 |   | 71.59174 | 6.5931601 | 70.0 | 42 | 100 |\n| 3 | Amazon | 70.00000 | NA | 70.0 | 70 | 70 |\n| 4 | Amazon mix | 74.00000 | 0.0000000 | 74.0 | 74 | 74 |\n| 5 | Amazon, ICS | 67.50000 | 0.7071068 | 67.5 | 67 | 68 |\n| 6 | Beniano | 71.00000 | 3.6055513 | 72.0 | 67 | 74 |\n| 7 | Blend | 71.39024 | 6.4532087 | 70.0 | 55 | 91 |\n| 8 | Blend-Forastero,Criollo | 70.00000 | NA | 70.0 | 70 | 70 |\n| 9 | CCN51 | 65.00000 | NA | 65.0 | 65 | 65 |\n| 10 | Criollo | 72.06711 | 5.8351966 | 71.0 | 58 | 100 |\n| 11 | Criollo (Amarru) | 70.00000 | 0.0000000 | 70.0 | 70 | 70 |\n| 12 | Criollo (Ocumare 61) | 73.50000 | 2.1213203 | 73.5 | 72 | 75 |\n| 13 | Criollo (Ocumare 67) | 70.00000 | NA | 70.0 | 70 | 70 |\n| 14 | Criollo (Ocumare 77) | 70.00000 | NA | 70.0 | 70 | 70 |\n| 15 | Criollo (Ocumare) | 80.00000 | NA | 80.0 | 80 | 80 |\n| 16 | Criollo (Porcelana) | 70.30000 | 3.9735235 | 70.0 | 64 | 75 |\n| 17 | Criollo (Wild) | 68.00000 | NA | 68.0 | 68 | 68 |\n| 18 | Criollo, + | 65.00000 | NA | 65.0 | 65 | 65 |\n| 19 | Criollo, Forastero | 76.50000 | 2.1213203 | 76.5 | 75 | 78 |\n| 20 | Criollo, Trinitario | 72.20513 | 4.6406640 | 70.0 | 61 | 85 |\n| 21 | EET | 71.33333 | 5.1316014 | 70.0 | 67 | 77 |\n| 22 | Forastero | 72.05747 | 6.5850541 | 70.0 | 57 | 100 |\n| 23 | Forastero (Amelonado) | 70.00000 | NA | 70.0 | 70 | 70 |\n| 24 | Forastero (Arriba) | 73.13514 | 12.0511921 | 72.0 | 55 | 100 |\n| 25 | Forastero (Arriba) ASS | 68.66667 | 10.1718566 | 68.0 | 58 | 80 |\n| 26 | Forastero (Arriba) ASSS | 70.00000 | NA | 70.0 | 70 | 70 |\n| 27 | Forastero (Catongo) | 72.50000 | 3.5355339 | 72.5 | 70 | 75 |\n| 28 | Forastero (Nacional) | 73.01923 | 7.4845996 | 70.0 | 53 | 100 |\n| 29 | Forastero (Parazinho) | 68.75000 | 10.2643628 | 70.0 | 50 | 85 |\n| 30 | Forastero, Trinitario | 70.00000 | NA | 70.0 | 70 | 70 |\n| 31 | Forastero(Arriba, CCN) | 75.00000 | NA | 75.0 | 75 | 75 |\n| 32 | Matina | 74.00000 | 6.9282032 | 70.0 | 70 | 82 |\n| 33 | Nacional | 71.00000 | 1.4142136 | 71.0 | 70 | 72 |\n| 34 | Nacional (Arriba) | 73.33333 | 2.8867513 | 75.0 | 70 | 75 |\n| 35 | Trinitario | 71.74522 | 5.3277734 | 70.0 | 55 | 100 |\n| 36 | Trinitario (85% Criollo) | 70.00000 | 0.0000000 | 70.0 | 70 | 70 |\n| 37 | Trinitario (Amelonado) | 70.00000 | NA | 70.0 | 70 | 70 |\n| 38 | Trinitario (Scavina) | 70.00000 | NA | 70.0 | 70 | 70 |\n| 39 | Trinitario, Criollo | 68.33333 | 3.2015621 | 70.0 | 65 | 74 |\n| 40 | Trinitario, Forastero | 70.50000 | 6.3639610 | 70.5 | 66 | 75 |\n| 41 | Trinitario, Nacional | 70.00000 | NA | 70.0 | 70 | 70 |\n| 42 | Trinitario, TCGA | 72.00000 | NA | 72.0 | 72 | 72 |\n\n","text/latex":"A data.frame: 42 × 6\n\\begin{tabular}{r|llllll}\n & Bean.Type & Cocoa.Percent.mean & Cocoa.Percent.sd & Cocoa.Percent.median & Cocoa.Percent.min & Cocoa.Percent.max\\\\\n & & & & & & \\\\\n\\hline\n\t1 & & 70.00000 & NA & 70.0 & 70 & 70\\\\\n\t2 &   & 71.59174 & 6.5931601 & 70.0 & 42 & 100\\\\\n\t3 & Amazon & 70.00000 & NA & 70.0 & 70 & 70\\\\\n\t4 & Amazon mix & 74.00000 & 0.0000000 & 74.0 & 74 & 74\\\\\n\t5 & Amazon, ICS & 67.50000 & 0.7071068 & 67.5 & 67 & 68\\\\\n\t6 & Beniano & 71.00000 & 3.6055513 & 72.0 & 67 & 74\\\\\n\t7 & Blend & 71.39024 & 6.4532087 & 70.0 & 55 & 91\\\\\n\t8 & Blend-Forastero,Criollo & 70.00000 & NA & 70.0 & 70 & 70\\\\\n\t9 & CCN51 & 65.00000 & NA & 65.0 & 65 & 65\\\\\n\t10 & Criollo & 72.06711 & 5.8351966 & 71.0 & 58 & 100\\\\\n\t11 & Criollo (Amarru) & 70.00000 & 0.0000000 & 70.0 & 70 & 70\\\\\n\t12 & Criollo (Ocumare 61) & 73.50000 & 2.1213203 & 73.5 & 72 & 75\\\\\n\t13 & Criollo (Ocumare 67) & 70.00000 & NA & 70.0 & 70 & 70\\\\\n\t14 & Criollo (Ocumare 77) & 70.00000 & NA & 70.0 & 70 & 70\\\\\n\t15 & Criollo (Ocumare) & 80.00000 & NA & 80.0 & 80 & 80\\\\\n\t16 & Criollo (Porcelana) & 70.30000 & 3.9735235 & 70.0 & 64 & 75\\\\\n\t17 & Criollo (Wild) & 68.00000 & NA & 68.0 & 68 & 68\\\\\n\t18 & Criollo, + & 65.00000 & NA & 65.0 & 65 & 65\\\\\n\t19 & Criollo, Forastero & 76.50000 & 2.1213203 & 76.5 & 75 & 78\\\\\n\t20 & Criollo, Trinitario & 72.20513 & 4.6406640 & 70.0 & 61 & 85\\\\\n\t21 & EET & 71.33333 & 5.1316014 & 70.0 & 67 & 77\\\\\n\t22 & Forastero & 72.05747 & 6.5850541 & 70.0 & 57 & 100\\\\\n\t23 & Forastero (Amelonado) & 70.00000 & NA & 70.0 & 70 & 70\\\\\n\t24 & Forastero (Arriba) & 73.13514 & 12.0511921 & 72.0 & 55 & 100\\\\\n\t25 & Forastero (Arriba) ASS & 68.66667 & 10.1718566 & 68.0 & 58 & 80\\\\\n\t26 & Forastero (Arriba) ASSS & 70.00000 & NA & 70.0 & 70 & 70\\\\\n\t27 & Forastero (Catongo) & 72.50000 & 3.5355339 & 72.5 & 70 & 75\\\\\n\t28 & Forastero (Nacional) & 73.01923 & 7.4845996 & 70.0 & 53 & 100\\\\\n\t29 & Forastero (Parazinho) & 68.75000 & 10.2643628 & 70.0 & 50 & 85\\\\\n\t30 & Forastero, Trinitario & 70.00000 & NA & 70.0 & 70 & 70\\\\\n\t31 & Forastero(Arriba, CCN) & 75.00000 & NA & 75.0 & 75 & 75\\\\\n\t32 & Matina & 74.00000 & 6.9282032 & 70.0 & 70 & 82\\\\\n\t33 & Nacional & 71.00000 & 1.4142136 & 71.0 & 70 & 72\\\\\n\t34 & Nacional (Arriba) & 73.33333 & 2.8867513 & 75.0 & 70 & 75\\\\\n\t35 & Trinitario & 71.74522 & 5.3277734 & 70.0 & 55 & 100\\\\\n\t36 & Trinitario (85\\% Criollo) & 70.00000 & 0.0000000 & 70.0 & 70 & 70\\\\\n\t37 & Trinitario (Amelonado) & 70.00000 & NA & 70.0 & 70 & 70\\\\\n\t38 & Trinitario (Scavina) & 70.00000 & NA & 70.0 & 70 & 70\\\\\n\t39 & Trinitario, Criollo & 68.33333 & 3.2015621 & 70.0 & 65 & 74\\\\\n\t40 & Trinitario, Forastero & 70.50000 & 6.3639610 & 70.5 & 66 & 75\\\\\n\t41 & Trinitario, Nacional & 70.00000 & NA & 70.0 & 70 & 70\\\\\n\t42 & Trinitario, TCGA & 72.00000 & NA & 72.0 & 72 & 72\\\\\n\\end{tabular}\n","text/plain":[" Bean.Type Cocoa.Percent.mean Cocoa.Percent.sd\n","1 70.00000 NA \n","2   71.59174 6.5931601 \n","3 Amazon 70.00000 NA \n","4 Amazon mix 74.00000 0.0000000 \n","5 Amazon, ICS 67.50000 0.7071068 \n","6 Beniano 71.00000 3.6055513 \n","7 Blend 71.39024 6.4532087 \n","8 Blend-Forastero,Criollo 70.00000 NA \n","9 CCN51 65.00000 NA \n","10 Criollo 72.06711 5.8351966 \n","11 Criollo (Amarru) 70.00000 0.0000000 \n","12 Criollo (Ocumare 61) 73.50000 2.1213203 \n","13 Criollo (Ocumare 67) 70.00000 NA \n","14 Criollo (Ocumare 77) 70.00000 NA \n","15 Criollo (Ocumare) 80.00000 NA \n","16 Criollo (Porcelana) 70.30000 3.9735235 \n","17 Criollo (Wild) 68.00000 NA \n","18 Criollo, + 65.00000 NA \n","19 Criollo, Forastero 76.50000 2.1213203 \n","20 Criollo, Trinitario 72.20513 4.6406640 \n","21 EET 71.33333 5.1316014 \n","22 Forastero 72.05747 6.5850541 \n","23 Forastero (Amelonado) 70.00000 NA \n","24 Forastero (Arriba) 73.13514 12.0511921 \n","25 Forastero (Arriba) ASS 68.66667 10.1718566 \n","26 Forastero (Arriba) ASSS 70.00000 NA \n","27 Forastero (Catongo) 72.50000 3.5355339 \n","28 Forastero (Nacional) 73.01923 7.4845996 \n","29 Forastero (Parazinho) 68.75000 10.2643628 \n","30 Forastero, Trinitario 70.00000 NA \n","31 Forastero(Arriba, CCN) 75.00000 NA \n","32 Matina 74.00000 6.9282032 \n","33 Nacional 71.00000 1.4142136 \n","34 Nacional (Arriba) 73.33333 2.8867513 \n","35 Trinitario 71.74522 5.3277734 \n","36 Trinitario (85% Criollo) 70.00000 0.0000000 \n","37 Trinitario (Amelonado) 70.00000 NA \n","38 Trinitario (Scavina) 70.00000 NA \n","39 Trinitario, Criollo 68.33333 3.2015621 \n","40 Trinitario, Forastero 70.50000 6.3639610 \n","41 Trinitario, Nacional 70.00000 NA \n","42 Trinitario, TCGA 72.00000 NA \n"," Cocoa.Percent.median Cocoa.Percent.min Cocoa.Percent.max\n","1 70.0 70 70 \n","2 70.0 42 100 \n","3 70.0 70 70 \n","4 74.0 74 74 \n","5 67.5 67 68 \n","6 72.0 67 74 \n","7 70.0 55 91 \n","8 70.0 70 70 \n","9 65.0 65 65 \n","10 71.0 58 100 \n","11 70.0 70 70 \n","12 73.5 72 75 \n","13 70.0 70 70 \n","14 70.0 70 70 \n","15 80.0 80 80 \n","16 70.0 64 75 \n","17 68.0 68 68 \n","18 65.0 65 65 \n","19 76.5 75 78 \n","20 70.0 61 85 \n","21 70.0 67 77 \n","22 70.0 57 100 \n","23 70.0 70 70 \n","24 72.0 55 100 \n","25 68.0 58 80 \n","26 70.0 70 70 \n","27 72.5 70 75 \n","28 70.0 53 100 \n","29 70.0 50 85 \n","30 70.0 70 70 \n","31 75.0 75 75 \n","32 70.0 70 82 \n","33 71.0 70 72 \n","34 75.0 70 75 \n","35 70.0 55 100 \n","36 70.0 70 70 \n","37 70.0 70 70 \n","38 70.0 70 70 \n","39 70.0 65 74 \n","40 70.5 66 75 \n","41 70.0 70 70 \n","42 72.0 72 72 "]},"metadata":{}}],"source":["#different statistics\n","sumby <- summaryBy(Cocoa.Percent ~ Bean.Type, choco.na, FUN = c(mean, sd, median, min, max))\n","sumby\n"]},{"cell_type":"code","source":["#other tunning\n","names(sumby) <- c(\"Bean type\", \"Mean\", \"St.Dev.\", \"Median\", \"Min.\", \"Max.\")\n","print(sumby, digits = 3)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"qgb9ciGEJ6TB","executionInfo":{"status":"ok","timestamp":1716654208437,"user_tz":-120,"elapsed":248,"user":{"displayName":"toni Monleon","userId":"05269440626861479105"}},"outputId":"2f5a592d-4269-4cfd-e5af-71ca188b9e92"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":[" Bean type Mean St.Dev. Median Min. Max.\n","1 70.0 NA 70.0 70 70\n","2   71.6 6.593 70.0 42 100\n","3 Amazon 70.0 NA 70.0 70 70\n","4 Amazon mix 74.0 0.000 74.0 74 74\n","5 Amazon, ICS 67.5 0.707 67.5 67 68\n","6 Beniano 71.0 3.606 72.0 67 74\n","7 Blend 71.4 6.453 70.0 55 91\n","8 Blend-Forastero,Criollo 70.0 NA 70.0 70 70\n","9 CCN51 65.0 NA 65.0 65 65\n","10 Criollo 72.1 5.835 71.0 58 100\n","11 Criollo (Amarru) 70.0 0.000 70.0 70 70\n","12 Criollo (Ocumare 61) 73.5 2.121 73.5 72 75\n","13 Criollo (Ocumare 67) 70.0 NA 70.0 70 70\n","14 Criollo (Ocumare 77) 70.0 NA 70.0 70 70\n","15 Criollo (Ocumare) 80.0 NA 80.0 80 80\n","16 Criollo (Porcelana) 70.3 3.974 70.0 64 75\n","17 Criollo (Wild) 68.0 NA 68.0 68 68\n","18 Criollo, + 65.0 NA 65.0 65 65\n","19 Criollo, Forastero 76.5 2.121 76.5 75 78\n","20 Criollo, Trinitario 72.2 4.641 70.0 61 85\n","21 EET 71.3 5.132 70.0 67 77\n","22 Forastero 72.1 6.585 70.0 57 100\n","23 Forastero (Amelonado) 70.0 NA 70.0 70 70\n","24 Forastero (Arriba) 73.1 12.051 72.0 55 100\n","25 Forastero (Arriba) ASS 68.7 10.172 68.0 58 80\n","26 Forastero (Arriba) ASSS 70.0 NA 70.0 70 70\n","27 Forastero (Catongo) 72.5 3.536 72.5 70 75\n","28 Forastero (Nacional) 73.0 7.485 70.0 53 100\n","29 Forastero (Parazinho) 68.8 10.264 70.0 50 85\n","30 Forastero, Trinitario 70.0 NA 70.0 70 70\n","31 Forastero(Arriba, CCN) 75.0 NA 75.0 75 75\n","32 Matina 74.0 6.928 70.0 70 82\n","33 Nacional 71.0 1.414 71.0 70 72\n","34 Nacional (Arriba) 73.3 2.887 75.0 70 75\n","35 Trinitario 71.7 5.328 70.0 55 100\n","36 Trinitario (85% Criollo) 70.0 0.000 70.0 70 70\n","37 Trinitario (Amelonado) 70.0 NA 70.0 70 70\n","38 Trinitario (Scavina) 70.0 NA 70.0 70 70\n","39 Trinitario, Criollo 68.3 3.202 70.0 65 74\n","40 Trinitario, Forastero 70.5 6.364 70.5 66 75\n","41 Trinitario, Nacional 70.0 NA 70.0 70 70\n","42 Trinitario, TCGA 72.0 NA 72.0 72 72\n"]}]},{"cell_type":"code","execution_count":null,"metadata":{"id":"ItC_3MchvmQK","colab":{"base_uri":"https://localhost:8080/","height":1000},"executionInfo":{"status":"ok","timestamp":1716654219580,"user_tz":-120,"elapsed":260,"user":{"displayName":"toni Monleon","userId":"05269440626861479105"}},"outputId":"cdc99575-673c-49c5-a827-2379293fd740"},"outputs":[{"output_type":"display_data","data":{"text/html":["\n","\n","\n","\t\n","\t\n","\n","\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\n","
A data.frame: 42 × 11
Bean.TypeCocoa.Percent.meanRating.meanCocoa.Percent.sdRating.sdCocoa.Percent.medianRating.medianCocoa.Percent.minRating.minCocoa.Percent.maxRating.max
<chr><dbl><dbl><dbl><dbl><dbl><dbl><dbl><dbl><dbl><dbl>
1 70.000004.000000 NA NA70.04.000704.00 704.00
2  71.591743.134748 6.59316010.484154470.03.250421.001004.00
3Amazon 70.000003.250000 NA NA70.03.250703.25 703.25
4Amazon mix 74.000003.750000 0.00000000.353553474.03.750743.50 744.00
5Amazon, ICS 67.500003.625000 0.70710680.176776767.53.625673.50 683.75
6Beniano 71.000003.583333 3.60555130.520416572.03.750673.00 744.00
7Blend 71.390243.353659 6.45320870.556146470.03.500552.00 915.00
8Blend-Forastero,Criollo 70.000003.750000 NA NA70.03.750703.75 703.75
9CCN51 65.000003.500000 NA NA65.03.500653.50 653.50
10Criollo 72.067113.239933 5.83519660.460316471.03.250582.001004.00
11Criollo (Amarru) 70.000003.250000 0.00000000.353553470.03.250703.00 703.50
12Criollo (Ocumare 61) 73.500003.250000 2.12132030.000000073.53.250723.25 753.25
13Criollo (Ocumare 67) 70.000004.000000 NA NA70.04.000704.00 704.00
14Criollo (Ocumare 77) 70.000003.750000 NA NA70.03.750703.75 703.75
15Criollo (Ocumare) 80.000003.000000 NA NA80.03.000803.00 803.00
16Criollo (Porcelana) 70.300003.375000 3.97352350.503460270.03.250642.50 754.00
17Criollo (Wild) 68.000004.000000 NA NA68.04.000684.00 684.00
18Criollo, + 65.000003.500000 NA NA65.03.500653.50 653.50
19Criollo, Forastero 76.500003.625000 2.12132030.176776776.53.625753.50 783.75
20Criollo, Trinitario 72.205133.294872 4.64066400.400952070.03.250612.50 854.00
21EET 71.333333.583333 5.13160140.144337670.03.500673.50 773.75
22Forastero 72.057473.100575 6.58505410.543807370.03.000571.001004.00
23Forastero (Amelonado) 70.000003.750000 NA NA70.03.750703.75 703.75
24Forastero (Arriba) 73.135142.83108112.05119210.603962872.03.000551.501004.00
25Forastero (Arriba) ASS 68.666672.87500010.17185660.541987168.03.000582.00 803.50
26Forastero (Arriba) ASSS 70.000003.500000 NA NA70.03.500703.50 703.50
27Forastero (Catongo) 72.500003.375000 3.53553390.176776772.53.375703.25 753.50
28Forastero (Nacional) 73.019233.269231 7.48459960.436805970.03.250532.001004.00
29Forastero (Parazinho) 68.750003.53125010.26436280.364434570.03.500502.75 854.00
30Forastero, Trinitario 70.000003.000000 NA NA70.03.000703.00 703.00
31Forastero(Arriba, CCN) 75.000003.000000 NA NA75.03.000753.00 753.00
32Matina 74.000003.416667 6.92820320.381881370.03.500703.00 823.75
33Nacional 71.000003.000000 1.41421360.707106871.03.000702.50 723.50
34Nacional (Arriba) 73.333333.250000 2.88675130.500000075.03.250702.75 753.75
35Trinitario 71.745223.244019 5.32777340.420146370.03.250551.751005.00
36Trinitario (85% Criollo)70.000003.875000 0.00000000.176776770.03.875703.75 704.00
37Trinitario (Amelonado) 70.000003.000000 NA NA70.03.000703.00 703.00
38Trinitario (Scavina) 70.000003.500000 NA NA70.03.500703.50 703.50
39Trinitario, Criollo 68.333333.027778 3.20156210.522081870.03.000652.25 743.75
40Trinitario, Forastero 70.500003.000000 6.36396100.000000070.53.000663.00 753.00
41Trinitario, Nacional 70.000003.750000 NA NA70.03.750703.75 703.75
42Trinitario, TCGA 72.000003.750000 NA NA72.03.750723.75 723.75
\n"],"text/markdown":"\nA data.frame: 42 × 11\n\n| | Bean.Type <chr> | Cocoa.Percent.mean <dbl> | Rating.mean <dbl> | Cocoa.Percent.sd <dbl> | Rating.sd <dbl> | Cocoa.Percent.median <dbl> | Rating.median <dbl> | Cocoa.Percent.min <dbl> | Rating.min <dbl> | Cocoa.Percent.max <dbl> | Rating.max <dbl> |\n|---|---|---|---|---|---|---|---|---|---|---|---|\n| 1 | | 70.00000 | 4.000000 | NA | NA | 70.0 | 4.000 | 70 | 4.00 | 70 | 4.00 |\n| 2 |   | 71.59174 | 3.134748 | 6.5931601 | 0.4841544 | 70.0 | 3.250 | 42 | 1.00 | 100 | 4.00 |\n| 3 | Amazon | 70.00000 | 3.250000 | NA | NA | 70.0 | 3.250 | 70 | 3.25 | 70 | 3.25 |\n| 4 | Amazon mix | 74.00000 | 3.750000 | 0.0000000 | 0.3535534 | 74.0 | 3.750 | 74 | 3.50 | 74 | 4.00 |\n| 5 | Amazon, ICS | 67.50000 | 3.625000 | 0.7071068 | 0.1767767 | 67.5 | 3.625 | 67 | 3.50 | 68 | 3.75 |\n| 6 | Beniano | 71.00000 | 3.583333 | 3.6055513 | 0.5204165 | 72.0 | 3.750 | 67 | 3.00 | 74 | 4.00 |\n| 7 | Blend | 71.39024 | 3.353659 | 6.4532087 | 0.5561464 | 70.0 | 3.500 | 55 | 2.00 | 91 | 5.00 |\n| 8 | Blend-Forastero,Criollo | 70.00000 | 3.750000 | NA | NA | 70.0 | 3.750 | 70 | 3.75 | 70 | 3.75 |\n| 9 | CCN51 | 65.00000 | 3.500000 | NA | NA | 65.0 | 3.500 | 65 | 3.50 | 65 | 3.50 |\n| 10 | Criollo | 72.06711 | 3.239933 | 5.8351966 | 0.4603164 | 71.0 | 3.250 | 58 | 2.00 | 100 | 4.00 |\n| 11 | Criollo (Amarru) | 70.00000 | 3.250000 | 0.0000000 | 0.3535534 | 70.0 | 3.250 | 70 | 3.00 | 70 | 3.50 |\n| 12 | Criollo (Ocumare 61) | 73.50000 | 3.250000 | 2.1213203 | 0.0000000 | 73.5 | 3.250 | 72 | 3.25 | 75 | 3.25 |\n| 13 | Criollo (Ocumare 67) | 70.00000 | 4.000000 | NA | NA | 70.0 | 4.000 | 70 | 4.00 | 70 | 4.00 |\n| 14 | Criollo (Ocumare 77) | 70.00000 | 3.750000 | NA | NA | 70.0 | 3.750 | 70 | 3.75 | 70 | 3.75 |\n| 15 | Criollo (Ocumare) | 80.00000 | 3.000000 | NA | NA | 80.0 | 3.000 | 80 | 3.00 | 80 | 3.00 |\n| 16 | Criollo (Porcelana) | 70.30000 | 3.375000 | 3.9735235 | 0.5034602 | 70.0 | 3.250 | 64 | 2.50 | 75 | 4.00 |\n| 17 | Criollo (Wild) | 68.00000 | 4.000000 | NA | NA | 68.0 | 4.000 | 68 | 4.00 | 68 | 4.00 |\n| 18 | Criollo, + | 65.00000 | 3.500000 | NA | NA | 65.0 | 3.500 | 65 | 3.50 | 65 | 3.50 |\n| 19 | Criollo, Forastero | 76.50000 | 3.625000 | 2.1213203 | 0.1767767 | 76.5 | 3.625 | 75 | 3.50 | 78 | 3.75 |\n| 20 | Criollo, Trinitario | 72.20513 | 3.294872 | 4.6406640 | 0.4009520 | 70.0 | 3.250 | 61 | 2.50 | 85 | 4.00 |\n| 21 | EET | 71.33333 | 3.583333 | 5.1316014 | 0.1443376 | 70.0 | 3.500 | 67 | 3.50 | 77 | 3.75 |\n| 22 | Forastero | 72.05747 | 3.100575 | 6.5850541 | 0.5438073 | 70.0 | 3.000 | 57 | 1.00 | 100 | 4.00 |\n| 23 | Forastero (Amelonado) | 70.00000 | 3.750000 | NA | NA | 70.0 | 3.750 | 70 | 3.75 | 70 | 3.75 |\n| 24 | Forastero (Arriba) | 73.13514 | 2.831081 | 12.0511921 | 0.6039628 | 72.0 | 3.000 | 55 | 1.50 | 100 | 4.00 |\n| 25 | Forastero (Arriba) ASS | 68.66667 | 2.875000 | 10.1718566 | 0.5419871 | 68.0 | 3.000 | 58 | 2.00 | 80 | 3.50 |\n| 26 | Forastero (Arriba) ASSS | 70.00000 | 3.500000 | NA | NA | 70.0 | 3.500 | 70 | 3.50 | 70 | 3.50 |\n| 27 | Forastero (Catongo) | 72.50000 | 3.375000 | 3.5355339 | 0.1767767 | 72.5 | 3.375 | 70 | 3.25 | 75 | 3.50 |\n| 28 | Forastero (Nacional) | 73.01923 | 3.269231 | 7.4845996 | 0.4368059 | 70.0 | 3.250 | 53 | 2.00 | 100 | 4.00 |\n| 29 | Forastero (Parazinho) | 68.75000 | 3.531250 | 10.2643628 | 0.3644345 | 70.0 | 3.500 | 50 | 2.75 | 85 | 4.00 |\n| 30 | Forastero, Trinitario | 70.00000 | 3.000000 | NA | NA | 70.0 | 3.000 | 70 | 3.00 | 70 | 3.00 |\n| 31 | Forastero(Arriba, CCN) | 75.00000 | 3.000000 | NA | NA | 75.0 | 3.000 | 75 | 3.00 | 75 | 3.00 |\n| 32 | Matina | 74.00000 | 3.416667 | 6.9282032 | 0.3818813 | 70.0 | 3.500 | 70 | 3.00 | 82 | 3.75 |\n| 33 | Nacional | 71.00000 | 3.000000 | 1.4142136 | 0.7071068 | 71.0 | 3.000 | 70 | 2.50 | 72 | 3.50 |\n| 34 | Nacional (Arriba) | 73.33333 | 3.250000 | 2.8867513 | 0.5000000 | 75.0 | 3.250 | 70 | 2.75 | 75 | 3.75 |\n| 35 | Trinitario | 71.74522 | 3.244019 | 5.3277734 | 0.4201463 | 70.0 | 3.250 | 55 | 1.75 | 100 | 5.00 |\n| 36 | Trinitario (85% Criollo) | 70.00000 | 3.875000 | 0.0000000 | 0.1767767 | 70.0 | 3.875 | 70 | 3.75 | 70 | 4.00 |\n| 37 | Trinitario (Amelonado) | 70.00000 | 3.000000 | NA | NA | 70.0 | 3.000 | 70 | 3.00 | 70 | 3.00 |\n| 38 | Trinitario (Scavina) | 70.00000 | 3.500000 | NA | NA | 70.0 | 3.500 | 70 | 3.50 | 70 | 3.50 |\n| 39 | Trinitario, Criollo | 68.33333 | 3.027778 | 3.2015621 | 0.5220818 | 70.0 | 3.000 | 65 | 2.25 | 74 | 3.75 |\n| 40 | Trinitario, Forastero | 70.50000 | 3.000000 | 6.3639610 | 0.0000000 | 70.5 | 3.000 | 66 | 3.00 | 75 | 3.00 |\n| 41 | Trinitario, Nacional | 70.00000 | 3.750000 | NA | NA | 70.0 | 3.750 | 70 | 3.75 | 70 | 3.75 |\n| 42 | Trinitario, TCGA | 72.00000 | 3.750000 | NA | NA | 72.0 | 3.750 | 72 | 3.75 | 72 | 3.75 |\n\n","text/latex":"A data.frame: 42 × 11\n\\begin{tabular}{r|lllllllllll}\n & Bean.Type & Cocoa.Percent.mean & Rating.mean & Cocoa.Percent.sd & Rating.sd & Cocoa.Percent.median & Rating.median & Cocoa.Percent.min & Rating.min & Cocoa.Percent.max & Rating.max\\\\\n & & & & & & & & & & & \\\\\n\\hline\n\t1 & & 70.00000 & 4.000000 & NA & NA & 70.0 & 4.000 & 70 & 4.00 & 70 & 4.00\\\\\n\t2 &   & 71.59174 & 3.134748 & 6.5931601 & 0.4841544 & 70.0 & 3.250 & 42 & 1.00 & 100 & 4.00\\\\\n\t3 & Amazon & 70.00000 & 3.250000 & NA & NA & 70.0 & 3.250 & 70 & 3.25 & 70 & 3.25\\\\\n\t4 & Amazon mix & 74.00000 & 3.750000 & 0.0000000 & 0.3535534 & 74.0 & 3.750 & 74 & 3.50 & 74 & 4.00\\\\\n\t5 & Amazon, ICS & 67.50000 & 3.625000 & 0.7071068 & 0.1767767 & 67.5 & 3.625 & 67 & 3.50 & 68 & 3.75\\\\\n\t6 & Beniano & 71.00000 & 3.583333 & 3.6055513 & 0.5204165 & 72.0 & 3.750 & 67 & 3.00 & 74 & 4.00\\\\\n\t7 & Blend & 71.39024 & 3.353659 & 6.4532087 & 0.5561464 & 70.0 & 3.500 & 55 & 2.00 & 91 & 5.00\\\\\n\t8 & Blend-Forastero,Criollo & 70.00000 & 3.750000 & NA & NA & 70.0 & 3.750 & 70 & 3.75 & 70 & 3.75\\\\\n\t9 & CCN51 & 65.00000 & 3.500000 & NA & NA & 65.0 & 3.500 & 65 & 3.50 & 65 & 3.50\\\\\n\t10 & Criollo & 72.06711 & 3.239933 & 5.8351966 & 0.4603164 & 71.0 & 3.250 & 58 & 2.00 & 100 & 4.00\\\\\n\t11 & Criollo (Amarru) & 70.00000 & 3.250000 & 0.0000000 & 0.3535534 & 70.0 & 3.250 & 70 & 3.00 & 70 & 3.50\\\\\n\t12 & Criollo (Ocumare 61) & 73.50000 & 3.250000 & 2.1213203 & 0.0000000 & 73.5 & 3.250 & 72 & 3.25 & 75 & 3.25\\\\\n\t13 & Criollo (Ocumare 67) & 70.00000 & 4.000000 & NA & NA & 70.0 & 4.000 & 70 & 4.00 & 70 & 4.00\\\\\n\t14 & Criollo (Ocumare 77) & 70.00000 & 3.750000 & NA & NA & 70.0 & 3.750 & 70 & 3.75 & 70 & 3.75\\\\\n\t15 & Criollo (Ocumare) & 80.00000 & 3.000000 & NA & NA & 80.0 & 3.000 & 80 & 3.00 & 80 & 3.00\\\\\n\t16 & Criollo (Porcelana) & 70.30000 & 3.375000 & 3.9735235 & 0.5034602 & 70.0 & 3.250 & 64 & 2.50 & 75 & 4.00\\\\\n\t17 & Criollo (Wild) & 68.00000 & 4.000000 & NA & NA & 68.0 & 4.000 & 68 & 4.00 & 68 & 4.00\\\\\n\t18 & Criollo, + & 65.00000 & 3.500000 & NA & NA & 65.0 & 3.500 & 65 & 3.50 & 65 & 3.50\\\\\n\t19 & Criollo, Forastero & 76.50000 & 3.625000 & 2.1213203 & 0.1767767 & 76.5 & 3.625 & 75 & 3.50 & 78 & 3.75\\\\\n\t20 & Criollo, Trinitario & 72.20513 & 3.294872 & 4.6406640 & 0.4009520 & 70.0 & 3.250 & 61 & 2.50 & 85 & 4.00\\\\\n\t21 & EET & 71.33333 & 3.583333 & 5.1316014 & 0.1443376 & 70.0 & 3.500 & 67 & 3.50 & 77 & 3.75\\\\\n\t22 & Forastero & 72.05747 & 3.100575 & 6.5850541 & 0.5438073 & 70.0 & 3.000 & 57 & 1.00 & 100 & 4.00\\\\\n\t23 & Forastero (Amelonado) & 70.00000 & 3.750000 & NA & NA & 70.0 & 3.750 & 70 & 3.75 & 70 & 3.75\\\\\n\t24 & Forastero (Arriba) & 73.13514 & 2.831081 & 12.0511921 & 0.6039628 & 72.0 & 3.000 & 55 & 1.50 & 100 & 4.00\\\\\n\t25 & Forastero (Arriba) ASS & 68.66667 & 2.875000 & 10.1718566 & 0.5419871 & 68.0 & 3.000 & 58 & 2.00 & 80 & 3.50\\\\\n\t26 & Forastero (Arriba) ASSS & 70.00000 & 3.500000 & NA & NA & 70.0 & 3.500 & 70 & 3.50 & 70 & 3.50\\\\\n\t27 & Forastero (Catongo) & 72.50000 & 3.375000 & 3.5355339 & 0.1767767 & 72.5 & 3.375 & 70 & 3.25 & 75 & 3.50\\\\\n\t28 & Forastero (Nacional) & 73.01923 & 3.269231 & 7.4845996 & 0.4368059 & 70.0 & 3.250 & 53 & 2.00 & 100 & 4.00\\\\\n\t29 & Forastero (Parazinho) & 68.75000 & 3.531250 & 10.2643628 & 0.3644345 & 70.0 & 3.500 & 50 & 2.75 & 85 & 4.00\\\\\n\t30 & Forastero, Trinitario & 70.00000 & 3.000000 & NA & NA & 70.0 & 3.000 & 70 & 3.00 & 70 & 3.00\\\\\n\t31 & Forastero(Arriba, CCN) & 75.00000 & 3.000000 & NA & NA & 75.0 & 3.000 & 75 & 3.00 & 75 & 3.00\\\\\n\t32 & Matina & 74.00000 & 3.416667 & 6.9282032 & 0.3818813 & 70.0 & 3.500 & 70 & 3.00 & 82 & 3.75\\\\\n\t33 & Nacional & 71.00000 & 3.000000 & 1.4142136 & 0.7071068 & 71.0 & 3.000 & 70 & 2.50 & 72 & 3.50\\\\\n\t34 & Nacional (Arriba) & 73.33333 & 3.250000 & 2.8867513 & 0.5000000 & 75.0 & 3.250 & 70 & 2.75 & 75 & 3.75\\\\\n\t35 & Trinitario & 71.74522 & 3.244019 & 5.3277734 & 0.4201463 & 70.0 & 3.250 & 55 & 1.75 & 100 & 5.00\\\\\n\t36 & Trinitario (85\\% Criollo) & 70.00000 & 3.875000 & 0.0000000 & 0.1767767 & 70.0 & 3.875 & 70 & 3.75 & 70 & 4.00\\\\\n\t37 & Trinitario (Amelonado) & 70.00000 & 3.000000 & NA & NA & 70.0 & 3.000 & 70 & 3.00 & 70 & 3.00\\\\\n\t38 & Trinitario (Scavina) & 70.00000 & 3.500000 & NA & NA & 70.0 & 3.500 & 70 & 3.50 & 70 & 3.50\\\\\n\t39 & Trinitario, Criollo & 68.33333 & 3.027778 & 3.2015621 & 0.5220818 & 70.0 & 3.000 & 65 & 2.25 & 74 & 3.75\\\\\n\t40 & Trinitario, Forastero & 70.50000 & 3.000000 & 6.3639610 & 0.0000000 & 70.5 & 3.000 & 66 & 3.00 & 75 & 3.00\\\\\n\t41 & Trinitario, Nacional & 70.00000 & 3.750000 & NA & NA & 70.0 & 3.750 & 70 & 3.75 & 70 & 3.75\\\\\n\t42 & Trinitario, TCGA & 72.00000 & 3.750000 & NA & NA & 72.0 & 3.750 & 72 & 3.75 & 72 & 3.75\\\\\n\\end{tabular}\n","text/plain":[" Bean.Type Cocoa.Percent.mean Rating.mean Cocoa.Percent.sd\n","1 70.00000 4.000000 NA \n","2   71.59174 3.134748 6.5931601 \n","3 Amazon 70.00000 3.250000 NA \n","4 Amazon mix 74.00000 3.750000 0.0000000 \n","5 Amazon, ICS 67.50000 3.625000 0.7071068 \n","6 Beniano 71.00000 3.583333 3.6055513 \n","7 Blend 71.39024 3.353659 6.4532087 \n","8 Blend-Forastero,Criollo 70.00000 3.750000 NA \n","9 CCN51 65.00000 3.500000 NA \n","10 Criollo 72.06711 3.239933 5.8351966 \n","11 Criollo (Amarru) 70.00000 3.250000 0.0000000 \n","12 Criollo (Ocumare 61) 73.50000 3.250000 2.1213203 \n","13 Criollo (Ocumare 67) 70.00000 4.000000 NA \n","14 Criollo (Ocumare 77) 70.00000 3.750000 NA \n","15 Criollo (Ocumare) 80.00000 3.000000 NA \n","16 Criollo (Porcelana) 70.30000 3.375000 3.9735235 \n","17 Criollo (Wild) 68.00000 4.000000 NA \n","18 Criollo, + 65.00000 3.500000 NA \n","19 Criollo, Forastero 76.50000 3.625000 2.1213203 \n","20 Criollo, Trinitario 72.20513 3.294872 4.6406640 \n","21 EET 71.33333 3.583333 5.1316014 \n","22 Forastero 72.05747 3.100575 6.5850541 \n","23 Forastero (Amelonado) 70.00000 3.750000 NA \n","24 Forastero (Arriba) 73.13514 2.831081 12.0511921 \n","25 Forastero (Arriba) ASS 68.66667 2.875000 10.1718566 \n","26 Forastero (Arriba) ASSS 70.00000 3.500000 NA \n","27 Forastero (Catongo) 72.50000 3.375000 3.5355339 \n","28 Forastero (Nacional) 73.01923 3.269231 7.4845996 \n","29 Forastero (Parazinho) 68.75000 3.531250 10.2643628 \n","30 Forastero, Trinitario 70.00000 3.000000 NA \n","31 Forastero(Arriba, CCN) 75.00000 3.000000 NA \n","32 Matina 74.00000 3.416667 6.9282032 \n","33 Nacional 71.00000 3.000000 1.4142136 \n","34 Nacional (Arriba) 73.33333 3.250000 2.8867513 \n","35 Trinitario 71.74522 3.244019 5.3277734 \n","36 Trinitario (85% Criollo) 70.00000 3.875000 0.0000000 \n","37 Trinitario (Amelonado) 70.00000 3.000000 NA \n","38 Trinitario (Scavina) 70.00000 3.500000 NA \n","39 Trinitario, Criollo 68.33333 3.027778 3.2015621 \n","40 Trinitario, Forastero 70.50000 3.000000 6.3639610 \n","41 Trinitario, Nacional 70.00000 3.750000 NA \n","42 Trinitario, TCGA 72.00000 3.750000 NA \n"," Rating.sd Cocoa.Percent.median Rating.median Cocoa.Percent.min Rating.min\n","1 NA 70.0 4.000 70 4.00 \n","2 0.4841544 70.0 3.250 42 1.00 \n","3 NA 70.0 3.250 70 3.25 \n","4 0.3535534 74.0 3.750 74 3.50 \n","5 0.1767767 67.5 3.625 67 3.50 \n","6 0.5204165 72.0 3.750 67 3.00 \n","7 0.5561464 70.0 3.500 55 2.00 \n","8 NA 70.0 3.750 70 3.75 \n","9 NA 65.0 3.500 65 3.50 \n","10 0.4603164 71.0 3.250 58 2.00 \n","11 0.3535534 70.0 3.250 70 3.00 \n","12 0.0000000 73.5 3.250 72 3.25 \n","13 NA 70.0 4.000 70 4.00 \n","14 NA 70.0 3.750 70 3.75 \n","15 NA 80.0 3.000 80 3.00 \n","16 0.5034602 70.0 3.250 64 2.50 \n","17 NA 68.0 4.000 68 4.00 \n","18 NA 65.0 3.500 65 3.50 \n","19 0.1767767 76.5 3.625 75 3.50 \n","20 0.4009520 70.0 3.250 61 2.50 \n","21 0.1443376 70.0 3.500 67 3.50 \n","22 0.5438073 70.0 3.000 57 1.00 \n","23 NA 70.0 3.750 70 3.75 \n","24 0.6039628 72.0 3.000 55 1.50 \n","25 0.5419871 68.0 3.000 58 2.00 \n","26 NA 70.0 3.500 70 3.50 \n","27 0.1767767 72.5 3.375 70 3.25 \n","28 0.4368059 70.0 3.250 53 2.00 \n","29 0.3644345 70.0 3.500 50 2.75 \n","30 NA 70.0 3.000 70 3.00 \n","31 NA 75.0 3.000 75 3.00 \n","32 0.3818813 70.0 3.500 70 3.00 \n","33 0.7071068 71.0 3.000 70 2.50 \n","34 0.5000000 75.0 3.250 70 2.75 \n","35 0.4201463 70.0 3.250 55 1.75 \n","36 0.1767767 70.0 3.875 70 3.75 \n","37 NA 70.0 3.000 70 3.00 \n","38 NA 70.0 3.500 70 3.50 \n","39 0.5220818 70.0 3.000 65 2.25 \n","40 0.0000000 70.5 3.000 66 3.00 \n","41 NA 70.0 3.750 70 3.75 \n","42 NA 72.0 3.750 72 3.75 \n"," Cocoa.Percent.max Rating.max\n","1 70 4.00 \n","2 100 4.00 \n","3 70 3.25 \n","4 74 4.00 \n","5 68 3.75 \n","6 74 4.00 \n","7 91 5.00 \n","8 70 3.75 \n","9 65 3.50 \n","10 100 4.00 \n","11 70 3.50 \n","12 75 3.25 \n","13 70 4.00 \n","14 70 3.75 \n","15 80 3.00 \n","16 75 4.00 \n","17 68 4.00 \n","18 65 3.50 \n","19 78 3.75 \n","20 85 4.00 \n","21 77 3.75 \n","22 100 4.00 \n","23 70 3.75 \n","24 100 4.00 \n","25 80 3.50 \n","26 70 3.50 \n","27 75 3.50 \n","28 100 4.00 \n","29 85 4.00 \n","30 70 3.00 \n","31 75 3.00 \n","32 82 3.75 \n","33 72 3.50 \n","34 75 3.75 \n","35 100 5.00 \n","36 70 4.00 \n","37 70 3.00 \n","38 70 3.50 \n","39 74 3.75 \n","40 75 3.00 \n","41 70 3.75 \n","42 72 3.75 "]},"metadata":{}},{"output_type":"stream","name":"stdout","text":[" Bean type Mean St.Dev. Median Min. Max. NA NA NA NA\n","1 70.0 4.00 NA NA 70.0 4.00 70 4.00 70\n","2   71.6 3.13 6.593 0.484 70.0 3.25 42 1.00 100\n","3 Amazon 70.0 3.25 NA NA 70.0 3.25 70 3.25 70\n","4 Amazon mix 74.0 3.75 0.000 0.354 74.0 3.75 74 3.50 74\n","5 Amazon, ICS 67.5 3.62 0.707 0.177 67.5 3.62 67 3.50 68\n","6 Beniano 71.0 3.58 3.606 0.520 72.0 3.75 67 3.00 74\n","7 Blend 71.4 3.35 6.453 0.556 70.0 3.50 55 2.00 91\n","8 Blend-Forastero,Criollo 70.0 3.75 NA NA 70.0 3.75 70 3.75 70\n","9 CCN51 65.0 3.50 NA NA 65.0 3.50 65 3.50 65\n","10 Criollo 72.1 3.24 5.835 0.460 71.0 3.25 58 2.00 100\n","11 Criollo (Amarru) 70.0 3.25 0.000 0.354 70.0 3.25 70 3.00 70\n","12 Criollo (Ocumare 61) 73.5 3.25 2.121 0.000 73.5 3.25 72 3.25 75\n","13 Criollo (Ocumare 67) 70.0 4.00 NA NA 70.0 4.00 70 4.00 70\n","14 Criollo (Ocumare 77) 70.0 3.75 NA NA 70.0 3.75 70 3.75 70\n","15 Criollo (Ocumare) 80.0 3.00 NA NA 80.0 3.00 80 3.00 80\n","16 Criollo (Porcelana) 70.3 3.38 3.974 0.503 70.0 3.25 64 2.50 75\n","17 Criollo (Wild) 68.0 4.00 NA NA 68.0 4.00 68 4.00 68\n","18 Criollo, + 65.0 3.50 NA NA 65.0 3.50 65 3.50 65\n","19 Criollo, Forastero 76.5 3.62 2.121 0.177 76.5 3.62 75 3.50 78\n","20 Criollo, Trinitario 72.2 3.29 4.641 0.401 70.0 3.25 61 2.50 85\n","21 EET 71.3 3.58 5.132 0.144 70.0 3.50 67 3.50 77\n","22 Forastero 72.1 3.10 6.585 0.544 70.0 3.00 57 1.00 100\n","23 Forastero (Amelonado) 70.0 3.75 NA NA 70.0 3.75 70 3.75 70\n","24 Forastero (Arriba) 73.1 2.83 12.051 0.604 72.0 3.00 55 1.50 100\n","25 Forastero (Arriba) ASS 68.7 2.88 10.172 0.542 68.0 3.00 58 2.00 80\n","26 Forastero (Arriba) ASSS 70.0 3.50 NA NA 70.0 3.50 70 3.50 70\n","27 Forastero (Catongo) 72.5 3.38 3.536 0.177 72.5 3.38 70 3.25 75\n","28 Forastero (Nacional) 73.0 3.27 7.485 0.437 70.0 3.25 53 2.00 100\n","29 Forastero (Parazinho) 68.8 3.53 10.264 0.364 70.0 3.50 50 2.75 85\n","30 Forastero, Trinitario 70.0 3.00 NA NA 70.0 3.00 70 3.00 70\n","31 Forastero(Arriba, CCN) 75.0 3.00 NA NA 75.0 3.00 75 3.00 75\n","32 Matina 74.0 3.42 6.928 0.382 70.0 3.50 70 3.00 82\n","33 Nacional 71.0 3.00 1.414 0.707 71.0 3.00 70 2.50 72\n","34 Nacional (Arriba) 73.3 3.25 2.887 0.500 75.0 3.25 70 2.75 75\n","35 Trinitario 71.7 3.24 5.328 0.420 70.0 3.25 55 1.75 100\n","36 Trinitario (85% Criollo) 70.0 3.88 0.000 0.177 70.0 3.88 70 3.75 70\n","37 Trinitario (Amelonado) 70.0 3.00 NA NA 70.0 3.00 70 3.00 70\n","38 Trinitario (Scavina) 70.0 3.50 NA NA 70.0 3.50 70 3.50 70\n","39 Trinitario, Criollo 68.3 3.03 3.202 0.522 70.0 3.00 65 2.25 74\n","40 Trinitario, Forastero 70.5 3.00 6.364 0.000 70.5 3.00 66 3.00 75\n","41 Trinitario, Nacional 70.0 3.75 NA NA 70.0 3.75 70 3.75 70\n","42 Trinitario, TCGA 72.0 3.75 NA NA 72.0 3.75 72 3.75 72\n"," NA\n","1 4.00\n","2 4.00\n","3 3.25\n","4 4.00\n","5 3.75\n","6 4.00\n","7 5.00\n","8 3.75\n","9 3.50\n","10 4.00\n","11 3.50\n","12 3.25\n","13 4.00\n","14 3.75\n","15 3.00\n","16 4.00\n","17 4.00\n","18 3.50\n","19 3.75\n","20 4.00\n","21 3.75\n","22 4.00\n","23 3.75\n","24 4.00\n","25 3.50\n","26 3.50\n","27 3.50\n","28 4.00\n","29 4.00\n","30 3.00\n","31 3.00\n","32 3.75\n","33 3.50\n","34 3.75\n","35 5.00\n","36 4.00\n","37 3.00\n","38 3.50\n","39 3.75\n","40 3.00\n","41 3.75\n","42 3.75\n"]}],"source":["#more than one quantitative variables vs one categorical variable\n","sumby <- summaryBy(Cocoa.Percent + Rating~ Bean.Type, choco.na, FUN = c(mean, sd, median, min, max))\n","sumby\n","#tunning\n","names(sumby) <- c(\"Bean type\", \"Mean\", \"St.Dev.\", \"Median\", \"Min.\", \"Max.\")\n","print(sumby, digits = 3)"]},{"cell_type":"markdown","metadata":{"id":"ZDSB_q4EYl9B"},"source":["# EXTRA EXERCISES\n","\n","\n","---\n","\n"]},{"cell_type":"markdown","metadata":{"id":"AicsXwTR_BjY"},"source":["\n","PROPOSED EXERCISE FOR STUDENTS\n","\n","An example of statistical data analysis\n","using the R environment for statistical computing:\n","\n","\n","\n","\n","Air Pollution in the United States\n","\n","For this chapter, we will use a simple case study to demonstrate the kinds of simple graphs that can be useful in exploratory analyses. The data we will be using come from the U.S. Environmental Protection Agency (EPA), which is the U.S. government agency that sets national air quality standards for outdoor air pollution. One of the national ambient air quality standards in the U.S. concerns the long-term average level of fine particle pollution, also referred to as PM2.5. Here, the standard says that the “annual mean, averaged over 3 years” cannot exceed 12 micrograms per cubic meter. Data on daily PM2.5 are available from the U.S. EPA web site, or specifically, the EPA Air Quality System web site.\n","\n","One key question we are interested is: Are there any counties in the U.S. that exceed the national standard for fine particle pollution? This question has important consequences because counties that are found to be in violation of the national standards can face serious legal consequences. In particular, states that have counties in violation of the standards are required to create a State Implementation Plan (SIP) that shows how those counties will come within the national standards within a given period of time.\n","\n","\n","Donwload the file: avgpm25.csv in your RSTUDIO and do the proposed exercise\n","\n","Use\n","\n","```{r}\n","pollution <- read.csv(\"avgpm25.csv\", header=T)\n","View(pollution)\n","\n","```\n","\n","\n","\n"]},{"cell_type":"markdown","source":["Try do to an EDA analysis using the dataframe:\n","airquality\n","help(airquality)"],"metadata":{"id":"MkD5CJ4WTNUF"}},{"cell_type":"markdown","source":["More exercises to do EDA:"],"metadata":{"id":"VPmQ3IMKK16p"}},{"cell_type":"markdown","metadata":{"id":"qqKqu2eT-fU2"},"source":["Study of the diamont characteristics in: https://www.statology.org/exploratory-data-analysis-in-r/"]},{"cell_type":"markdown","metadata":{"id":"ROGkI_IOMvrL"},"source":["Try to do extra exercises in: https://datasciencebook.ca/intro.html#exploring-data-with-visualizations"]},{"cell_type":"markdown","source":["# PATTERNS"],"metadata":{"id":"EAxNrqqAD4Wp"}},{"cell_type":"markdown","source":["\n","\n","![](http://languagelog.ldc.upenn.edu/~bgzimmer/data1.jpg)\n","\n"],"metadata":{"id":"xtYtiDb9DuhJ"}},{"cell_type":"markdown","source":["Absolutely, Exploratory Data Analysis (EDA) is a crucial step in data analysis specifically focused on uncovering patterns and gaining insights from a dataset. It's like sifting through sand to find hidden treasures.\n","\n","Here's how EDA helps with finding patterns:\n","\n","* Unveiling Trends and Relationships: EDA uses visualization techniques like histograms, scatter plots, and boxplots to uncover trends and relationships between different variables in your data. Imagine a scatter plot revealing a positive correlation between customer age and purchase amount.\n","\n","* Detecting Outliers: EDA helps identify outliers, data points that fall far outside the expected range. These outliers can skew your analysis, and EDA allows you to spot them for further investigation.\n","\n","* Understanding Data Distribution: EDA uses statistical methods to summarize the data, like calculating measures of central tendency (mean, median) and dispersion (standard deviation). This helps understand how the data is distributed and where patterns might lie.\n","\n","By applying these techniques, EDA helps you discover hidden patterns, ask new questions, and formulate hypotheses for further analysis. It's like getting a feel for the data before diving into complex modeling techniques."],"metadata":{"id":"vcPXMyFxEhCh"}},{"cell_type":"markdown","source":["# Mosaic Plot in R\n","The Mosaic Plot in R Programming is very useful to visualize the data from the contingency table or two-way frequency table. The R Mosaic Plot draws a rectangle, and its height represents the proportional value. From the second example, you see the White color products are the least selling in all the countries.\n","\n","Let us see how to Create a Mosaic Plot in R, Format its color, borders, shades, and changing directions of the mosaic plot in R Programming language with example."],"metadata":{"id":"wqWWxsPTLNx6"}},{"cell_type":"markdown","source":["See and explore the dataframe INFERT:\n","\n","We can learn more about the dataset with the help(infert) command and we’ll learn that it is infert: Infertility after Spontaneous and Induced Abortion. See more in:\n"," Trichopoulos, D., Handanos, N., Danezis, J., Kalandidi, A., &\n"," Kalapothaki, V. (1976). Induced abortion and secondary\n"," infertility. _British Journal of Obstetrics and Gynaecology_,\n"," *83*, 645-650. doi:10.1111/j.1471-0528.1976.tb00904.x\n"," .\n","\n"," Variables in:\n"," 1. Education 0 = 0-5 years \n"," 1 = 6-11 years \n"," 2 = 12+ years \n"," 2. age age in years of case\n"," 3. parity count \n"," 4. number of prior 0 = 0 \n"," induced abortions 1 = 1 \n"," 2 = 2 or more \n"," 5. case status 1 = case \n"," 0 = control \n"," 6. number of prior 0 = 0 \n"," spontaneous abortions 1 = 1 \n"," 2 = 2 or more \n"," 7. matched set number 1-83 \n"," 8. stratum number 1-63 "],"metadata":{"id":"pG-wcI97LWEQ"}},{"cell_type":"code","source":["data(infert)\n","infert\n","help(infert)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":1000},"id":"qHEbHwncR8K5","executionInfo":{"status":"ok","timestamp":1716800523744,"user_tz":-120,"elapsed":263,"user":{"displayName":"toni Monleon","userId":"05269440626861479105"}},"outputId":"c6c84d24-c302-4a47-d9a5-59bf9eafceeb"},"execution_count":7,"outputs":[{"output_type":"display_data","data":{"text/html":["\n","\n","\n","\t\n","\t\n","\n","\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\n","
A data.frame: 248 × 8
educationageparityinducedcasespontaneousstratumpooled.stratum
<fct><dbl><dbl><dbl><dbl><dbl><int><dbl>
0-5yrs 266112 1 3
0-5yrs 421110 2 1
0-5yrs 396210 3 4
0-5yrs 344210 4 2
6-11yrs353111 532
6-11yrs364211 636
6-11yrs231010 7 6
6-11yrs322010 822
6-11yrs211011 9 5
6-11yrs2820101019
6-11yrs2921101120
6-11yrs3742111237
6-11yrs31111013 9
6-11yrs2932101429
6-11yrs3121111521
6-11yrs2722101618
6-11yrs3052111738
6-11yrs26101118 7
6-11yrs2532111928
6-11yrs4410112017
6-11yrs4010112114
6-11yrs3522102224
6-11yrs2820122319
6-11yrs3610112412
6-11yrs2721112518
6-11yrs4020122627
6-11yrs3820122726
6-11yrs3430122831
6-11yrs2841122934
6-11yrs3042103035
⋮⋮⋮⋮⋮⋮⋮⋮
12+ yrs2810015442
12+ yrs2921015552
12+ yrs3620015655
12+ yrs2822005751
12+ yrs2821005851
12+ yrs2810005942
12+ yrs2721006050
12+ yrs3521006154
12+ yrs2511006241
12+ yrs3410006347
12+ yrs3121006453
12+ yrs2620026549
12+ yrs3211006646
12+ yrs2110006739
12+ yrs2832006858
12+ yrs3730026959
12+ yrs2511007041
12+ yrs3210007146
12+ yrs2511007241
12+ yrs3110007345
12+ yrs3860027463
12+ yrs2621017549
12+ yrs3111007645
12+ yrs3120017753
12+ yrs2510017841
12+ yrs3110017945
12+ yrs3410008047
12+ yrs3522008154
12+ yrs2910018243
12+ yrs2310018340
\n"],"text/markdown":"\nA data.frame: 248 × 8\n\n| education <fct> | age <dbl> | parity <dbl> | induced <dbl> | case <dbl> | spontaneous <dbl> | stratum <int> | pooled.stratum <dbl> |\n|---|---|---|---|---|---|---|---|\n| 0-5yrs | 26 | 6 | 1 | 1 | 2 | 1 | 3 |\n| 0-5yrs | 42 | 1 | 1 | 1 | 0 | 2 | 1 |\n| 0-5yrs | 39 | 6 | 2 | 1 | 0 | 3 | 4 |\n| 0-5yrs | 34 | 4 | 2 | 1 | 0 | 4 | 2 |\n| 6-11yrs | 35 | 3 | 1 | 1 | 1 | 5 | 32 |\n| 6-11yrs | 36 | 4 | 2 | 1 | 1 | 6 | 36 |\n| 6-11yrs | 23 | 1 | 0 | 1 | 0 | 7 | 6 |\n| 6-11yrs | 32 | 2 | 0 | 1 | 0 | 8 | 22 |\n| 6-11yrs | 21 | 1 | 0 | 1 | 1 | 9 | 5 |\n| 6-11yrs | 28 | 2 | 0 | 1 | 0 | 10 | 19 |\n| 6-11yrs | 29 | 2 | 1 | 1 | 0 | 11 | 20 |\n| 6-11yrs | 37 | 4 | 2 | 1 | 1 | 12 | 37 |\n| 6-11yrs | 31 | 1 | 1 | 1 | 0 | 13 | 9 |\n| 6-11yrs | 29 | 3 | 2 | 1 | 0 | 14 | 29 |\n| 6-11yrs | 31 | 2 | 1 | 1 | 1 | 15 | 21 |\n| 6-11yrs | 27 | 2 | 2 | 1 | 0 | 16 | 18 |\n| 6-11yrs | 30 | 5 | 2 | 1 | 1 | 17 | 38 |\n| 6-11yrs | 26 | 1 | 0 | 1 | 1 | 18 | 7 |\n| 6-11yrs | 25 | 3 | 2 | 1 | 1 | 19 | 28 |\n| 6-11yrs | 44 | 1 | 0 | 1 | 1 | 20 | 17 |\n| 6-11yrs | 40 | 1 | 0 | 1 | 1 | 21 | 14 |\n| 6-11yrs | 35 | 2 | 2 | 1 | 0 | 22 | 24 |\n| 6-11yrs | 28 | 2 | 0 | 1 | 2 | 23 | 19 |\n| 6-11yrs | 36 | 1 | 0 | 1 | 1 | 24 | 12 |\n| 6-11yrs | 27 | 2 | 1 | 1 | 1 | 25 | 18 |\n| 6-11yrs | 40 | 2 | 0 | 1 | 2 | 26 | 27 |\n| 6-11yrs | 38 | 2 | 0 | 1 | 2 | 27 | 26 |\n| 6-11yrs | 34 | 3 | 0 | 1 | 2 | 28 | 31 |\n| 6-11yrs | 28 | 4 | 1 | 1 | 2 | 29 | 34 |\n| 6-11yrs | 30 | 4 | 2 | 1 | 0 | 30 | 35 |\n| ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ |\n| 12+ yrs | 28 | 1 | 0 | 0 | 1 | 54 | 42 |\n| 12+ yrs | 29 | 2 | 1 | 0 | 1 | 55 | 52 |\n| 12+ yrs | 36 | 2 | 0 | 0 | 1 | 56 | 55 |\n| 12+ yrs | 28 | 2 | 2 | 0 | 0 | 57 | 51 |\n| 12+ yrs | 28 | 2 | 1 | 0 | 0 | 58 | 51 |\n| 12+ yrs | 28 | 1 | 0 | 0 | 0 | 59 | 42 |\n| 12+ yrs | 27 | 2 | 1 | 0 | 0 | 60 | 50 |\n| 12+ yrs | 35 | 2 | 1 | 0 | 0 | 61 | 54 |\n| 12+ yrs | 25 | 1 | 1 | 0 | 0 | 62 | 41 |\n| 12+ yrs | 34 | 1 | 0 | 0 | 0 | 63 | 47 |\n| 12+ yrs | 31 | 2 | 1 | 0 | 0 | 64 | 53 |\n| 12+ yrs | 26 | 2 | 0 | 0 | 2 | 65 | 49 |\n| 12+ yrs | 32 | 1 | 1 | 0 | 0 | 66 | 46 |\n| 12+ yrs | 21 | 1 | 0 | 0 | 0 | 67 | 39 |\n| 12+ yrs | 28 | 3 | 2 | 0 | 0 | 68 | 58 |\n| 12+ yrs | 37 | 3 | 0 | 0 | 2 | 69 | 59 |\n| 12+ yrs | 25 | 1 | 1 | 0 | 0 | 70 | 41 |\n| 12+ yrs | 32 | 1 | 0 | 0 | 0 | 71 | 46 |\n| 12+ yrs | 25 | 1 | 1 | 0 | 0 | 72 | 41 |\n| 12+ yrs | 31 | 1 | 0 | 0 | 0 | 73 | 45 |\n| 12+ yrs | 38 | 6 | 0 | 0 | 2 | 74 | 63 |\n| 12+ yrs | 26 | 2 | 1 | 0 | 1 | 75 | 49 |\n| 12+ yrs | 31 | 1 | 1 | 0 | 0 | 76 | 45 |\n| 12+ yrs | 31 | 2 | 0 | 0 | 1 | 77 | 53 |\n| 12+ yrs | 25 | 1 | 0 | 0 | 1 | 78 | 41 |\n| 12+ yrs | 31 | 1 | 0 | 0 | 1 | 79 | 45 |\n| 12+ yrs | 34 | 1 | 0 | 0 | 0 | 80 | 47 |\n| 12+ yrs | 35 | 2 | 2 | 0 | 0 | 81 | 54 |\n| 12+ yrs | 29 | 1 | 0 | 0 | 1 | 82 | 43 |\n| 12+ yrs | 23 | 1 | 0 | 0 | 1 | 83 | 40 |\n\n","text/latex":"A data.frame: 248 × 8\n\\begin{tabular}{llllllll}\n education & age & parity & induced & case & spontaneous & stratum & pooled.stratum\\\\\n & & & & & & & \\\\\n\\hline\n\t 0-5yrs & 26 & 6 & 1 & 1 & 2 & 1 & 3\\\\\n\t 0-5yrs & 42 & 1 & 1 & 1 & 0 & 2 & 1\\\\\n\t 0-5yrs & 39 & 6 & 2 & 1 & 0 & 3 & 4\\\\\n\t 0-5yrs & 34 & 4 & 2 & 1 & 0 & 4 & 2\\\\\n\t 6-11yrs & 35 & 3 & 1 & 1 & 1 & 5 & 32\\\\\n\t 6-11yrs & 36 & 4 & 2 & 1 & 1 & 6 & 36\\\\\n\t 6-11yrs & 23 & 1 & 0 & 1 & 0 & 7 & 6\\\\\n\t 6-11yrs & 32 & 2 & 0 & 1 & 0 & 8 & 22\\\\\n\t 6-11yrs & 21 & 1 & 0 & 1 & 1 & 9 & 5\\\\\n\t 6-11yrs & 28 & 2 & 0 & 1 & 0 & 10 & 19\\\\\n\t 6-11yrs & 29 & 2 & 1 & 1 & 0 & 11 & 20\\\\\n\t 6-11yrs & 37 & 4 & 2 & 1 & 1 & 12 & 37\\\\\n\t 6-11yrs & 31 & 1 & 1 & 1 & 0 & 13 & 9\\\\\n\t 6-11yrs & 29 & 3 & 2 & 1 & 0 & 14 & 29\\\\\n\t 6-11yrs & 31 & 2 & 1 & 1 & 1 & 15 & 21\\\\\n\t 6-11yrs & 27 & 2 & 2 & 1 & 0 & 16 & 18\\\\\n\t 6-11yrs & 30 & 5 & 2 & 1 & 1 & 17 & 38\\\\\n\t 6-11yrs & 26 & 1 & 0 & 1 & 1 & 18 & 7\\\\\n\t 6-11yrs & 25 & 3 & 2 & 1 & 1 & 19 & 28\\\\\n\t 6-11yrs & 44 & 1 & 0 & 1 & 1 & 20 & 17\\\\\n\t 6-11yrs & 40 & 1 & 0 & 1 & 1 & 21 & 14\\\\\n\t 6-11yrs & 35 & 2 & 2 & 1 & 0 & 22 & 24\\\\\n\t 6-11yrs & 28 & 2 & 0 & 1 & 2 & 23 & 19\\\\\n\t 6-11yrs & 36 & 1 & 0 & 1 & 1 & 24 & 12\\\\\n\t 6-11yrs & 27 & 2 & 1 & 1 & 1 & 25 & 18\\\\\n\t 6-11yrs & 40 & 2 & 0 & 1 & 2 & 26 & 27\\\\\n\t 6-11yrs & 38 & 2 & 0 & 1 & 2 & 27 & 26\\\\\n\t 6-11yrs & 34 & 3 & 0 & 1 & 2 & 28 & 31\\\\\n\t 6-11yrs & 28 & 4 & 1 & 1 & 2 & 29 & 34\\\\\n\t 6-11yrs & 30 & 4 & 2 & 1 & 0 & 30 & 35\\\\\n\t ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮\\\\\n\t 12+ yrs & 28 & 1 & 0 & 0 & 1 & 54 & 42\\\\\n\t 12+ yrs & 29 & 2 & 1 & 0 & 1 & 55 & 52\\\\\n\t 12+ yrs & 36 & 2 & 0 & 0 & 1 & 56 & 55\\\\\n\t 12+ yrs & 28 & 2 & 2 & 0 & 0 & 57 & 51\\\\\n\t 12+ yrs & 28 & 2 & 1 & 0 & 0 & 58 & 51\\\\\n\t 12+ yrs & 28 & 1 & 0 & 0 & 0 & 59 & 42\\\\\n\t 12+ yrs & 27 & 2 & 1 & 0 & 0 & 60 & 50\\\\\n\t 12+ yrs & 35 & 2 & 1 & 0 & 0 & 61 & 54\\\\\n\t 12+ yrs & 25 & 1 & 1 & 0 & 0 & 62 & 41\\\\\n\t 12+ yrs & 34 & 1 & 0 & 0 & 0 & 63 & 47\\\\\n\t 12+ yrs & 31 & 2 & 1 & 0 & 0 & 64 & 53\\\\\n\t 12+ yrs & 26 & 2 & 0 & 0 & 2 & 65 & 49\\\\\n\t 12+ yrs & 32 & 1 & 1 & 0 & 0 & 66 & 46\\\\\n\t 12+ yrs & 21 & 1 & 0 & 0 & 0 & 67 & 39\\\\\n\t 12+ yrs & 28 & 3 & 2 & 0 & 0 & 68 & 58\\\\\n\t 12+ yrs & 37 & 3 & 0 & 0 & 2 & 69 & 59\\\\\n\t 12+ yrs & 25 & 1 & 1 & 0 & 0 & 70 & 41\\\\\n\t 12+ yrs & 32 & 1 & 0 & 0 & 0 & 71 & 46\\\\\n\t 12+ yrs & 25 & 1 & 1 & 0 & 0 & 72 & 41\\\\\n\t 12+ yrs & 31 & 1 & 0 & 0 & 0 & 73 & 45\\\\\n\t 12+ yrs & 38 & 6 & 0 & 0 & 2 & 74 & 63\\\\\n\t 12+ yrs & 26 & 2 & 1 & 0 & 1 & 75 & 49\\\\\n\t 12+ yrs & 31 & 1 & 1 & 0 & 0 & 76 & 45\\\\\n\t 12+ yrs & 31 & 2 & 0 & 0 & 1 & 77 & 53\\\\\n\t 12+ yrs & 25 & 1 & 0 & 0 & 1 & 78 & 41\\\\\n\t 12+ yrs & 31 & 1 & 0 & 0 & 1 & 79 & 45\\\\\n\t 12+ yrs & 34 & 1 & 0 & 0 & 0 & 80 & 47\\\\\n\t 12+ yrs & 35 & 2 & 2 & 0 & 0 & 81 & 54\\\\\n\t 12+ yrs & 29 & 1 & 0 & 0 & 1 & 82 & 43\\\\\n\t 12+ yrs & 23 & 1 & 0 & 0 & 1 & 83 & 40\\\\\n\\end{tabular}\n","text/plain":[" education age parity induced case spontaneous stratum pooled.stratum\n","1 0-5yrs 26 6 1 1 2 1 3 \n","2 0-5yrs 42 1 1 1 0 2 1 \n","3 0-5yrs 39 6 2 1 0 3 4 \n","4 0-5yrs 34 4 2 1 0 4 2 \n","5 6-11yrs 35 3 1 1 1 5 32 \n","6 6-11yrs 36 4 2 1 1 6 36 \n","7 6-11yrs 23 1 0 1 0 7 6 \n","8 6-11yrs 32 2 0 1 0 8 22 \n","9 6-11yrs 21 1 0 1 1 9 5 \n","10 6-11yrs 28 2 0 1 0 10 19 \n","11 6-11yrs 29 2 1 1 0 11 20 \n","12 6-11yrs 37 4 2 1 1 12 37 \n","13 6-11yrs 31 1 1 1 0 13 9 \n","14 6-11yrs 29 3 2 1 0 14 29 \n","15 6-11yrs 31 2 1 1 1 15 21 \n","16 6-11yrs 27 2 2 1 0 16 18 \n","17 6-11yrs 30 5 2 1 1 17 38 \n","18 6-11yrs 26 1 0 1 1 18 7 \n","19 6-11yrs 25 3 2 1 1 19 28 \n","20 6-11yrs 44 1 0 1 1 20 17 \n","21 6-11yrs 40 1 0 1 1 21 14 \n","22 6-11yrs 35 2 2 1 0 22 24 \n","23 6-11yrs 28 2 0 1 2 23 19 \n","24 6-11yrs 36 1 0 1 1 24 12 \n","25 6-11yrs 27 2 1 1 1 25 18 \n","26 6-11yrs 40 2 0 1 2 26 27 \n","27 6-11yrs 38 2 0 1 2 27 26 \n","28 6-11yrs 34 3 0 1 2 28 31 \n","29 6-11yrs 28 4 1 1 2 29 34 \n","30 6-11yrs 30 4 2 1 0 30 35 \n","⋮ ⋮ ⋮ ⋮ ⋮ ⋮ ⋮ ⋮ ⋮ \n","219 12+ yrs 28 1 0 0 1 54 42 \n","220 12+ yrs 29 2 1 0 1 55 52 \n","221 12+ yrs 36 2 0 0 1 56 55 \n","222 12+ yrs 28 2 2 0 0 57 51 \n","223 12+ yrs 28 2 1 0 0 58 51 \n","224 12+ yrs 28 1 0 0 0 59 42 \n","225 12+ yrs 27 2 1 0 0 60 50 \n","226 12+ yrs 35 2 1 0 0 61 54 \n","227 12+ yrs 25 1 1 0 0 62 41 \n","228 12+ yrs 34 1 0 0 0 63 47 \n","229 12+ yrs 31 2 1 0 0 64 53 \n","230 12+ yrs 26 2 0 0 2 65 49 \n","231 12+ yrs 32 1 1 0 0 66 46 \n","232 12+ yrs 21 1 0 0 0 67 39 \n","233 12+ yrs 28 3 2 0 0 68 58 \n","234 12+ yrs 37 3 0 0 2 69 59 \n","235 12+ yrs 25 1 1 0 0 70 41 \n","236 12+ yrs 32 1 0 0 0 71 46 \n","237 12+ yrs 25 1 1 0 0 72 41 \n","238 12+ yrs 31 1 0 0 0 73 45 \n","239 12+ yrs 38 6 0 0 2 74 63 \n","240 12+ yrs 26 2 1 0 1 75 49 \n","241 12+ yrs 31 1 1 0 0 76 45 \n","242 12+ yrs 31 2 0 0 1 77 53 \n","243 12+ yrs 25 1 0 0 1 78 41 \n","244 12+ yrs 31 1 0 0 1 79 45 \n","245 12+ yrs 34 1 0 0 0 80 47 \n","246 12+ yrs 35 2 2 0 0 81 54 \n","247 12+ yrs 29 1 0 0 1 82 43 \n","248 12+ yrs 23 1 0 0 1 83 40 "]},"metadata":{}}]},{"cell_type":"code","source":["\n","\n","\n"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":1000},"id":"pIkPPeSkQubm","executionInfo":{"status":"ok","timestamp":1716800197097,"user_tz":-120,"elapsed":286,"user":{"displayName":"toni Monleon","userId":"05269440626861479105"}},"outputId":"c5013846-6052-4d63-8828-57aef2568abf"},"execution_count":2,"outputs":[{"output_type":"display_data","data":{"text/html":["\n","\n","\n","\t\n","\t\n","\n","\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\t\n","\n","
A data.frame: 153 × 6
OzoneSolar.RWindTempMonthDay
<int><int><dbl><int><int><int>
41190 7.4675 1
36118 8.0725 2
1214912.6745 3
1831311.5625 4
NA NA14.3565 5
28 NA14.9665 6
23299 8.6655 7
19 9913.8595 8
8 1920.1615 9
NA194 8.669510
7 NA 6.974511
16256 9.769512
11290 9.266513
1427410.968514
18 6513.258515
1433411.564516
3430712.066517
6 7818.457518
3032211.568519
11 44 9.762520
1 8 9.759521
1132016.673522
4 25 9.761523
32 9212.061524
NA 6616.657525
NA26614.958526
NA NA 8.057527
23 1312.067528
4525214.981529
115223 5.779530
⋮⋮⋮⋮⋮⋮
96167 6.9919 1
78197 5.1929 2
73183 2.8939 3
91189 4.6939 4
47 95 7.4879 5
32 9215.5849 6
2025210.9809 7
2322010.3789 8
2123010.9759 9
24259 9.773910
4423614.981911
2125915.576912
28238 6.377913
9 2410.971914
1311211.571915
46237 6.978916
1822413.867917
13 2710.376918
2423810.368919
16201 8.082920
1323812.664921
23 14 9.271922
3613910.381923
7 4910.369924
14 2016.663925
30193 6.970926
NA14513.277927
1419114.375928
18131 8.076929
2022311.568930
\n"],"text/markdown":"\nA data.frame: 153 × 6\n\n| Ozone <int> | Solar.R <int> | Wind <dbl> | Temp <int> | Month <int> | Day <int> |\n|---|---|---|---|---|---|\n| 41 | 190 | 7.4 | 67 | 5 | 1 |\n| 36 | 118 | 8.0 | 72 | 5 | 2 |\n| 12 | 149 | 12.6 | 74 | 5 | 3 |\n| 18 | 313 | 11.5 | 62 | 5 | 4 |\n| NA | NA | 14.3 | 56 | 5 | 5 |\n| 28 | NA | 14.9 | 66 | 5 | 6 |\n| 23 | 299 | 8.6 | 65 | 5 | 7 |\n| 19 | 99 | 13.8 | 59 | 5 | 8 |\n| 8 | 19 | 20.1 | 61 | 5 | 9 |\n| NA | 194 | 8.6 | 69 | 5 | 10 |\n| 7 | NA | 6.9 | 74 | 5 | 11 |\n| 16 | 256 | 9.7 | 69 | 5 | 12 |\n| 11 | 290 | 9.2 | 66 | 5 | 13 |\n| 14 | 274 | 10.9 | 68 | 5 | 14 |\n| 18 | 65 | 13.2 | 58 | 5 | 15 |\n| 14 | 334 | 11.5 | 64 | 5 | 16 |\n| 34 | 307 | 12.0 | 66 | 5 | 17 |\n| 6 | 78 | 18.4 | 57 | 5 | 18 |\n| 30 | 322 | 11.5 | 68 | 5 | 19 |\n| 11 | 44 | 9.7 | 62 | 5 | 20 |\n| 1 | 8 | 9.7 | 59 | 5 | 21 |\n| 11 | 320 | 16.6 | 73 | 5 | 22 |\n| 4 | 25 | 9.7 | 61 | 5 | 23 |\n| 32 | 92 | 12.0 | 61 | 5 | 24 |\n| NA | 66 | 16.6 | 57 | 5 | 25 |\n| NA | 266 | 14.9 | 58 | 5 | 26 |\n| NA | NA | 8.0 | 57 | 5 | 27 |\n| 23 | 13 | 12.0 | 67 | 5 | 28 |\n| 45 | 252 | 14.9 | 81 | 5 | 29 |\n| 115 | 223 | 5.7 | 79 | 5 | 30 |\n| ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ |\n| 96 | 167 | 6.9 | 91 | 9 | 1 |\n| 78 | 197 | 5.1 | 92 | 9 | 2 |\n| 73 | 183 | 2.8 | 93 | 9 | 3 |\n| 91 | 189 | 4.6 | 93 | 9 | 4 |\n| 47 | 95 | 7.4 | 87 | 9 | 5 |\n| 32 | 92 | 15.5 | 84 | 9 | 6 |\n| 20 | 252 | 10.9 | 80 | 9 | 7 |\n| 23 | 220 | 10.3 | 78 | 9 | 8 |\n| 21 | 230 | 10.9 | 75 | 9 | 9 |\n| 24 | 259 | 9.7 | 73 | 9 | 10 |\n| 44 | 236 | 14.9 | 81 | 9 | 11 |\n| 21 | 259 | 15.5 | 76 | 9 | 12 |\n| 28 | 238 | 6.3 | 77 | 9 | 13 |\n| 9 | 24 | 10.9 | 71 | 9 | 14 |\n| 13 | 112 | 11.5 | 71 | 9 | 15 |\n| 46 | 237 | 6.9 | 78 | 9 | 16 |\n| 18 | 224 | 13.8 | 67 | 9 | 17 |\n| 13 | 27 | 10.3 | 76 | 9 | 18 |\n| 24 | 238 | 10.3 | 68 | 9 | 19 |\n| 16 | 201 | 8.0 | 82 | 9 | 20 |\n| 13 | 238 | 12.6 | 64 | 9 | 21 |\n| 23 | 14 | 9.2 | 71 | 9 | 22 |\n| 36 | 139 | 10.3 | 81 | 9 | 23 |\n| 7 | 49 | 10.3 | 69 | 9 | 24 |\n| 14 | 20 | 16.6 | 63 | 9 | 25 |\n| 30 | 193 | 6.9 | 70 | 9 | 26 |\n| NA | 145 | 13.2 | 77 | 9 | 27 |\n| 14 | 191 | 14.3 | 75 | 9 | 28 |\n| 18 | 131 | 8.0 | 76 | 9 | 29 |\n| 20 | 223 | 11.5 | 68 | 9 | 30 |\n\n","text/latex":"A data.frame: 153 × 6\n\\begin{tabular}{llllll}\n Ozone & Solar.R & Wind & Temp & Month & Day\\\\\n & & & & & \\\\\n\\hline\n\t 41 & 190 & 7.4 & 67 & 5 & 1\\\\\n\t 36 & 118 & 8.0 & 72 & 5 & 2\\\\\n\t 12 & 149 & 12.6 & 74 & 5 & 3\\\\\n\t 18 & 313 & 11.5 & 62 & 5 & 4\\\\\n\t NA & NA & 14.3 & 56 & 5 & 5\\\\\n\t 28 & NA & 14.9 & 66 & 5 & 6\\\\\n\t 23 & 299 & 8.6 & 65 & 5 & 7\\\\\n\t 19 & 99 & 13.8 & 59 & 5 & 8\\\\\n\t 8 & 19 & 20.1 & 61 & 5 & 9\\\\\n\t NA & 194 & 8.6 & 69 & 5 & 10\\\\\n\t 7 & NA & 6.9 & 74 & 5 & 11\\\\\n\t 16 & 256 & 9.7 & 69 & 5 & 12\\\\\n\t 11 & 290 & 9.2 & 66 & 5 & 13\\\\\n\t 14 & 274 & 10.9 & 68 & 5 & 14\\\\\n\t 18 & 65 & 13.2 & 58 & 5 & 15\\\\\n\t 14 & 334 & 11.5 & 64 & 5 & 16\\\\\n\t 34 & 307 & 12.0 & 66 & 5 & 17\\\\\n\t 6 & 78 & 18.4 & 57 & 5 & 18\\\\\n\t 30 & 322 & 11.5 & 68 & 5 & 19\\\\\n\t 11 & 44 & 9.7 & 62 & 5 & 20\\\\\n\t 1 & 8 & 9.7 & 59 & 5 & 21\\\\\n\t 11 & 320 & 16.6 & 73 & 5 & 22\\\\\n\t 4 & 25 & 9.7 & 61 & 5 & 23\\\\\n\t 32 & 92 & 12.0 & 61 & 5 & 24\\\\\n\t NA & 66 & 16.6 & 57 & 5 & 25\\\\\n\t NA & 266 & 14.9 & 58 & 5 & 26\\\\\n\t NA & NA & 8.0 & 57 & 5 & 27\\\\\n\t 23 & 13 & 12.0 & 67 & 5 & 28\\\\\n\t 45 & 252 & 14.9 & 81 & 5 & 29\\\\\n\t 115 & 223 & 5.7 & 79 & 5 & 30\\\\\n\t ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮\\\\\n\t 96 & 167 & 6.9 & 91 & 9 & 1\\\\\n\t 78 & 197 & 5.1 & 92 & 9 & 2\\\\\n\t 73 & 183 & 2.8 & 93 & 9 & 3\\\\\n\t 91 & 189 & 4.6 & 93 & 9 & 4\\\\\n\t 47 & 95 & 7.4 & 87 & 9 & 5\\\\\n\t 32 & 92 & 15.5 & 84 & 9 & 6\\\\\n\t 20 & 252 & 10.9 & 80 & 9 & 7\\\\\n\t 23 & 220 & 10.3 & 78 & 9 & 8\\\\\n\t 21 & 230 & 10.9 & 75 & 9 & 9\\\\\n\t 24 & 259 & 9.7 & 73 & 9 & 10\\\\\n\t 44 & 236 & 14.9 & 81 & 9 & 11\\\\\n\t 21 & 259 & 15.5 & 76 & 9 & 12\\\\\n\t 28 & 238 & 6.3 & 77 & 9 & 13\\\\\n\t 9 & 24 & 10.9 & 71 & 9 & 14\\\\\n\t 13 & 112 & 11.5 & 71 & 9 & 15\\\\\n\t 46 & 237 & 6.9 & 78 & 9 & 16\\\\\n\t 18 & 224 & 13.8 & 67 & 9 & 17\\\\\n\t 13 & 27 & 10.3 & 76 & 9 & 18\\\\\n\t 24 & 238 & 10.3 & 68 & 9 & 19\\\\\n\t 16 & 201 & 8.0 & 82 & 9 & 20\\\\\n\t 13 & 238 & 12.6 & 64 & 9 & 21\\\\\n\t 23 & 14 & 9.2 & 71 & 9 & 22\\\\\n\t 36 & 139 & 10.3 & 81 & 9 & 23\\\\\n\t 7 & 49 & 10.3 & 69 & 9 & 24\\\\\n\t 14 & 20 & 16.6 & 63 & 9 & 25\\\\\n\t 30 & 193 & 6.9 & 70 & 9 & 26\\\\\n\t NA & 145 & 13.2 & 77 & 9 & 27\\\\\n\t 14 & 191 & 14.3 & 75 & 9 & 28\\\\\n\t 18 & 131 & 8.0 & 76 & 9 & 29\\\\\n\t 20 & 223 & 11.5 & 68 & 9 & 30\\\\\n\\end{tabular}\n","text/plain":[" Ozone Solar.R Wind Temp Month Day\n","1 41 190 7.4 67 5 1 \n","2 36 118 8.0 72 5 2 \n","3 12 149 12.6 74 5 3 \n","4 18 313 11.5 62 5 4 \n","5 NA NA 14.3 56 5 5 \n","6 28 NA 14.9 66 5 6 \n","7 23 299 8.6 65 5 7 \n","8 19 99 13.8 59 5 8 \n","9 8 19 20.1 61 5 9 \n","10 NA 194 8.6 69 5 10 \n","11 7 NA 6.9 74 5 11 \n","12 16 256 9.7 69 5 12 \n","13 11 290 9.2 66 5 13 \n","14 14 274 10.9 68 5 14 \n","15 18 65 13.2 58 5 15 \n","16 14 334 11.5 64 5 16 \n","17 34 307 12.0 66 5 17 \n","18 6 78 18.4 57 5 18 \n","19 30 322 11.5 68 5 19 \n","20 11 44 9.7 62 5 20 \n","21 1 8 9.7 59 5 21 \n","22 11 320 16.6 73 5 22 \n","23 4 25 9.7 61 5 23 \n","24 32 92 12.0 61 5 24 \n","25 NA 66 16.6 57 5 25 \n","26 NA 266 14.9 58 5 26 \n","27 NA NA 8.0 57 5 27 \n","28 23 13 12.0 67 5 28 \n","29 45 252 14.9 81 5 29 \n","30 115 223 5.7 79 5 30 \n","⋮ ⋮ ⋮ ⋮ ⋮ ⋮ ⋮ \n","124 96 167 6.9 91 9 1 \n","125 78 197 5.1 92 9 2 \n","126 73 183 2.8 93 9 3 \n","127 91 189 4.6 93 9 4 \n","128 47 95 7.4 87 9 5 \n","129 32 92 15.5 84 9 6 \n","130 20 252 10.9 80 9 7 \n","131 23 220 10.3 78 9 8 \n","132 21 230 10.9 75 9 9 \n","133 24 259 9.7 73 9 10 \n","134 44 236 14.9 81 9 11 \n","135 21 259 15.5 76 9 12 \n","136 28 238 6.3 77 9 13 \n","137 9 24 10.9 71 9 14 \n","138 13 112 11.5 71 9 15 \n","139 46 237 6.9 78 9 16 \n","140 18 224 13.8 67 9 17 \n","141 13 27 10.3 76 9 18 \n","142 24 238 10.3 68 9 19 \n","143 16 201 8.0 82 9 20 \n","144 13 238 12.6 64 9 21 \n","145 23 14 9.2 71 9 22 \n","146 36 139 10.3 81 9 23 \n","147 7 49 10.3 69 9 24 \n","148 14 20 16.6 63 9 25 \n","149 30 193 6.9 70 9 26 \n","150 NA 145 13.2 77 9 27 \n","151 14 191 14.3 75 9 28 \n","152 18 131 8.0 76 9 29 \n","153 20 223 11.5 68 9 30 "]},"metadata":{}}]},{"cell_type":"code","source":["\n","\n","table1 <- table(infert$education, infert$induced)\n","mosaicplot(table1,color = \"skyblue2\",\n"," border = \"chocolate\")\n","\n"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":437},"id":"7ctP4j5PLZJB","executionInfo":{"status":"ok","timestamp":1716800839154,"user_tz":-120,"elapsed":380,"user":{"displayName":"toni Monleon","userId":"05269440626861479105"}},"outputId":"7d3dbb95-bd57-44b6-e08b-50517358f8c8"},"execution_count":8,"outputs":[{"output_type":"display_data","data":{"text/plain":["Plot with title “table1”"],"image/png":"iVBORw0KGgoAAAANSUhEUgAAA0gAAANICAIAAAByhViMAAAACXBIWXMAABJ0AAASdAHeZh94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\nAAAAAElFTkSuQmCC"},"metadata":{"image/png":{"width":420,"height":420}}}]},{"cell_type":"markdown","source":["Let’s do the chi-squared test using the chisq.test() function. It takes the two vectors as the input. We also set `correct=FALSE` to turn off Yates’ continuity correction.\n","\n"],"metadata":{"id":"MayOz2D8T8Us"}},{"cell_type":"code","source":["#Chi square test for contingency tables:\n","chisq.test(table1, correct=FALSE)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":138},"id":"GjRbZ0i1T0i6","executionInfo":{"status":"ok","timestamp":1716800985174,"user_tz":-120,"elapsed":259,"user":{"displayName":"toni Monleon","userId":"05269440626861479105"}},"outputId":"887decf3-e30f-4539-ecdb-cc7974761498"},"execution_count":9,"outputs":[{"output_type":"stream","name":"stderr","text":["Warning message in chisq.test(table1, correct = FALSE):\n","“Chi-squared approximation may be incorrect”\n"]},{"output_type":"display_data","data":{"text/plain":["\n","\tPearson's Chi-squared test\n","\n","data: table1\n","X-squared = 16.531, df = 4, p-value = 0.002384\n"]},"metadata":{}}]},{"cell_type":"markdown","source":["We can assure that the row variable and the column variable are not independent, since the p-value < 0.05"],"metadata":{"id":"Kw00HE73USJ8"}},{"cell_type":"markdown","source":["SEE MORE IN: https://www.tutorialgateway.org/mosaic-plot-in-r/"],"metadata":{"id":"Oj_q8MI7Lu-8"}},{"cell_type":"markdown","source":["#HEATMATS in R"],"metadata":{"id":"Tgdx80yDL7mz"}},{"cell_type":"code","source":["#see in https://r-graph-gallery.com/215-the-heatmap-function.html"],"metadata":{"id":"02PFM1QtLsbH"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["# The mtcars dataset:\n","data <- as.matrix(mtcars)\n","\n","# Default Heatmap\n","heatmap(data)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":437},"id":"PKsckfW3MFsP","executionInfo":{"status":"ok","timestamp":1716801083541,"user_tz":-120,"elapsed":684,"user":{"displayName":"toni Monleon","userId":"05269440626861479105"}},"outputId":"e9fa614f-5893-4283-a0d2-e8688ddd271e"},"execution_count":10,"outputs":[{"output_type":"display_data","data":{"text/plain":["plot without title"],"image/png":"iVBORw0KGgoAAAANSUhEUgAAA0gAAANICAIAAAByhViMAAAACXBIWXMAABJ0AAASdAHeZh94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\nQBt51+gfwS0AAAAASUVORK5CYII="},"metadata":{"image/png":{"width":420,"height":420}}}]},{"cell_type":"markdown","source":["\n","![](https://www.fintechfutures.com/files/2023/08/ArewereadyIanFoley.jpg )\n","\n"],"metadata":{"id":"Q7u-fENk_dY6"}}],"metadata":{"colab":{"provenance":[{"file_id":"1dneD0o5zf7OC4I-K4vr9XBmmX-E7IRtq","timestamp":1676999972013}],"authorship_tag":"ABX9TyMX3VYWtYMkKEdlmi32HlwW"},"kernelspec":{"display_name":"R","name":"ir"},"language_info":{"name":"R"}},"nbformat":4,"nbformat_minor":0}