{
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   "cell_type": "markdown",
   "id": "f53ef1d6-ec34-4b1e-9b34-c625edc86331",
   "metadata": {},
   "source": [
    "# MPG Correlogram\n",
    "\n",
    "A correlogram provides a quick overview of the entire dataset and allows analysing the relationship between each pair of numerical variables."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "d48d91c8-4a42-434c-85c6-f84a96e10d5b",
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     "shell.execute_reply": "2024-08-23T10:43:22.994718Z"
    }
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "from lets_plot import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "184c6176-bd7a-4881-bb9d-1642ffd3bc40",
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     "iopub.status.idle": "2024-08-23T10:43:22.999857Z",
     "shell.execute_reply": "2024-08-23T10:43:22.999660Z"
    }
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    {
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       "                    e.appendChild(script);\n",
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       "            </script>\n",
       "            "
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   "source": [
    "LetsPlot.setup_html()"
   ]
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   "cell_type": "code",
   "execution_count": 3,
   "id": "191b90ef-a38e-4988-b98c-774e0a2aa8c0",
   "metadata": {
    "execution": {
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    {
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     "output_type": "stream",
     "text": [
      "(234, 11)\n"
     ]
    },
    {
     "data": {
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       "<style scoped>\n",
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       "\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>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>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>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>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>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>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": [
       "  manufacturer model  displ  year  cyl       trans drv  cty  hwy fl    class\n",
       "0         audi    a4    1.8  1999    4    auto(l5)   f   18   29  p  compact\n",
       "1         audi    a4    1.8  1999    4  manual(m5)   f   21   29  p  compact\n",
       "2         audi    a4    2.0  2008    4  manual(m6)   f   20   31  p  compact\n",
       "3         audi    a4    2.0  2008    4    auto(av)   f   21   30  p  compact\n",
       "4         audi    a4    2.8  1999    6    auto(l5)   f   16   26  p  compact"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(\"https://raw.githubusercontent.com/JetBrains/lets-plot-docs/master/data/mpg.csv\")\n",
    "df.drop(columns=[\"Unnamed: 0\"], inplace=True)\n",
    "print(df.shape)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "52e52e19-90fa-4edf-b626-bad6ffb2994d",
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     "shell.execute_reply": "2024-08-23T10:43:23.376654Z"
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   "outputs": [
    {
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      "text/html": [
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       "\n",
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       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>x</th>\n",
       "      <th>y</th>\n",
       "      <th>corr</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>displ</td>\n",
       "      <td>displ</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>displ</td>\n",
       "      <td>year</td>\n",
       "      <td>0.147843</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>displ</td>\n",
       "      <td>cyl</td>\n",
       "      <td>0.930227</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>displ</td>\n",
       "      <td>cty</td>\n",
       "      <td>-0.798524</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>displ</td>\n",
       "      <td>hwy</td>\n",
       "      <td>-0.766020</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       x      y      corr\n",
       "0  displ  displ  1.000000\n",
       "1  displ   year  0.147843\n",
       "2  displ    cyl  0.930227\n",
       "3  displ    cty -0.798524\n",
       "4  displ    hwy -0.766020"
      ]
     },
     "execution_count": 4,
     "metadata": {},
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    }
   ],
   "source": [
    "corr_df = df.corr(numeric_only=True).stack().to_frame().reset_index()\n",
    "corr_df.columns = [\"x\", \"y\", \"corr\"]\n",
    "corr_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "98012f57-2c7d-4099-a7d6-cc6b23224256",
   "metadata": {
    "execution": {
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     "shell.execute_reply": "2024-08-23T10:43:23.381333Z"
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   "outputs": [],
   "source": [
    "corr_df0 = corr_df[corr_df[\"x\"] == corr_df[\"y\"]]\n",
    "\n",
    "corr_df1 = corr_df[corr_df[\"x\"] < corr_df[\"y\"]]\n",
    "corr_df1 = pd.concat([\n",
    "    corr_df1.assign(half=\"corr\"),\n",
    "    corr_df1.assign(corr=np.where(corr_df1[\"corr\"] > 0, 1 - corr_df1[\"corr\"], -1 - corr_df1[\"corr\"]), half=\"remainder\")\n",
    "]).reset_index(drop=True)\n",
    "\n",
    "corr_df2 = corr_df[corr_df[\"x\"] > corr_df[\"y\"]]\n",
    "corr_df2 = corr_df2.assign(angle=np.where(corr_df2[\"corr\"] > 0, np.pi / 4, 3 * np.pi / 4))\n",
    "\n",
    "vars = sorted(corr_df[\"x\"].unique())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3284b8f2-2203-4e50-b7e6-5cf05938071e",
   "metadata": {},
   "source": [
    "### The Correlogram\n",
    "\n",
    "Let's plot a correlogram of the mpg dataset variables. Here's how it should be interpreted:\n",
    "\n",
    "- The filled portion of the pie shows the correlation magnitude.\n",
    "- The diagonal of the squares shows the sign of the correlation.\n",
    "- The depth of the figures shading shows the correlation magnitude.\n",
    "- The names of the variables are on the diagonal."
   ]
  },
  {
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       "\"geom\":\"spoke\",\n",
       "\"data\":{\n",
       "\"x\":[\"displ\",\"displ\",\"year\",\"year\",\"year\",\"year\",\"cyl\",\"hwy\",\"hwy\",\"hwy\"],\n",
       "\"y\":[\"cyl\",\"cty\",\"displ\",\"cyl\",\"cty\",\"hwy\",\"cty\",\"displ\",\"cyl\",\"cty\"],\n",
       "\"angle\":[0.7853981633974483,2.356194490192345,0.7853981633974483,0.7853981633974483,2.356194490192345,0.7853981633974483,2.356194490192345,2.356194490192345,2.356194490192345,0.7853981633974483]\n",
       "},\n",
       "\"mapping\":{\n",
       "\"x\":\"x\",\n",
       "\"y\":\"y\",\n",
       "\"angle\":\"angle\"\n",
       "},\n",
       "\"data_meta\":{\n",
       "\"series_annotations\":[{\n",
       "\"type\":\"str\",\n",
       "\"column\":\"x\"\n",
       "},{\n",
       "\"type\":\"str\",\n",
       "\"column\":\"y\"\n",
       "},{\n",
       "\"type\":\"float\",\n",
       "\"column\":\"corr\"\n",
       "},{\n",
       "\"type\":\"float\",\n",
       "\"column\":\"angle\"\n",
       "}]\n",
       "},\n",
       "\"pivot\":\"middle\",\n",
       "\"radius\":1.4142135623730951,\n",
       "\"size\":1.0,\n",
       "\"color\":\"white\"\n",
       "},{\n",
       "\"geom\":\"text\",\n",
       "\"data\":{\n",
       "\"x\":[\"displ\",\"year\",\"cyl\",\"cty\",\"hwy\"],\n",
       "\"y\":[\"displ\",\"year\",\"cyl\",\"cty\",\"hwy\"]\n",
       "},\n",
       "\"mapping\":{\n",
       "\"x\":\"x\",\n",
       "\"y\":\"y\",\n",
       "\"label\":\"x\"\n",
       "},\n",
       "\"data_meta\":{\n",
       "\"series_annotations\":[{\n",
       "\"type\":\"str\",\n",
       "\"column\":\"x\"\n",
       "},{\n",
       "\"type\":\"str\",\n",
       "\"column\":\"y\"\n",
       "},{\n",
       "\"type\":\"float\",\n",
       "\"column\":\"corr\"\n",
       "}]\n",
       "},\n",
       "\"size_unit\":\"x\",\n",
       "\"size\":1.0\n",
       "}],\n",
       "\"metainfo_list\":[]\n",
       "};\n",
       "           var plotContainer = document.getElementById(\"94ZDBF\");\n",
       "           window.letsPlotCall(function() {{\n",
       "               LetsPlot.buildPlotFromProcessedSpecs(plotSpec, -1, -1, plotContainer);\n",
       "           }});\n",
       "       })();\n",
       "   </script>"
      ],
      "text/plain": [
       "<lets_plot.plot.core.PlotSpec at 0x7050b40f6e30>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ggplot() + \\\n",
    "    geom_pie(aes(\"x\", \"y\", slice=\"corr\", paint_a=\"corr\"), \\\n",
    "             data=corr_df1, stat='identity', \\\n",
    "             size=1, size_unit='x', spacer_width=1, \\\n",
    "             fill_by='paint_a', tooltips='none') + \\\n",
    "    geom_pie(aes(\"x\", \"y\", slice=\"corr\", paint_b=\"half\"), \\\n",
    "             data=corr_df1, stat='identity', \\\n",
    "             size=1, size_unit='x', show_legend=False, \\\n",
    "             fill_by='paint_b', tooltips='none') + \\\n",
    "    geom_point(aes(\"x\", \"y\", paint_a=\"corr\"), \\\n",
    "               data=corr_df2, shape=22, size=1, stroke=1, size_unit='x', \\\n",
    "               color=\"white\", fill_by='paint_a', tooltips='none') + \\\n",
    "    geom_spoke(aes(\"x\", \"y\", angle=\"angle\"), \\\n",
    "               radius=np.sqrt(2), data=corr_df2, \\\n",
    "               pivot='middle', size=1, color=\"white\") + \\\n",
    "    geom_text(aes(\"x\", \"y\", label=\"x\"), \\\n",
    "              data=corr_df0, size=1, size_unit='x') + \\\n",
    "    scale_x_discrete(breaks=vars, expand=[.1, 0]) + scale_y_discrete(breaks=vars, expand=[.1, 0]) + \\\n",
    "    scale_gradient2('paint_a', low=\"#a50026\", mid=\"white\", high=\"#313695\") + \\\n",
    "    scale_manual('paint_b', values=[\"rgba(0, 0, 0, 0)\", \"lightgrey\"]) + \\\n",
    "    coord_fixed() + \\\n",
    "    ggsize(660, 600) + \\\n",
    "    theme_void()"
   ]
  }
 ],
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