{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "65f82e0b",
   "metadata": {},
   "source": [
    "Plotting categorical data is an important step in exploring and visualizing the distribution and relationships between categorical variables. In this tutorial, we will cover some common plots for categorical data using R. As usual, we will be employing **ggplot** for this."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "579b4fff",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "── \u001b[1mAttaching packages\u001b[22m ─────────────────────────────────────── tidyverse 1.3.2 ──\n",
      "\u001b[32m✔\u001b[39m \u001b[34mggplot2\u001b[39m 3.4.0      \u001b[32m✔\u001b[39m \u001b[34mpurrr  \u001b[39m 1.0.1 \n",
      "\u001b[32m✔\u001b[39m \u001b[34mtibble \u001b[39m 3.1.8      \u001b[32m✔\u001b[39m \u001b[34mdplyr  \u001b[39m 1.0.10\n",
      "\u001b[32m✔\u001b[39m \u001b[34mtidyr  \u001b[39m 1.2.1      \u001b[32m✔\u001b[39m \u001b[34mstringr\u001b[39m 1.5.0 \n",
      "\u001b[32m✔\u001b[39m \u001b[34mreadr  \u001b[39m 2.1.3      \u001b[32m✔\u001b[39m \u001b[34mforcats\u001b[39m 0.5.2 \n",
      "── \u001b[1mConflicts\u001b[22m ────────────────────────────────────────── tidyverse_conflicts() ──\n",
      "\u001b[31m✖\u001b[39m \u001b[34mdplyr\u001b[39m::\u001b[32mfilter()\u001b[39m masks \u001b[34mstats\u001b[39m::filter()\n",
      "\u001b[31m✖\u001b[39m \u001b[34mdplyr\u001b[39m::\u001b[32mlag()\u001b[39m    masks \u001b[34mstats\u001b[39m::lag()\n"
     ]
    }
   ],
   "source": [
    "library(tidyverse)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ee8500da",
   "metadata": {},
   "source": [
    "Now, to illustrate this in this tutorial, we will be utilizing a dataset from a clinical trial. The trial involved administering a drug to some participants and a placebo to others, and their progress was monitored. At the end of the study, it was determined whether the participants' progress had worsened, remained the same, or improved."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "a563455b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A data.frame: 6 × 2</caption>\n",
       "<thead>\n",
       "\t<tr><th></th><th scope=col>group</th><th scope=col>evolution</th></tr>\n",
       "\t<tr><th></th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>1</th><td>Placebo</td><td>Worse</td></tr>\n",
       "\t<tr><th scope=row>2</th><td>Placebo</td><td>Worse</td></tr>\n",
       "\t<tr><th scope=row>3</th><td>Placebo</td><td>Worse</td></tr>\n",
       "\t<tr><th scope=row>4</th><td>Placebo</td><td>Worse</td></tr>\n",
       "\t<tr><th scope=row>5</th><td>Placebo</td><td>Worse</td></tr>\n",
       "\t<tr><th scope=row>6</th><td>Placebo</td><td>Worse</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A data.frame: 6 × 2\n",
       "\\begin{tabular}{r|ll}\n",
       "  & group & evolution\\\\\n",
       "  & <chr> & <chr>\\\\\n",
       "\\hline\n",
       "\t1 & Placebo & Worse\\\\\n",
       "\t2 & Placebo & Worse\\\\\n",
       "\t3 & Placebo & Worse\\\\\n",
       "\t4 & Placebo & Worse\\\\\n",
       "\t5 & Placebo & Worse\\\\\n",
       "\t6 & Placebo & Worse\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A data.frame: 6 × 2\n",
       "\n",
       "| <!--/--> | group &lt;chr&gt; | evolution &lt;chr&gt; |\n",
       "|---|---|---|\n",
       "| 1 | Placebo | Worse |\n",
       "| 2 | Placebo | Worse |\n",
       "| 3 | Placebo | Worse |\n",
       "| 4 | Placebo | Worse |\n",
       "| 5 | Placebo | Worse |\n",
       "| 6 | Placebo | Worse |\n",
       "\n"
      ],
      "text/plain": [
       "  group   evolution\n",
       "1 Placebo Worse    \n",
       "2 Placebo Worse    \n",
       "3 Placebo Worse    \n",
       "4 Placebo Worse    \n",
       "5 Placebo Worse    \n",
       "6 Placebo Worse    "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dat<-read.csv(\"https://raw.githubusercontent.com/jrasero/cm-85309-2023/main/datasets/tutorial8chisquare.csv\")\n",
    "head(dat)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3efd6251",
   "metadata": {},
   "source": [
    "# Bar plot\n",
    "\n",
    "A bar plot is the simplest plot to show the frequency or proportion of each category in **one** categorical variable. To create a bar plot in R, we can use `geom_bar`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "a17ba29b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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jjrJ92W3JwpyBY1addjl/327LB6hwQA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\nAEAQwg4AI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      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 420
      }
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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Tpt2mrZ1oIWhalynY03Xb8kpbTNnrs3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\nAAAASUVORK5CYII=",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 420
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ggplot(data = dat, mapping = aes(group)) + geom_bar()\n",
    "\n",
    "ggplot(data = dat, mapping = aes(evolution)) + geom_bar()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9d91a5dc",
   "metadata": {},
   "source": [
    "This is nice, but it only shows the information in one categorical variable. Some times, we will want to see the relationship between two categorical variables, and for that, we will need to incorporate both variables to the same plot. This can be achieved using either a **grouped bar plot**, or a **stacked bar plot**."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0426f9a6",
   "metadata": {},
   "source": [
    "# Grouped bar plot\n",
    "\n",
    "A grouped bar plot is a plot that shows the frequency or proportion of each category in a categorical variable, as well as the distribution of another categorical variable between categories. In this case, the proportions across the levels in one categorical variable are shown next to each other within each level of the other categorical variable. To create a grouped bar plot in R, we can use `geom_bar` function with the position = \"dodge\" argument to create the grouped bars."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "509a0886",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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fns27MKSupt1WWXlrVCCGHpq//e6x/P1zr1obemT3/v6f7tpl91dP+XVly2Nvma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\nF1aOVPzJAAAAAElFTkSuQmCC",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 420
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ggplot(data = dat, mapping = aes(x=group, fill=evolution)) + geom_bar(position = \"dodge\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1dd5e7d7",
   "metadata": {},
   "source": [
    "The *fill* argument in `aes` specifies the variable to fill the bars, and position = \"dodge\" creates the grouped bars."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8d6e4739",
   "metadata": {},
   "source": [
    "# Stacked bar plot\n",
    "\n",
    "A stacked bar plot is a plot that displays the frequency or proportion of each category in a categorical variable, as well as the distribution of another categorical variable within each category. It is similar to the grouped bar plot, but here, bars within the levels of one categorical variable are stacked on top of each other. This is useful to see if there are differences in the proportions across levels in the categorical variable. To create a stacked bar plot in R, we can use `geom_bar` function with the position = \"fill\" argument to create the stacked bars."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "ea79900c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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VgUwuofWjh37tyQkfPk4IFT3ivKaNi26xGnHL9vs9z17vv9998vXLhw5VFycnIy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\nRU5ErkJggg==",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 420
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ggplot(data = dat, mapping = aes(x=group, fill=evolution)) + \n",
    "geom_bar(position = \"fill\") +\n",
    "ylab(\"proportion\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8b2e65b7",
   "metadata": {},
   "source": [
    "The *fill* argument in `aes` specifies the variable to fill the bars, and position = \"fill\" creates the stacked bars."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0afe35aa",
   "metadata": {},
   "source": [
    "This plot has the limitation of not showing the number of observations contributed to each bar. One solution to this issue is to include the counts on the plot. To do this, you must specify the counts for each group. There are two ways to do this. The first method uses the `stat_*` functions provided by **ggplot**, which offer computed aesthetics such as bar height for a bar plot. To add labels to your plot using `stat_count`, you must specify how the data should be represented by setting the *geom* argument = \"text\". The second method is necessary when using a computed aesthetic as an argument to `aes`. To accomplish this, **ggplot** provides the `after_stat` function, which takes the name of the required statistic (in this case, count) as its argument. Additionally, you may need to adjust the height of labels by changing the argument *position*."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "06d94d97",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Mg6tfhdBFQSbsUCwN0rIvLRnEiLta7S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      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 420
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ggplot(data = dat, mapping = aes(x=group, fill=evolution)) + geom_bar(position = \"fill\") + \n",
    "ylab(\"proportion\") +\n",
    "    stat_count(geom = \"text\", \n",
    "               aes(label = after_stat(count)),\n",
    "               position=position_fill(vjust=0.5),\n",
    "               size=10,\n",
    "               colour=\"black\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a638eefb",
   "metadata": {},
   "source": [
    "This is great, but wouldn't it be even better if we could show the proportions instead of the actual observed occurrences that depend on the total number of observations in each categorical level? In the end, when analyzing categorical data to detect differences across populations, we should look at the proportions. Here below is an example of how to do this (found in stackoverflow: https://stackoverflow.com/questions/63653351/how-to-use-stat-count-to-label-a-bar-chart-with-counts-or-percentages-in-ggplo)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "d139a0c8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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KwxEPqcnIIkqE09ei3VxCNmpiYcKGir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HoujcO/L6LZc//vWQsZMDQsJcnJ0d3YI9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\nrqqsVCf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      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 420
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ggplot(data = dat, mapping = aes(x=group, fill=evolution)) + geom_bar(position = \"fill\") + \n",
    "ylab(\"proportion\") +\n",
    "    stat_count(geom = \"text\", \n",
    "               aes(label = after_stat(round(100*count / tapply(count, x, sum)[as.character(x)], 2))),\n",
    "               position=position_fill(vjust=0.5),\n",
    "               size=10,\n",
    "               colour=\"black\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1062c0f4",
   "metadata": {},
   "source": [
    "## Mosaic plot\n",
    "\n",
    "Another fancy way of showing the the relationship between two categorical variables in a way that reflects the proportions of each category is by means of a mosaic plot. In this case, to create a mosaic plot we can just use the `mosaicplot` R built-in function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "4d3c4374",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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/z55dOrhx/HTmh8wYsTFyz7acbnT565Y0ap2rV/mYTA1rJs0aJVFWo1qFE+5Cwe\n98DVD4wPq1evLt/t+N5z779y0BeL12TPGXnDxVe/s6haxczDB/Se8o+L/v3ZguzUiinPn3/A\nzscOnpIRQsh5/Z+3j1kRwpofh93yn59PPe2Q+r1P6f7NwPMHjVmUvXr+x3ddfM+MnicdWTPp\nJYWtQdjB+uY/d+6+J9w9btGanOWTn7tx8Jf7d9u/YtIzQQnS8JS//73du/2a1WvcrMMpL7Y5\n/diG3379dXaNXve+eGXVh3vtULNW27M/P/jRYRfuEEKtPoNf/VvtJ/rvXKdqwz2umHHkE89c\ntlMIIVTZt8O8y/bpsGv7bnfXuOnFW/evGOqf9PDLl2Y+3HfHmjV2OHZY42teffikBkkvKGwV\nGalUKukZoFhZ9sk9Z5/39xfGzc6utmOXM2/+11XdGvkDCIBtgrADAIiEPREAAJEQdgAAkRB2\nAACREHYAAJEQdgAAkRB2AACREHYAAJEQdgAAkRB2AACREHYAAJEQdgAAkRB2AACREHYAAJEQ\ndgAAkRB2AACRKJP0AKStWbPmvffeW7VqVdKDwNZSpUqVzp07b/o6CxYs+Oyzz36feYAodejQ\noWbNmklPkZwUxcPzzz+f9HMBtrqJEydu+hfh9NNPT3pGYNt2+umn/z4b7uLJHrviYtWqVVWr\nVn322WeTHgS2ikWLFvXq1etX90mvXr26W7dul1566e8zFRCZ2267bfXq1UlPkSTH2AEARELY\nAQBEQtgBAERC2AEARELYAQBEQtgBAERC2AEARELYAQBEQtgBAERC2AEARELYAQBEQtgBAERC\n2AEARELYAQBEQtgBAERC2AEARELYAQBEQtgBAERC2AEARELYAQBEQtgBAERC2AEARELYAQBE\nQtgBAERC2AEARELYAQBEQtgBAERC2AEARELYAQBEQtgBAERC2AEARELYAQBEQtgBAERC2AEA\nRELYAQBEQtgBAERC2AEARELYAQBEQtgBAERC2AEARELYAQBEQtgBAERC2AEARELYAQBEQtgB\nAERC2AEARELYAQBEQtgBAERC2AEARELYAQBEQtgBAERC2AEARELYAQBEQtgBAERC2AEARELY\nAQBEQtgBAERC2AEARELYAQBEQtgBAERC2AEARELYAQBEQtgBAERC2AEARELYAQBEQtgBAERC\n2AEARELYAQBEQtgBAERC2AEARELYAQBEQtgBAERC2AEARELYAQBEQtgBAERC2HzVC+QAABof\nSURBVAEARELYAQBEQtgBAERC2AEARELYAQBEQtgBAERC2AEARELYAQBEQtgBAERC2AEARELY\nAQBEQtgBAERC2AEARELYAQBEQtgBAERC2AEARELYAQBEQtgBAERC2AEARELYAQBEQtgBAERC\n2AEARELYAQBEQtgBAERC2AEARELYAQBEQtgBAERC2BXF7LcefmLcoqSnAAAIIQi7ohn7yLl3\nvbE06SkAAEIIwq5oOh3Vd+mwgU9/NmnustXZeXJSSY8FAJRMwq4o3h8+Yvond/Tu2LxuZrmy\nefo+lfRYAEDJVCbpAbZpXa5/95PLNrwws0ESowAA2GNXFJXrt2iaOfuNQddfds5Zj86u8s1T\nT06qsH39ykmPBQCUTMKuSGYO7df+6MEz65Wf+9F3C3PWTBzS59ArP16T9FQAQMkk7Ipi/D3X\njTv5hXcfva1/m9IhNDjygVeuyRzyyIdJjwUAlEzCriimT5/ZpuOu5ddd0LBJ1vKlzn8CACRC\n2BVFu067jhx019i8kls5cdDgke07tk10JgCgxPKu2KJocObAq5/uukfWvfVKL1s1pmPzKVOa\nXvPqGd4VCwAkQtgVSdldzn99QtdXR7z1xZTFFRq23v/wHu1q2QkKACRD2BXFpNce/rHtSft0\nPb5V19wLln425NYpe192dItk5wIASiRhVyjzJ7z/9dyc8PZNF47pu31Ye1Bd9oRBV16T+rew\nAwCSIOwKpdzkIaed8NT8VUsWrh5z1Mj0g5iRUaZ89dYX37dvsrMBACWVsCuUKt0GTZg3KLx9\n1ZFj+j53obfBAgDFgSP9i6LJfr13q5P/gqWfDbn1me+SGgcAKNnssSsUx9gBAMWPsCsUx9gB\nAMWPsCsUx9gBAMWPY+yK4oDrnzuvznuDLjm596EHXfve7BG33vzq9JykhwIASiphVyQzh/Zr\nf/TgmfXKz/3ou4U5ayYO6XPolR+vSXoqAKBkEnZFMf6e68ad/MK7j97Wv03pEBoc+cAr12QO\neeTDpMcCAEomYVcU06fPbNNx1/LrLmjYJGv50qXJDQQAlGTCrijaddp15KC7xuaV3MqJgwaP\nbN/ReykAgER4V2xRNDhz4NVPd90j6956pZetGtOx+ZQpTa959YwGSY8FAJRMwq5Iyu5y/usT\nur464q0vpiyu0LD1/of3aFfLTlAAIBnCrqgyqrXqenyrg5cvWLSmSq0qqg4ASIwQKazFnz90\n+dkX3PnWnJUTHzq5ba3MWrWr1di+25Wvz0l6MACgpLLHrnCWPffHg88dvevB1U85/Oma42bv\nfPuLg9qmvnzsij+fcNmekx7qUSnp+QCAEkjYFc7HL71U7vRnXvm/Fv/au+HV/V/68JxuZUPY\na58aE1/s8fyHoUeXpOcDAEogL8UWzoIFC+o3alQqNGjatFyDJk3K5l6a0bhxw4ULFyY7GgBQ\nUgm7wsnJSZUqVSqEkJGRkftFnlSOT4sFABLhpdhCWzZ1zOjRsybMS62YPnb06EUhhBDmTvGx\nE1BE06ZNGzFiRNJTANukadOm1atXL+kpkiTsCu2bu3vteXful/32HLTu8t7JjANR6Nix4zvv\nvPPss88mPQiwrerYsWPSIyRJ2BVOr6ErVqwp+Fuly/2+o0BMzjvvvPPOOy/pKQC2VcKucEqV\nrVChbNJDAADk580TAACREHYAAJEQdgAAkRB2AACREHYAAJEQdgAAkRB2AACREHYAAJFwgmKg\nGHniiSeeeuqppKcAtmG9e/c+9thjk54iMcIOKEZeeumlTz/9tHPnzkkPAmyTPvzww0qVKgk7\ngOKiZcuWZ5xxRtJTANukhQsXJj1CwhxjBwAQCWEHABAJYQcAEAlhBwAQCWEHABAJYQcAEAlh\nBwAQCWEHABAJYQcAEAlhBwAQCWEHABAJYQcAEAlhBwAQCWEHABAJYQcAEAlhBwAQCWEHABAJ\nYQcAEAlhBwAQCWEHABAJYQcAEAlhBwAQCWEHABAJYQcAEAlhBwAQCWEHABAJYQcAEAlhBwAQ\nCWEHABAJYQcAEAlhBwAQCWEHABAJYQcAEAlhBwAQCWEHABAJYQcAEAlhBwAQCWEHABAJYQcA\nEAlhBwAQCWEHABAJYQcAEAlhBwAQCWEHABAJYQcAEAlhBwAQCWEHABAJYQcAEAlhBwAQCWEH\nABAJYQcAEAlhBwAQCWEHABAJYQcAEAlhBwAQCWEHABAJYQcAEAlhBwAQCWEHABAJYQcAEAlh\nBwAQCWEHABAJYQcAEAlhBwAQCWEHABAJYQcAEAlhBwAQCWEHABAJYQcAEAlhBwAQCWEHABAJ\nYQcAEAlhBwAQCWEHABAJYQcAEAlhBwAQCWEHABAJYQcAEAlhBwAQCWEHABAJYQcAEAlhBwAQ\nCWEHABAJYQcAEAlhBwAQCWEHABAJYQcAEAlhBwAQCWEHABAJYQcAEAlhBwAQCWEHABCJMkkP\nQFpGRsby5csvueSSpAeBrWL16tUhhIyMjKQHAYiZsCsu9ttvvxtvvDGVSiU9CGwtAwYMaNGi\nRdJTAMRM2BUXtWvXvvTSS5OeAgDYhjnGDgAgEsIOACASwg4AIBLCDgAgEsIOACASwg4AIBLC\nDgAgEsIOACASTlBcjCxdujT3Y5cgSuXLl69UqVLSUwDETNgVF2+//XaXLl2SngK2orJly37/\n/fdZWVlJDwIQLWFXXMyfP79y5cp33HFH0oPAVrFkyZJLLrlk8eLFSQ8CEDNhV4yULl26ZcuW\nSU8BW8WiRYuSHgEgft48AQAQCWEHABAJYQcAEAlhBwAQCWEHABAJYQcAEAlhBwAQCWEHABAJ\nYQcAEAlhBwAQCWEHABAJYQcAEAlhBwAQCWEHABAJYQcAEAlhBwAQCWEHABAJYQcAEAlhBwAQ\nCWEHABAJYQcAEAlhBwAQCWEHABAJYQcAEAlhBwAQCWEHABAJYQcAEAlhBwAQCWEHABAJYQcA\nEAlhBwAQCWEHABAJYQcAEAlhBwAQCWEHABAJYQcAEAlhBwAQCWEHABAJYQcAEAlhBwAQCWEH\nABAJYQcAEAlhBwAQCWEHABAJYQcAEAlhBwAQCWEHABAJYQcAEAlhBwAQCWEHABAJYQcAEAlh\nBwAQCWEHABAJYQcAEAlhBwAQCWEHABAJYQcAEAlhBwAQCWEHABAJYQcAEAlhBwAQCWEHABAJ\nYQcAEAlhBwAQCWEHABAJYQcAEAlhBwAQCWEHABAJYQcAEAlhBwAQCWEHABAJYQcAEAlhBwAQ\nCWEHABAJYQcAEAlhBwAQCWEHABAJYQcAEAlhBwAQCWEHABAJYQcAEAlhBwAQCWEHABAJYQcA\nEAlhBwAQCWEHABAJYQcAEAlhBwAQCWEHABAJYQcAEAlhBwAQCWEHABAJYQcAEAlhBwAQCWEH\nABAJYVdE2bPeG3TJyb0PPeja92aPuPXmV6fnJD0RAFBSCbsimTm0X/ujB8+sV37uR98tzFkz\ncUifQ6/8eE3SUwEAJZOwK4rx91w37uQX3n30tv5tSofQ4MgHXrkmc8gjHyY9FgBQMgm7opg+\nfWabjruWX3dBwyZZy5cuTW4gAKAkE3ZF0a7TriMH3TU2r+RWThw0eGT7jm0TnQkAKLHKJD3A\nNq3BmQOvfrrrHln31iu9bNWYjs2nTGl6zatnNEh6LACgZBJ2RVJ2l/Nfn9D11RFvfTFlcYWG\nrfc/vEe7WnaCAgDJEHZFlSpXp/WehzTvHEIIYeGk7xZmNmhRv3LCQwEAJZGwK4rsL+4+vPuf\nX/5xdf4Lez+ZGtYnqYkAgBLM64ZF8e49N847ddTsn9ek8lF1AEAy7LErilSqVueue9Qt/+vX\nBADY6uyxK4p9zz5+wg2XP/3Jt9NmrbNoZdJjAQAlkz12RZGTXfrnj27pvdst+S90jB0AkAxh\nVxSj7r1z5VmvTbigQ93y63Z9lvOWWAAgEcKuKDIyau95xMEt6yU9BwBAcIxd0ex7Zu9xV/71\n2fEzFy37ea3VOUmPBQCUTMKuKF69795P37n56J0b1sisuNZxTyc9FgBQMnkptigOvOmjsVds\neGHlukmMAgAg7IqiUp2sKtNHPP/hjOXZOakQUjmrV8z5umy3+y7cJ+nJAIASSNgVxbIXz2h7\nxHOZHWov+HJ5o52rzf1y/Pzt+z54QtJjAQAlk2PsimL0M8/Uue6DCR89fEqDDhe/9eWU8YMO\nXpIq2yjpsQCAkknYFcXq1aF1m5YZoVWr7T///KtQttnp5+z3ydtjkh4LACiZhF1R7Ljj9h+/\nMWpxqLbTTtnvvjMlhCULFvy0YkXSYwEAJZNj7Iqi2UmXdNr9kAObjP/ohJMXH7T3rkPLfffd\nAfc9kvRYAEDJJOyKpFG/od/s9s1PWaUa3/D2azsNHbVo+yNOPaJm0lMBACWTsCuijCrNd6oS\nQkhV36nnGbvVqFw66YEAgBLLMXaFlfPjazcNOPSq10IIqalPnNiqVvWa1Rrue/mb85MeDAAo\nqYRd4aS+uvWInv/4oXW7rDVh6VOXnvVe5/u+mDjqzxUHnXDF66uTHg4AKJmEXeGMe/Sh7469\n78WBx7QqvfqNp4a3OOe6/m132OuSy479+fnnP056OACgZBJ2hfPDDz/ssMsulUIIYcx779Xt\n0qVpCCFkZGU1njdvXpKDAQAll7ArnCpVqixYsCCEEH4YNeqn/fZrH0IIIefbbyfVrVs30ckA\ngBJL2BXObgcdNHfIDf8a+cHjfxs04YheB5UOIayYcPf/PVOxa9cOSQ8HAJRMTndSOJl9bn/w\n9aPOOmjv7J1OHfzSkZXCsmF9Gx77XO3jhj7UvVzSwwEAJZOwK6RSTfrc91nvf69JlS5dKoQQ\nKnf603/eub37Xk0qJD0ZAFBSCbuiyChdOiPv66b7HN00wVEAABxjBwAQCWEHABAJL8UCxcuK\nFStmzpyZ9BTANmnFihVJj5AwYQcUI5mZmSNHjhw5cmTSgwDbqp133jnpEZIk7IBi5M4777zo\noouSngLYhjVu3DjpEZIk7IBipFy5cttvv33SUwBsq7x5AgAgEsIOACASwg4AIBLCDgAgEsIO\nACASwg4AIBLCDgAgEsIOACASTlAMFCPjxo374IMPkp4C2Ibtueeebdu2TXqKxAg7oBi58847\nn3766fr16yc9CLBNmjVrVq9evR566KGkB0mMsAOKkVQqtddee1166aVJDwJsk2677bZUKpX0\nFElyjB0AQCSEHQBAJIQdAEAkhB0AQCSEHQBAJIQdAEAkhB0AQCSEHQBAJIQdAEAkhB0AQCSE\nHQBAJIQdAEAkhB0AQCSEHQBAJIQdAEAkhB0AQCSEHQBAJIQdAEAkhB0AQCSEHQBAJIQdAEAk\nhB0AQCSEHQBAJIQdAEAkhB0AQCSEHQBAJIQdAEAkhB0AQCSEHQBAJIQdAEAkhB0AQCSEHQBA\nJIQdAEAkhB0AQCSEHQBAJIQdAEAkhB0AQCSEHQBAJIQdAEAkhB0AQCSEHQBAJIQdAEAkhB0A\nQCSEHQBAJIQdAEAkhB0AQCSEHQBAJIQdAEAkhB0AQCSEHQBAJIQdAEAkhB0AQCSEHQBAJIQd\nAEAkhB0AQCSEHQBAJIQdAEAkhB0AQCSEHQBAJIQdAEAkhB0AQCSEHQBAJIQdAEAkhB0AQCSE\nHQBAJIQdAEAkhB0AQCSEHQBAJIQdAEAkhB0AQCSEHQBAJIQdAEAkhB0AQCSEHQBAJIQdAEAk\nhB0AQCSEHQBAJIQdAEAkyiQ9AGmlSpVaunTp8ccfn/QgsFXk5OSEEEqV8sckwFYk7IqLQw45\n5P7778/Ozk56ENhaKlWq1LJly6SnAIiZsCsuMjMzTz311KSnAAC2YV4WAQCIhLADAIiEsAMA\niISwAwCIhLADAIiEsAMAiISwAwCIhLADAIiEExQXF6lUasqUKbkfuwRRKl++fKNGjZKeAiBm\nwq64eOWVV7p37570FLAVZWRkfP/9982aNUt6EIBoCbviYtmyZZmZmYMGDUp6ENgqFi9efPbZ\nZy9fvjzpQQBiJuyKkVKlSjVo0CDpKWCrqFixYtIjAMTPmycAACIh7AAAIiHsAAAiIewAACIh\n7AAAIiHsAAAiIewAACIh7AAAIiHsAAAiIewAACIh7AAAIiHsAAAiIewAACIh7AAAIiHsAAAi\nIewAACIh7AAAIiHsAAAiIewAACIh7AAAIiHsAAAiIewAACIh7AAAIiHsAAAiIeyKKHvWe4Mu\nObn3oQdd+97sEbfe/Or0nKQnAgBKKmFXJDOH9mt/9OCZ9crP/ei7hTlrJg7pc+iVHyc9FABQ\nQgm7ohh/z3XjTn7h3Udv69+mdAgNjnzglWsyhzyS9FQAQAkl7Ipi+vSZbTruWn7dBQ2bZC1f\nmtw8AECJJuyKol2nXUcOumtsXsqtnDho8Mj2HRMdCQAoucokPcA2rcGZA69+uuseWffWK71s\n1ZiOzadMaXrNq2ckPRUAUEIJuyIpu8v5r0/o+uqIt76YsrhCw9b7H96jXS07QQGAZAi7osqo\n1qrr8a26hrBm+eKfy6k6ACAxQqRIVk0Y+qe+t4wOYeaTJzarXq1a/T2veWdx0kMBACWUsCuK\nuQ+de84HtXeqn/PpnZc91erWTz+5Leu+C+9JeioAoIQSdkXxxSefH/yXfxzZdOJLL8045JQz\nOrQ/pf/+33yZ9FQAQAkl7IqiXLnSixYtDZOHD/96z0MOygypyRO/rVYr6akAgBLKmyeKovOx\nx//Qe/ddq8ycccyDfeuO/dfBR1z38ylvJj0VAFBCCbuiWLim7YUDd61Vsdme3feuG6Y073PT\nyGP6O0ExAJAMYVcUYx/508Ptvhp9QeMQQgjbdfvDdgkPBACUZI6xK4pOR/VdOmzg059Nmrts\ndXaenKSnAgBKKHvsiuL94SOmfzK7d8c78l/YO5UaltRAAEBJJuyKosv1735y2YYXZiYxCQCA\nsCuSyvVbNJzw5C3XP/jml9MWl6/f5sABf7nihBZJTwUAlFCOsSuSHwYd1fmPr5Tf49jzLj6v\n/17lX7tgvyPvmZT0UABACWWPXVF8cd8dP/zhxS9v6VQuhBDCGX/oWbdt//vCObckPBcAUCLZ\nY1cUP/44q23H9uXW/rt8h047z52V4EAAQEkm7IqizS5t33n4P5NXp/+56vsHhoxq2z7RkQCA\nkstLsUWR9Ye/n/WfQ9rsMLjLbk0rLZv80cgJdS9/9Q9JTwUAlFDCrpC+Gz+vSZvaFfe6/sOv\nD/nfk29+NSenxr59ru3de/f6HlIAIBkqpJD+2rbeS006Hdz9sMMOO6z7H645qXJG0hMBACWd\nY+wK6dF5X714x4D2pcY/elmPFrXrtzv05L8MfHzUxIXZSQ8GAJRYwq6QytXccd/eZ1/zr/+N\n/Gr2wsmv3XlKh9Ljn7j4wCa1W1yc9GgAQAnlpdiiWj1/wvtvvP7a62+8OWr0hOyme+zVKumJ\nAIASStgV0vyv3nhxxIgRw4e//OGsau0P7H7Y4VcNvfeg3bIcawcAJEXYFdJZbbq+0/nE88/5\n15VPHdC2bvmkxwEAEHaFdchx+457+Ynbb5w0bszYHj16dNuvTe1yv/5TAABbjzdPFNJZ/31r\nwtyZH93/x87lvvzP+Qdk1W66Z69zbnjghTEzVyQ9GgBQQgm7IihdrcV+fS+49eHXx8+eM3bo\nxfuVGXnDMR0anZT0WABACeWl2CJZs/iHz94bNWrkqJGjRr33xYK6nY44/6wBSQ8FAJRQwq6Q\nhl544pBRo94bO6P0drsf2LVrz8se+edBuzXJtAcUAEiMsCukb74t12nA7Vd0O3jPHWp4EAGA\n4kCTFNLfRjyQ9AgAAOvx0iEAQCSEHQBAJIQdAEAkhB0AQCSEHQBAJIQdAEAkhB0AQCSEHQBA\nJIQdAEAkhB0AQCSEHQBAJIQdAEAkhB0AQCSEHQBAJIQdAEAkhB0AQCSEHQBAJIQdAEAkhB0A\nQCSEHQBAJIQdAEAkhB0AQCSEHQBAJIQdAEAkyiQ9AOusXLny/vvvT3oK2Cp+/vnnzbzmxIkT\n/SIAhTNx4sR69eolPUWShF1x0aZNm/3333/u3LlJDwJbS8+ePRs1arTp63Tp0mXGjBl+EYDC\n2X777bt06ZL0FEnKSKVSSc8AAMBvwDF2AACREHYAAJEQdgAAkRB2AACREHYAAJEQdgAAkRB2\nAACREHYAAJEQdgAAkRB2AACREHYAAJEQdgAAkRB2AACREHYAAJEQdgAAkRB2AACREHYAAJEQ\ndgAAkRB2AACREHYAAJEQdgAAkRB2AACREHYAAJEQdgAAkRB2AACREHYAAJEQdgAAkRB2AACR\nEHYAAJEQdgAAkRB2AACREHYAAJEQdgAAkRB2AACREHYAAJEQdgAAkRB2AACREHYAAJEQdgAA\nkRB2AACREHYAAJEQdgAAkRB2AACREHYAAJEQdgAAkRB2AACREHYAAJEQdgAAkRB2AACREHYA\nAJEQdgAAkRB2AACREHYAAJEQdgAAkRB2AACREHYAAJEQdgAAkRB2AACREHYAAJEQdgAAkRB2\nAACREHYAAJEQdgAAkRB2AACREHYAAJEQdgAAkRB2AACREHYAAJEQdgAAkRB2AACREHYAAJEQ\ndgAAkRB2AACREHYAAJEQdgAAkRB2AACREHYAAJEQdgAAkRB2AACREHYAAJEQdgAAkRB2AACR\nEHYAAJEQdgAAkRB2AACREHYAAJEQdgAAkRB2AACREHYAAJEQdgAAkRB2AACREHYAAJEQdgAA\nkRB2AACREHYAAJEQdgAAkRB2AACREHYAAJEQdgAAkRB2AACREHYAAJEQdgAAkRB2AACREHYA\nAJEQdgAAkRB2AACREHYAAJEQdgAAkRB2AACREHYAAJEQdgAAkRB2AACREHYAAJEQdgAAkRB2\nAACREHYAAJEQdgAAkRB2AACREHYAAJEQdgAAkRB2AACREHYAAJEQdgAAkfh/f9eF3rp1ajEA\nAAAASUVORK5CYII=",
      "text/plain": [
       "Plot with title “table(dat$group, dat$evolution)”"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 420
      }
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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G8mTZw8a+GqjOwatXJ3btc6t2JG0psAABKUkmG3dubQK0+76P63vl3787Hy9fc9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\nABAJYQcAEIn/Byxf4oVIZx1FAAAAAElFTkSuQmCC",
      "text/plain": [
       "Plot with title “dat”"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 420
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "mosaicplot(table(dat$group, dat$evolution))\n",
    "mosaicplot(group~evolution, data = dat)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e899f408",
   "metadata": {},
   "source": [
    "More code recipes on how to create all of these kinds of plots:\n",
    "\n",
    "https://r-graph-gallery.com/barplot.html"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "R",
   "language": "R",
   "name": "ir"
  },
  "language_info": {
   "codemirror_mode": "r",
   "file_extension": ".r",
   "mimetype": "text/x-r-source",
   "name": "R",
   "pygments_lexer": "r",
   "version": "4.2.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}