{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "558d28c0",
   "metadata": {},
   "source": [
    "# Handling an overplotting on a scatter plot: `geom_count()`/`stat_sum()`\n",
    "\n",
    "The `geom_count()` counts the number of observations at each location. \n",
    "\n",
    "Computed variables:\n",
    "\n",
    "- `..n..` - number of observations at location\n",
    "- `..prop..` - value in range 0..1 : share of observations at location\n",
    "- `..proppct..` - value in range 0..100 : % of observations at location"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "da3d73f3",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-23T10:36:35.796089Z",
     "iopub.status.busy": "2024-08-23T10:36:35.795993Z",
     "iopub.status.idle": "2024-08-23T10:36:36.124869Z",
     "shell.execute_reply": "2024-08-23T10:36:36.124552Z"
    }
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "\n",
    "from lets_plot import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "08103843",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-23T10:36:36.126468Z",
     "iopub.status.busy": "2024-08-23T10:36:36.126327Z",
     "iopub.status.idle": "2024-08-23T10:36:36.128405Z",
     "shell.execute_reply": "2024-08-23T10:36:36.128236Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "            <div id=\"mTIMNM\"></div>\n",
       "            <script type=\"text/javascript\" data-lets-plot-script=\"library\">\n",
       "                if(!window.letsPlotCallQueue) {\n",
       "                    window.letsPlotCallQueue = [];\n",
       "                }; \n",
       "                window.letsPlotCall = function(f) {\n",
       "                    window.letsPlotCallQueue.push(f);\n",
       "                };\n",
       "                (function() {\n",
       "                    var script = document.createElement(\"script\");\n",
       "                    script.type = \"text/javascript\";\n",
       "                    script.src = \"https://cdn.jsdelivr.net/gh/JetBrains/lets-plot@v4.4.1/js-package/distr/lets-plot.min.js\";\n",
       "                    script.onload = function() {\n",
       "                        window.letsPlotCall = function(f) {f();};\n",
       "                        window.letsPlotCallQueue.forEach(function(f) {f();});\n",
       "                        window.letsPlotCallQueue = [];\n",
       "                        \n",
       "                    };\n",
       "                    script.onerror = function(event) {\n",
       "                        window.letsPlotCall = function(f) {};    // noop\n",
       "                        window.letsPlotCallQueue = [];\n",
       "                        var div = document.createElement(\"div\");\n",
       "                        div.style.color = 'darkred';\n",
       "                        div.textContent = 'Error loading Lets-Plot JS';\n",
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       "                    };\n",
       "                    var e = document.getElementById(\"mTIMNM\");\n",
       "                    e.appendChild(script);\n",
       "                })()\n",
       "            </script>\n",
       "            "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "LetsPlot.setup_html() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "99409355",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-23T10:36:36.129439Z",
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     "shell.execute_reply": "2024-08-23T10:36:36.276225Z"
    }
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    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>manufacturer</th>\n",
       "      <th>model</th>\n",
       "      <th>displ</th>\n",
       "      <th>year</th>\n",
       "      <th>cyl</th>\n",
       "      <th>trans</th>\n",
       "      <th>drv</th>\n",
       "      <th>cty</th>\n",
       "      <th>hwy</th>\n",
       "      <th>fl</th>\n",
       "      <th>class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>audi</td>\n",
       "      <td>a4</td>\n",
       "      <td>1.8</td>\n",
       "      <td>1999</td>\n",
       "      <td>4</td>\n",
       "      <td>auto(l5)</td>\n",
       "      <td>f</td>\n",
       "      <td>18</td>\n",
       "      <td>29</td>\n",
       "      <td>p</td>\n",
       "      <td>compact</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>audi</td>\n",
       "      <td>a4</td>\n",
       "      <td>1.8</td>\n",
       "      <td>1999</td>\n",
       "      <td>4</td>\n",
       "      <td>manual(m5)</td>\n",
       "      <td>f</td>\n",
       "      <td>21</td>\n",
       "      <td>29</td>\n",
       "      <td>p</td>\n",
       "      <td>compact</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>audi</td>\n",
       "      <td>a4</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2008</td>\n",
       "      <td>4</td>\n",
       "      <td>manual(m6)</td>\n",
       "      <td>f</td>\n",
       "      <td>20</td>\n",
       "      <td>31</td>\n",
       "      <td>p</td>\n",
       "      <td>compact</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>audi</td>\n",
       "      <td>a4</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2008</td>\n",
       "      <td>4</td>\n",
       "      <td>auto(av)</td>\n",
       "      <td>f</td>\n",
       "      <td>21</td>\n",
       "      <td>30</td>\n",
       "      <td>p</td>\n",
       "      <td>compact</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>audi</td>\n",
       "      <td>a4</td>\n",
       "      <td>2.8</td>\n",
       "      <td>1999</td>\n",
       "      <td>6</td>\n",
       "      <td>auto(l5)</td>\n",
       "      <td>f</td>\n",
       "      <td>16</td>\n",
       "      <td>26</td>\n",
       "      <td>p</td>\n",
       "      <td>compact</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0 manufacturer model  displ  year  cyl       trans drv  cty  hwy  \\\n",
       "0           1         audi    a4    1.8  1999    4    auto(l5)   f   18   29   \n",
       "1           2         audi    a4    1.8  1999    4  manual(m5)   f   21   29   \n",
       "2           3         audi    a4    2.0  2008    4  manual(m6)   f   20   31   \n",
       "3           4         audi    a4    2.0  2008    4    auto(av)   f   21   30   \n",
       "4           5         audi    a4    2.8  1999    6    auto(l5)   f   16   26   \n",
       "\n",
       "  fl    class  \n",
       "0  p  compact  \n",
       "1  p  compact  \n",
       "2  p  compact  \n",
       "3  p  compact  \n",
       "4  p  compact  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mpg_df = pd.read_csv (\"https://raw.githubusercontent.com/JetBrains/lets-plot-docs/master/data/mpg.csv\")\n",
    "mpg_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "78db0a6b",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-23T10:36:36.277957Z",
     "iopub.status.busy": "2024-08-23T10:36:36.277876Z",
     "iopub.status.idle": "2024-08-23T10:36:36.279661Z",
     "shell.execute_reply": "2024-08-23T10:36:36.279484Z"
    }
   },
   "outputs": [],
   "source": [
    "p = ggplot(mpg_df, aes(x=as_discrete('class', order=1), y=as_discrete('drv', order=1)))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fafaa792",
   "metadata": {},
   "source": [
    "#### 1. Plot an Observation Count by Location"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "9bf746ef",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-23T10:36:36.280805Z",
     "iopub.status.busy": "2024-08-23T10:36:36.280734Z",
     "iopub.status.idle": "2024-08-23T10:36:36.312684Z",
     "shell.execute_reply": "2024-08-23T10:36:36.312489Z"
    }
   },
   "outputs": [
    {
     "data": {
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       "   <div id=\"sBNh2A\"></div>\n",
       "   <script type=\"text/javascript\" data-lets-plot-script=\"plot\">\n",
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     "execution_count": 5,
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    }
   ],
   "source": [
    "p + geom_count()"
   ]
  },
  {
   "cell_type": "code",
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   "outputs": [
    {
     "data": {
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       "<lets_plot.plot.core.PlotSpec at 0x7f113c01ae60>"
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     "execution_count": 6,
     "metadata": {},
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   ],
   "source": [
    "p + stat_sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "68d94a2c",
   "metadata": {},
   "source": [
    "#### 2. Plot an Observations Share by Location "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "feb247d0",
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    "execution": {
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   "outputs": [
    {
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       "},\n",
       "\"data\":{\n",
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       "};\n",
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    }
   ],
   "source": [
    "p + geom_count(aes(size='..prop..'))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "caf89a4b",
   "metadata": {},
   "source": [
    "#### 3. Plot an Observations Share by Drivetrain Type within each Vehicle \"class\"\n",
    "\n",
    "Note: group by \"class\"."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "e513cb6c",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-23T10:36:36.324521Z",
     "iopub.status.busy": "2024-08-23T10:36:36.324445Z",
     "iopub.status.idle": "2024-08-23T10:36:36.329123Z",
     "shell.execute_reply": "2024-08-23T10:36:36.328948Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "   <div id=\"rId3Kc\"></div>\n",
       "   <script type=\"text/javascript\" data-lets-plot-script=\"plot\">\n",
       "       (function() {\n",
       "           var plotSpec={\n",
       "\"data\":{\n",
       "},\n",
       "\"mapping\":{\n",
       "\"x\":\"class\",\n",
       "\"y\":\"drv\"\n",
       "},\n",
       "\"data_meta\":{\n",
       "\"series_annotations\":[{\n",
       "\"type\":\"str\",\n",
       "\"column\":\"class\"\n",
       "},{\n",
       "\"type\":\"str\",\n",
       "\"column\":\"drv\"\n",
       "},{\n",
       "\"type\":\"int\",\n",
       "\"column\":\"Unnamed: 0\"\n",
       "},{\n",
       "\"type\":\"str\",\n",
       "\"column\":\"manufacturer\"\n",
       "},{\n",
       "\"type\":\"str\",\n",
       "\"column\":\"model\"\n",
       "},{\n",
       "\"type\":\"float\",\n",
       "\"column\":\"displ\"\n",
       "},{\n",
       "\"type\":\"int\",\n",
       "\"column\":\"year\"\n",
       "},{\n",
       "\"type\":\"int\",\n",
       "\"column\":\"cyl\"\n",
       "},{\n",
       "\"type\":\"str\",\n",
       "\"column\":\"trans\"\n",
       "},{\n",
       "\"type\":\"int\",\n",
       "\"column\":\"cty\"\n",
       "},{\n",
       "\"type\":\"int\",\n",
       "\"column\":\"hwy\"\n",
       "},{\n",
       "\"type\":\"str\",\n",
       "\"column\":\"fl\"\n",
       "}],\n",
       "\"mapping_annotations\":[{\n",
       "\"parameters\":{\n",
       "\"label\":\"class\",\n",
       "\"order\":1.0\n",
       "},\n",
       "\"aes\":\"x\",\n",
       "\"annotation\":\"as_discrete\"\n",
       "},{\n",
       "\"parameters\":{\n",
       "\"label\":\"drv\",\n",
       "\"order\":1.0\n",
       "},\n",
       "\"aes\":\"y\",\n",
       "\"annotation\":\"as_discrete\"\n",
       "}]\n",
       "},\n",
       "\"kind\":\"plot\",\n",
       "\"scales\":[],\n",
       "\"layers\":[{\n",
       "\"geom\":\"point\",\n",
       "\"stat\":\"sum\",\n",
       "\"mapping\":{\n",
       "\"size\":\"..prop..\",\n",
       "\"group\":\"class\"\n",
       "},\n",
       "\"data_meta\":{\n",
       "},\n",
       "\"data\":{\n",
       "\"..prop..\":[1.0,0.07317073170731707,0.926829268292683,1.0,0.7446808510638298,0.2553191489361702,0.2571428571428571,0.6285714285714286,0.11428571428571428,1.0,0.1774193548387097,0.8225806451612904],\n",
       "\"class\":[\"pickup\",\"midsize\",\"midsize\",\"minivan\",\"compact\",\"compact\",\"subcompact\",\"subcompact\",\"subcompact\",\"2seater\",\"suv\",\"suv\"],\n",
       "\"drv\":[\"4\",\"4\",\"f\",\"f\",\"f\",\"4\",\"r\",\"f\",\"4\",\"r\",\"r\",\"4\"]\n",
       "}\n",
       "}],\n",
       "\"metainfo_list\":[]\n",
       "};\n",
       "           var plotContainer = document.getElementById(\"rId3Kc\");\n",
       "           window.letsPlotCall(function() {{\n",
       "               LetsPlot.buildPlotFromProcessedSpecs(plotSpec, -1, -1, plotContainer);\n",
       "           }});\n",
       "       })();\n",
       "   </script>"
      ],
      "text/plain": [
       "<lets_plot.plot.core.PlotSpec at 0x7f126d97bc40>"
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     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p + geom_count(aes(size='..prop..', group='class'))"
   ]
  }
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