demographic.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# A predictive approach to estimate the maximum voluntary activation of the shoulder muscles\n",
    "\n",
    "Romain Martinez<sup>*a</sup>, Jonathan Tremblay<sup>a</sup>, Mickael Begon<sup>a</sup>, Fabien Dal Maso<sup>a</sup>\n",
    "\n",
    "<sup>a</sup>School of Kinesiology and Exercise Science, Faculty of Medicine, University of Montreal\n",
    "\n",
    "[GitHub repo](https://github.com/romainmartinez/mvc)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Appendix: demographic data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pathlib import Path\n",
    "\n",
    "import pandas as pd\n",
    "import altair as alt"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "- hvsf (79): ref OK\n",
    "- evsn (32): ref OK\n",
    "- sylvain (10): ref OK\n",
    "- patrick (16): ref\n",
    "- yoann (22): ref\n",
    "- violon (10): ref OK\n",
    "- tennis (16): ref OK"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\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>participant</th>\n",
       "      <th>study</th>\n",
       "      <th>sex</th>\n",
       "      <th>mass</th>\n",
       "      <th>height</th>\n",
       "      <th>age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>sarc</td>\n",
       "      <td>hvsf</td>\n",
       "      <td>w</td>\n",
       "      <td>55.5</td>\n",
       "      <td>170.0</td>\n",
       "      <td>21.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>inea</td>\n",
       "      <td>hvsf</td>\n",
       "      <td>w</td>\n",
       "      <td>55.8</td>\n",
       "      <td>167.0</td>\n",
       "      <td>20.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>dapo</td>\n",
       "      <td>hvsf</td>\n",
       "      <td>w</td>\n",
       "      <td>61.4</td>\n",
       "      <td>176.0</td>\n",
       "      <td>22.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>laug</td>\n",
       "      <td>hvsf</td>\n",
       "      <td>w</td>\n",
       "      <td>66.0</td>\n",
       "      <td>180.0</td>\n",
       "      <td>21.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>mara</td>\n",
       "      <td>hvsf</td>\n",
       "      <td>w</td>\n",
       "      <td>56.7</td>\n",
       "      <td>163.0</td>\n",
       "      <td>21.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  participant study sex  mass  height   age\n",
       "0        sarc  hvsf   w  55.5   170.0  21.0\n",
       "1        inea  hvsf   w  55.8   167.0  20.0\n",
       "2        dapo  hvsf   w  61.4   176.0  22.0\n",
       "3        laug  hvsf   w  66.0   180.0  21.0\n",
       "4        mara  hvsf   w  56.7   163.0  21.0"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_path = Path(\"./data/demo.csv\")\n",
    "\n",
    "demo_p3 = pd.read_csv(data_path)\n",
    "demo_p3.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "shwartz = {\n",
    "    \"n\": 13,\n",
    "    \"male\": 13,\n",
    "    \"female\": 0,\n",
    "    \"age_mean\": 25.3,\n",
    "    \"age_std\": 3.7,\n",
    "    \"mass_mean\": 76.2,\n",
    "    \"mass_std\": 11.5,\n",
    "    \"height_mean\": 180,\n",
    "    \"height_std\": 5,\n",
    "}\n",
    "\n",
    "mcdonald = {\n",
    "    \"n\": 10,\n",
    "    \"male\": 10,\n",
    "    \"female\": 0,\n",
    "    \"age_mean\": 23.6,\n",
    "    \"age_std\": 3.4,\n",
    "    \"mass_mean\": 79.4,\n",
    "    \"mass_std\": 12.6,\n",
    "    \"height_mean\": 179,\n",
    "    \"height_std\": 4.8,\n",
    "}\n",
    "\n",
    "boettcher = {\n",
    "    \"n\": 15,\n",
    "    \"male\": 9,\n",
    "    \"female\": 6,\n",
    "    \"age_mean\": 28.4,\n",
    "    \"age_std\": None,\n",
    "    \"mass_mean\": None,\n",
    "    \"mass_std\": None,\n",
    "    \"height_mean\": None,\n",
    "    \"height_std\": None,\n",
    "}\n",
    "\n",
    "dalmaso = {\n",
    "    \"n\": 38,\n",
    "    \"male\": 24,\n",
    "    \"female\": 14,\n",
    "    \"age_mean\": 23.50,\n",
    "    \"age_std\": 2.50,\n",
    "    \"mass_mean\": 72.25,\n",
    "    \"mass_std\": 11.65,\n",
    "    \"height_mean\": 180.50,\n",
    "    \"height_std\": 10,\n",
    "}\n",
    "\n",
    "us = {\n",
    "    \"n\": demo_p3.shape[0] + 38,\n",
    "    \"male\": demo_p3.query('sex == \"m\"').shape[0] + 24,\n",
    "    \"female\": demo_p3.query('sex == \"w\"').shape[0] + 14,\n",
    "    \"age_mean\": demo_p3[\"age\"].mean(),\n",
    "    \"age_std\": demo_p3[\"age\"].std(),\n",
    "    \"mass_mean\": demo_p3[\"mass\"].mean(),\n",
    "    \"mass_std\": demo_p3[\"mass\"].std(),\n",
    "    \"height_mean\": demo_p3[\"height\"].mean(),\n",
    "    \"height_std\": demo_p3[\"height\"].std(),\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
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       "    }\n",
       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>index</th>\n",
       "      <th>age_mean</th>\n",
       "      <th>age_std</th>\n",
       "      <th>female</th>\n",
       "      <th>height_mean</th>\n",
       "      <th>height_std</th>\n",
       "      <th>male</th>\n",
       "      <th>mass_mean</th>\n",
       "      <th>mass_std</th>\n",
       "      <th>n</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>P-3</td>\n",
       "      <td>25.669725</td>\n",
       "      <td>6.728952</td>\n",
       "      <td>59.0</td>\n",
       "      <td>174.307087</td>\n",
       "      <td>9.406543</td>\n",
       "      <td>106.0</td>\n",
       "      <td>71.280394</td>\n",
       "      <td>12.548017</td>\n",
       "      <td>165.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>DM-14</td>\n",
       "      <td>23.500000</td>\n",
       "      <td>2.500000</td>\n",
       "      <td>14.0</td>\n",
       "      <td>180.500000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>24.0</td>\n",
       "      <td>72.250000</td>\n",
       "      <td>11.650000</td>\n",
       "      <td>38.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Mcdonald</td>\n",
       "      <td>23.600000</td>\n",
       "      <td>3.400000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>179.000000</td>\n",
       "      <td>4.800000</td>\n",
       "      <td>10.0</td>\n",
       "      <td>79.400000</td>\n",
       "      <td>12.600000</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Shwartz</td>\n",
       "      <td>25.300000</td>\n",
       "      <td>3.700000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>180.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>13.0</td>\n",
       "      <td>76.200000</td>\n",
       "      <td>11.500000</td>\n",
       "      <td>13.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Boettcher</td>\n",
       "      <td>28.400000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>15.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       index   age_mean   age_std  female  height_mean  height_std   male  \\\n",
       "0        P-3  25.669725  6.728952    59.0   174.307087    9.406543  106.0   \n",
       "1      DM-14  23.500000  2.500000    14.0   180.500000   10.000000   24.0   \n",
       "2   Mcdonald  23.600000  3.400000     0.0   179.000000    4.800000   10.0   \n",
       "3    Shwartz  25.300000  3.700000     0.0   180.000000    5.000000   13.0   \n",
       "4  Boettcher  28.400000       NaN     6.0          NaN         NaN    9.0   \n",
       "\n",
       "   mass_mean   mass_std      n  \n",
       "0  71.280394  12.548017  165.0  \n",
       "1  72.250000  11.650000   38.0  \n",
       "2  79.400000  12.600000   10.0  \n",
       "3  76.200000  11.500000   13.0  \n",
       "4        NaN        NaN   15.0  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d = pd.DataFrame({'P-3': us, 'DM-14': dalmaso, 'Mcdonald': mcdonald, 'Shwartz':shwartz, 'Boettcher': boettcher}).T.reset_index()\n",
    "d"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "p = alt.Chart().mark_circle(color='grey', size=200).encode(alt.Y(\"index\", title=None))\n",
    "s = (\n",
    "    alt.Chart()\n",
    "    .mark_rule(color='grey', size=3)\n",
    "    .encode(alt.X(\"x:Q\", scale=alt.Scale(zero=False)), alt.X2(\"x2:Q\"), alt.Y(\"index\"))\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "$schema": "https://vega.github.io/schema/vega-lite/v2.6.0.json",
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         "width": 400
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       "datasets": {
        "data-b7a2ede0781e9cc99e03d91c8773bde9": [
         {
          "age_mean": 25.6697247706422,
          "age_std": 6.728952158082983,
          "female": 59,
          "height_mean": 174.30708661417322,
          "height_std": 9.40654264963608,
          "index": "P-3",
          "male": 106,
          "mass_mean": 71.2803937007874,
          "mass_std": 12.548017069778476,
          "n": 165
         },
         {
          "age_mean": 23.5,
          "age_std": 2.5,
          "female": 14,
          "height_mean": 180.5,
          "height_std": 10,
          "index": "DM-14",
          "male": 24,
          "mass_mean": 72.25,
          "mass_std": 11.65,
          "n": 38
         },
         {
          "age_mean": 23.6,
          "age_std": 3.4,
          "female": 0,
          "height_mean": 179,
          "height_std": 4.8,
          "index": "Mcdonald",
          "male": 10,
          "mass_mean": 79.4,
          "mass_std": 12.6,
          "n": 10
         },
         {
          "age_mean": 25.3,
          "age_std": 3.7,
          "female": 0,
          "height_mean": 180,
          "height_std": 5,
          "index": "Shwartz",
          "male": 13,
          "mass_mean": 76.2,
          "mass_std": 11.5,
          "n": 13
         },
         {
          "age_mean": 28.4,
          "age_std": null,
          "female": 6,
          "height_mean": null,
          "height_std": null,
          "index": "Boettcher",
          "male": 9,
          "mass_mean": null,
          "mass_std": null,
          "n": 15
         }
        ]
       },
       "facet": {},
       "spec": {
        "layer": [
         {
          "encoding": {
           "x": {
            "field": "age_mean",
            "title": "Age (year)",
            "type": "quantitative"
           },
           "y": {
            "field": "index",
            "title": null,
            "type": "nominal"
           }
          },
          "mark": {
           "color": "grey",
           "size": 200,
           "type": "circle"
          }
         },
         {
          "encoding": {
           "x": {
            "field": "x",
            "scale": {
             "zero": false
            },
            "type": "quantitative"
           },
           "x2": {
            "field": "x2",
            "type": "quantitative"
           },
           "y": {
            "field": "index",
            "type": "nominal"
           }
          },
          "mark": {
           "color": "grey",
           "size": 3,
           "type": "rule"
          },
          "transform": [
           {
            "as": "x",
            "calculate": "datum.age_mean - datum.age_std"
           },
           {
            "as": "x2",
            "calculate": "datum.age_mean + datum.age_std"
           }
          ]
         }
        ]
       }
      },
      "image/png": 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KBOGbiBzx1OTStYpJZXxUVaoLephATaBVx0Ht9VMaWEXuwr355pvP3bRpk76fvN2Tkh5VjGQ9qqBXf13bo35MJ5VoAjWBTuo7jdVLcYJYv379frfeeuuhS5YsuT0bS5Zl1y1duvSy1atXXzMKiBT1mMRo1mMS1JqrY3s0h21ZySZQE2hZn2m8vCeIxiEu1YDtUQquxgvbHo1DXLgBE6gJtLCztFXQE0RbSBdrx/YohlNbpWyPtpAe344J1AQ63ktaLuEJomXAxzRne9geTSDQBb8ygZpAmxgblWR2YWAJAOtRyQ1qr2x71A5pJYFdsIcJ1ARaaRA0UbkLA8sE2oRnVJNpv6qGX921u2APE6gJtO5xUVleFwaWCbSyG9QuwH5VO6SVBHbBHiZQE2ilQdBE5S4MrNQJdN26dQdv27btvlmW7ZPn+ZaYV/cLIQSlGdzusT2a8PLJZdoek2NXd00TqAm0bp+qLM8TRGUIRwo45ZRT7rt169ZnA/cdUmgb8PchBOXz/f/H9mjOHpNItj0mQa2ZOiZQE2gznlVBqieICuAtUjWE8DvASeOk53n+2bVr1yoh/e2P7TEOsXY/tz3axXux1maJQB8OfBj4HyAH7hBTnq1foKDyir4VuDOwa0zwrUTcQx9nY0nHGT1hN2eLXq+3f5ZlbyzRwgdDCG8xgZZArKWiJtCWgC7QzKwR6Grg8VGv5cDXgd8FvjGg61HAkTH3qH6/ADjGBFrAGxIp4gmifkOsXbt2RZ7negkt/CxduvSla9asucr2KAxZKwVtj1ZgLtTILBPorwCXAg8Drl2g7S7AbcAzgCOAF5tAC/lDEoU8QdRrhk2bNu15yy23bLevWaSFLMve3+v1zrM9iqDVXhnboz2sx7U0awT6IeDyuISrk4NvBkZNDGcAx8Wfj0+LQEMIippPGWcIf24EjIARMAI7IHBLCGH3VHGZNQIdXMLtY6qEx++L/zkbOGsA7LsCXwLuDdyyYcOGkGVZb6Ex5ufnmZuby5owkgm0CVQt0wgYgZ0EARNoTYbW/s0wAl0oXkf07wScCewNfA24F3DrsH40fYjIBFqT9S3GCBiBnREBE2hNVi9KoHsC58UTuPq3lnK19Dv0aZpAa9K9kBjvjRSCqbVCqdhDScGBd5ZV3HugZRFrp3wqflVV2y7oMUtLuFXtZQJtBMH6hXZhYAmVlPTo9XorsyzTgbsyz8tCCFempEeZzi8saz2qoFd/3S7YwwTqixTqHxkVJXZhYKVGoOvXrz/gtttue0MJ0/h7oCXAarOox0ebaC/elgnUBJqON8aeeIJoxiRr1qx52NKlS1eOk+6biMYhNN3PPT6mi/9g6yZQE2g63mgCbdwWIYT7Ac8adhdunuf5kiVL/r7X6223X+oJu3GzlGrA9igFV6OFTaAm0EYdbBLhniAmQa1cHWVjyfP8fnme76MT6lmWXbPbbrt9YcWKFTd677Aclm2X9vhoG/HR7ZlATaDpeKMj0ORsoQ55wk7LLLZHOvYwgZpA0/FGE2hytjCBpmcSE2g6NjGBmkDT8UYTaHK2MIGmZxITaDo2MYGaQNPxRhNocrYwgaZnEhNoOjYxgZpA0/FGE2hytjCBpmcSE2g6NjGBmkDT8UYTaHK2MIGmZxITaDo2MYGaQNPxRhNoLbZYt27dEXmeH5jn+R5Zlv0sy7IrTz755M9PKtwT9qTINVPP9mgG10mkmkBNoJP4TaN1PEFMBu/atWsfDTw9z/NfHSLheuD8EMKny0q3Pcoi1mx526NZfMtIN4GaQMv4SytlPUGUhzmEcCzwtHE1syy7oNfrvWtcucHPbY8yaDVf1vZoHuOiLTRJoL8J6PLqOwI5cEnM53kMcCgw9k7OokrEcgcC+wOl3rCdzqwkyi0U9wRRDmRFnnmev7RErTPLRKK2RwlkWyhqe7QAcsEmmiTQDwBnAx8BlsacnG8C9miIQE8AdgFeX1D324uZQMug1U5ZTxDlcA4hKP/tXYrWyrLs+l6v95yi5W2Poki1U872aAfnIq00SaAisr2Bs4DLgDsAtwFPAUR2P4yD/hzgR8Bj4t9PAh4CPBl4BrAf8FFgPbAZ2Bd4GaBDEV8EvgVsBQ4AlgArgOOj8kqofWSMTH8wDBATaBE3abeMJ4jieIcQNFZWFa/xi5JLlixZX/Rgke1RFt1my9sezeJbRnqTBLor8IJIhAcDHwZeAvwBoLffxwI67PBZ4BDgC8ADYsR6N+DBgLJCrIkR638A/w08E3holH153Pf5ciTfwQhU7V8EvBH44ChQTKBl3KVa2RCClvL9GIHOIhBCyJpWzgTaNMLF5TdFoHKiJ0bSVG92A84AlOlB0ehhwKtiN78N3AN4P7Ah7pPqb+8DTgEeBjwSeCXwc2A5cB3wPEAEKlk3LyBQtX8+8K+RQG9vasOGDSHLst5CeObn55mbm2vc8YubZbKSqQ8sE+hkdnWt2UHABFrcVqnPV0U0aYpA1baiRR1s0PKrnpMBRYVfXbAH2ifQ44DnAhcCVwOvBj4GBOArcen3irhEqwNKKisC1YEkEesL4zLxmZGstwEnjgPBEeg4hOr73ARaH5aWlCYCJtDidjGBLo7V/eP+p5ZjtfepJVgt6WoJd/AUbp9AlZfw+3E59xpAe5aHR/JUtPongMpuAVT2CQsI9OHAW+OPItdPxENF6qX2iC4d1l0TaHGHb6tkFwaWsGpDD++BFvfKNuxRvDeTl7Qek2NXd80mI9C6+9qIPBNoI7BWEuoJohx8ZU/hAteHEHwKtxzMyZT2+EjGFJhAfZFCOt4Ye+IJopxJ/D3QYnjZr4rh1FapLtjDBGoCbWu8FG6nCwOrrSXcPqi+iWi8e9mvxmPUZoku2MMEagJtc8wUaqsLA6ttAlV7MRLVd6eHXapwQ5Zl5/d6vU8VMsJAIdujLGLNlrc9msW3jHQTqAm0jL+0UtYTRDWYdbAoy7K5eI3mTXmeXxVC+LdJpdoekyLXTD3boxlcJ5FqAjWBTuI3jdbxBNEovKWF2x6lIWu0gu3RKLylhJtATaClHKaNwp4g2kC5eBu2R3Gs2ihpe7SBcrE2TKAm0GKe0mIpTxAtgl2gKdujAEgtFrE9WgR7TFM7PYHqer+jjz56h+v90jGRe2IEjIARMAKpIpDnOQceeOAOV8HO/N2wRQHfuHHjxTFrS9EqLmcEjIARMAJGQAhcsnLlyt9dCMXORKD5ypUrZ15f3apkPdIZ0bZHOrZQT2wP26MJBEb51cwTSlGwPLCKItVOOdujHZyLtmJ7FEWqnXK2Rzs4F23FBOrIraivtFLOE0QrMBduxPYoDFUrBW2PVmAu3IgJ1ARa2FnaKOgJog2Ui7dhexTHqo2StkcbKBdvY6cnUJ3EPemkk5R3dKYf65GW+WwP26MJBOxXTaA6ucxR9thp9kAnh841jYARMAJGwAjsiIAJ1F5hBIyAETACRmACBEygE4DmKkbACBgBI2AEdkYC3QW4EFgPKHvFEuBs4O7AbsDLgf9IxDXuAJwH7AnsBZwBXAQcCpwJ3Ax8FViRSH9HdWOUHk8AXgXcBNwCPCvqlKo6o/To93dd9KPnpapA7NcoPQ4E/grYAuwKKG3aDxPWZZQevwNsAG4E/gd4WcI6qGtLgbcCd464Px/45gyO81F6pDzOjwR0S93PgX/WV4kHfGVf4EvAg4BvD/OhnY1ADwLeBtwDmI8E+ijgROAPgd8GTgUel8iA+3PgXsDKSKBfAQ4APgs8E7gKOB84VzdlJNLnYd0YpceV0Tl/BIh8bgD+egb1UJf/KNrp60DqBDrKHp+IL5Dysz8GrgEunUF76NYxjekvAh+KY/pzCetxVLwl7YT4+wXAMTM4zkfpkfI4vwx4PPC9OIcKe41hvUC+Bzg42sQEChwWox1N1q+PBLo/8CbgSYCuatLbqsg0hUfRst7qNgN3iwPqPtHAihb0/Bmgf69JocMj+jBMD+Wy3AP4WayjyEcTt14GUn1G6fGb8c1VKxl6MUudQIfpcX/gX4GPAIdH4pRPbUvVGMAoe7wO+Kf4IzJ9JfDvCeuhrkmX22LUf0R8kdFEPkvjfJgeL058nPdx1+qjXrJEptcBb4grlfrmxlMdgW4/et49QKAipvfHpdx7Ak8HPpXYYLtTnNg0MXwyLjUcEvv4p8AjgZcm1udh3RnU470DBYT5XwKPAG6dMT0+BkiX4wDZRIMtdQLtQzxoD00eijjlS1rh+Lvoa2+fMXvIFoqw1wL/C/wU0BLi1hnQQ1s08iP9fH6Gx/mgHh+fgXGulUcFUdqu0MrL04Bfiy/F/2IC3XHkDBKolmyXxb04kakA09u41sRTeH4jEvxrgHfFvn4NUNSjR0u5WuZN/TuuC/XoY3sS8FhALwJayk39WaiH+q09FPV9H+CuceApok75WajHcuDauA+nfiuS1guNXmxSfhbqsXskTr3MfAc4DfgJsCllJQb6Jv/Rvtv94hL0rI3zvip9Pe4dzzfMwjjXvrnOlfw+kMefBwL/FSNT+dF2z862B9pXfpBAdYhFy6QCT+velwMPGFhanOa401vQp4Hj4/Javy+KEHTA4xuAIgRFC6lFzYO4jdJjNaAl6WfHgyvTxLpI26P06Nd9zIxEoKP00J6hfO3LgF7YtMeuZelUn2F6aCxrz01jWIfTXhjPPLw6VSWi/2s1QAcD9wb0gqyXYi0/z9I41zgepofm2BTHuQ6Q6uDQE+OBs5PjSoWCqv7jCHTIwBkkUL2xat/tjoDewnXq9YJEBttZcULWUlT/0QlDbWy/NpKOCD/1U4bD9PiTOEFrn0enPvXogJf2HlJ9Rtmjv+w8KwQ6So/7xpPe2nP/AfDcxF9sRumhQ4A6Ta9VAUUSWlJPeXVDp+w17+gFXv/WEqgOPyn6maVxPkwPvZR9K57bSHGc6+DTi+LLlpb79QKpKNQEmuos7H4ZASNgBIzA7COwsy7hzr7lrIERMAJGwAhMFQET6FThd+NGwAgYASMwqwiYQGfVcu63ETACRsAITBUBE+hU4XfjRsAIGAEjMKsImEBn1XLutxEwAkbACEwVARPoVOF340bACBgBIzCrCJhAZ9Vy7rcRMAJGwAhMFQET6FThd+NGoDACunFKd6TqggDdWFPno2vudIPMX9QpdIGsf4zXZOqSAD9GoBMImEA7YUYr0XEEdMOL0i3p1izd3KR7Uut6lNrvauDBgFI7NfXovmNd9L5fvJu2qXYs1wi0hoAJtDWo3ZARmBgBZRfRnbRvjNeOPTxmTJFApYV7B6Br+BSl6hL4vwV0z7DS8iniU5IE5YtVhCkiHnyUskyJzCVH+UCVb7Z/gblSgCndnJIOj5KlLEBK6K5k0LpWUun1RMRKVq8MKEpKfEWsr3yvulT8byZGwhWNQEIImEATMoa7YgRGICAy0l3Nyo4iAlRWHpGeHqW3E3nqIm9div2SmEBaZKtL1fVbuTGVCu8/4+Xkg818JqYxU0o5pXHSPdDKm6vUTt+M9+F+dIQsJR9WsgMRr9JWKS2gfnSfqPqsNFEibRGrEiCo7I9j5h0b2wjMPAIm0Jk3oRXoOAIiR6VT2hijyfNjzk5Fdrr8+pZIkq+IyRAUMSqFl6LA98QsE0qKrcTBSkqgRASDj0hS5U4ElFRYKcDeAihaVOJ5ZTzR5eyjZN0deDLwUEAXc+sS9KMjgepydJFx/1Hftfw8+LeOm8/qdRkBE2iXrWvduoDA6YDIceGjDBJvjnlrtSSqyFNR6o0xAlVKLEWTRwBfAO4So0qR6eCjTBnam9ThJD2KWJWA+voYdQ5GpgtliTwV1Sr5s5aRlZNWqdCOiQSqrBa/t4BADwIe1AXDWAcjYAK1DxiBdBFQ1PjtuJSqPc3+I7LSUq7SXWkJV8mjlffyD+IepPIZilx1OEgRodJ+adlXS7lKwD74KOfkd4Fj4x8Pj4mc9d/Hx6XZ/kGjhbIUlV4U911FpEoEreVe7cNqCVf5OOBdcDwAAAFcSURBVJXirf8oZ+11A22li7x7ZgQKIGACLQCSixiBKSGgpdEL4/6mcqX2HyV/XxlPzipSFDnq4I+Ishd/tPz6R4C+oqKDQdqD1NdgRGCDzyrgOfEQUf/vSqSt5dz940Eg/X2YLC0ha8/zUTG3q5aP94jJrBcSqJIXq6+Kpgd1mRK0btYIVEfABFodQ0swAtNEQN8J3SuS0r3ib+1BKjos8mgZVvug9wc0H+iE7zlxz/XVRQQULCO5imAVzSoy9WMEZh4BE+jMm9AK7OQIaNn2NTGC3ByjUS3nLtzrXAym1wLLIpHqsNLngKfEPdO64H0ncGk8DVyXTMsxAlNFwAQ6VfjduBEwAkbACMwqAibQWbWc+20EjIARMAJTRcAEOlX43bgRMAJGwAjMKgIm0Fm1nPttBIyAETACU0XABDpV+N24ETACRsAIzCoCJtBZtZz7bQSMgBEwAlNF4P8AtJWM1C6zhQIAAAAASUVORK5CYII=",
      "text/plain": [
       "<VegaLite 2 object>\n",
       "\n",
       "If you see this message, it means the renderer has not been properly enabled\n",
       "for the frontend that you are using. For more information, see\n",
       "https://altair-viz.github.io/user_guide/troubleshooting.html\n"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "age_p = p.encode(alt.X(\"age_mean\", title=\"Age (year)\"))\n",
    "age_s = s.transform_calculate(\n",
    "    x=\"datum.age_mean - datum.age_std\", x2=\"datum.age_mean + datum.age_std\"\n",
    ")\n",
    "\n",
    "age = (age_p + age_s).facet(data=d)\n",
    "age"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.vegalite.v2+json": {
       "$schema": "https://vega.github.io/schema/vega-lite/v2.6.0.json",
       "config": {
        "view": {
         "height": 300,
         "width": 400
        }
       },
       "data": {
        "name": "data-b7a2ede0781e9cc99e03d91c8773bde9"
       },
       "datasets": {
        "data-b7a2ede0781e9cc99e03d91c8773bde9": [
         {
          "age_mean": 25.6697247706422,
          "age_std": 6.728952158082983,
          "female": 59,
          "height_mean": 174.30708661417322,
          "height_std": 9.40654264963608,
          "index": "P-3",
          "male": 106,
          "mass_mean": 71.2803937007874,
          "mass_std": 12.548017069778476,
          "n": 165
         },
         {
          "age_mean": 23.5,
          "age_std": 2.5,
          "female": 14,
          "height_mean": 180.5,
          "height_std": 10,
          "index": "DM-14",
          "male": 24,
          "mass_mean": 72.25,
          "mass_std": 11.65,
          "n": 38
         },
         {
          "age_mean": 23.6,
          "age_std": 3.4,
          "female": 0,
          "height_mean": 179,
          "height_std": 4.8,
          "index": "Mcdonald",
          "male": 10,
          "mass_mean": 79.4,
          "mass_std": 12.6,
          "n": 10
         },
         {
          "age_mean": 25.3,
          "age_std": 3.7,
          "female": 0,
          "height_mean": 180,
          "height_std": 5,
          "index": "Shwartz",
          "male": 13,
          "mass_mean": 76.2,
          "mass_std": 11.5,
          "n": 13
         },
         {
          "age_mean": 28.4,
          "age_std": null,
          "female": 6,
          "height_mean": null,
          "height_std": null,
          "index": "Boettcher",
          "male": 9,
          "mass_mean": null,
          "mass_std": null,
          "n": 15
         }
        ]
       },
       "facet": {},
       "spec": {
        "layer": [
         {
          "encoding": {
           "x": {
            "field": "height_mean",
            "title": "Height (cm)",
            "type": "quantitative"
           },
           "y": {
            "field": "index",
            "title": null,
            "type": "nominal"
           }
          },
          "mark": {
           "color": "grey",
           "size": 200,
           "type": "circle"
          }
         },
         {
          "encoding": {
           "x": {
            "field": "x",
            "scale": {
             "zero": false
            },
            "type": "quantitative"
           },
           "x2": {
            "field": "x2",
            "type": "quantitative"
           },
           "y": {
            "field": "index",
            "type": "nominal"
           }
          },
          "mark": {
           "color": "grey",
           "size": 3,
           "type": "rule"
          },
          "transform": [
           {
            "as": "x",
            "calculate": "datum.height_mean - datum.height_std"
           },
           {
            "as": "x2",
            "calculate": "datum.height_mean + datum.height_std"
           }
          ]
         }
        ]
       }
      },
      "image/png": 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TzwBUZaz9UI3r95VlqcInHY+c+9diajGNjV4WU/OrlUCssZwH21EX04mJicc0Go0Zi3cXRfHx8fFx1cbe5si5f4dJTO8PfAL4CdAAdkpl2la2EVdd1LcBtwF2TMXKVVS84+FNG2KDV7t1zsE+6oNZHT3t/o1RHFV+K1asOHBqampFr/SKonjH+Pj4+cM0vgybmC4HHpkA3wz4EfBg4IoW6E8EDk21U/XnC4GjLKa9hnHsvFEdLGLUbrQ2vxhJ88uT30zVYDp4vfm66657Zvv+uzn37zCL6W2BbwH3A65s64wdAC1kHw0cArzEYhq7yXq1zjnYnZn22ovdz3P/xhiOIr+yLPcCXt8vuaIozhofH9ds5JYjZ37DJqYfBy5N07x/B84C3tulk04Hjkk/n7GY9hvK1c7POdgtptX6dFgGM/fv9unfU0455eC5c+deHP+0eluYmpp66YoVK86st9XeWxs2MW2d5m1e5d2B5ty6Fq1bYd4B+DawN3Dd5ORkWRTFeDuesbGxLVUILAa9B0+nM83P/GIEYtaOv8Hzs5h2ZnxTENP2K3s2sEuaVlgA/AC4K/C3Tgj8AlLs5mu39mAW42l+5hcjELPuJf4spqMjprcE3p7e5NXfNd2r6eGOh8U0dvNZTM2vXgKx1noRg9gnxKxH0T+vmUKWBVhjobyttcW0XqKjOFjUSdD8YjTNL09+ExMTpzUaDS3J9XQURbF5p512eubY2Ng1rQY59+8wTfP21An9nmQx7ZfY9OfnHOzy3P7F+tv8zK8KgbR94ESvtv6eaa+kMjrPYlpvZ3iwjfE0P/OLEYhZDzL+et0BSZvzlGWpb2pscwzSvxg5cGbq7QSjMbSVfc7B7sw03tXu3xjDUec3w968f9VXHb03byzGZs3amWm96Ed9sIjSNL8YQfMbDn7NqjFFUSxoNBrXzZkz54oNGzasO+ecc7R/QNcj5/51ZurMNHb3tVnnHOzOTONd7f6NMTS/my4/i6nFNBbdFlPzq5VArDGLlfnFCFS3tphaTKtHTwdLD2YxnOZnfjECMWvHX3V+FlOLafXosZjWys7T0HGcFoMYQ/Orzs9iajGtHj0W01rZWUzjOC0GMYbmV52fxdRiWj16LKa1srOYxnFaDGIMza86P4upxbR69FhMa2VnMY3jtBjEGObIb9myZbvPmzdv/6Iodl28ePF5a9eufSxwSVmWG2JXW6+1xdRiWmtE5Xgztl6g/Yt1t/mZX4xA79ZLly5dMH/+/GMbjcahTauFCxcetmHDhhsKlxRFcVGj0Ti3LMtre291cGdaTC2mtUaXB9sYTvMzvxiBmHUu8VeW5Z0A1Z7Wn1uOVjFNgnrlpk2bJlatWvXr2JXHrQcppncD3gTcAmgAFwEq7n0UcAAwFnd/qxb2AvYAvthPu94BqR9aM5+by83YzVP7N3MfTneG+ZlfjEBv1uPj46cVRbFNlZl2MU2tXVqW5Um9tTy4swYpph8DzgY+CcxNNUXfAtx8QGJ6PLAD8IZ+cFlM+6E187kebGdmZLGKMTK/mza/siwPB47rdJVdxFSnarr3gsGRmbnlQYqpRG0BcKYWi4GdgH8ARwISvj8AtxME4I/Aw9LvTwYWA48DjgZ2Bz4FrAQ2AXcEXgF8A7gY+AWwGdgTmAMsbekIFQfXfLsy1qs74bCYzhwk/ZxhMe2H1rbnmp/5xQjErHOIv7IszwD27lNMLyvL8pWxq49ZD1JMdwRemERxH5XVAV4GPAZ4DvBw4PbA14D9gW8C+6ZMdjfgvsB7gFNSJvtd4MfAM4Alqe1LgacC30lC3JqZ6vP1pPJm4MJumCymsQBqt87hZnTmUm+ftrbm/o2xNb/O/Mqy1FJgbUdZlkVtjfXY0KDEVBfy6CSgcmVn4HTgzylLPQh4VfJRrzcvBD4MTKZ1Vf3ufOA1wP2ABwInAtcDNwOuAo4FJKZqa2ObmOrz3w18PYnpDR81OTlZFkWhRe2tjrGxsS3gHew9Rk6X08zP/GIEYtaOv+HkZzGdvt+URb48TdHqzFMBZYvfb1szbYrpMcBzgY8ClwOvBj4NlMD30vTwZWkaVy836VyJqV5mksi+KE0lvz4J9xQw46K0M9PYzefM1PzqJRBrzWI6nPwsptP3273SeqmmbLVWqmlaTftqmrf1bd6mmN4a+G2a8v1lWuM8OAmpstgjAJ2renc691FtYnp/4G3pRxnt59ILSfJyGfCtTu5aTGM3n8XU/OolEGvNYjr8/LxmGuvDWbO2mNaL3oNZjKf5mV+MQMw6h/ir8jZvURTnjI+Pd303JkalN+tBrZn29ukZnGUxrbcTcrgZp7si+xfrb/MzvxiB3qz9PdPeOGV1lsW03u7wYBvjaX7mFyMQs84l/rwDUqwfZ8XaYlov9lxuxm5XZf9i/W1+5hcj0Lt1L3vzbty48bw1a9Zc03urgzvT07zem7fW6PJgG8NpfuYXIxCzzjH+Vq5cufvmzZsPaDQauy5ZsuTcdevWHT537txLli9frhdVszksphbTWoMxx5ux9QLtX6y7zc/8YgRi1jnHn8XUYhqL7jbrnINdrtq/WHebn/nFCMSsc44/i6nFNBbdFlPzq5VArLGcB1s/zMX6Nnd+Iy+m2mLw5JNP1i5LNxy6GeNd7hZMwARMwARGjUCj0WCvvfbaZl/g7b5RcA7gV69e/aVUaSYHd+yDCZiACZjA8BC4aGxs7MHt7o6qmDZaN77PrQ/bv8pj//ojYH798Wo/2/zML0YgZj2s8WcxjfX7QKyHNZgGAqNCo+ZXAVqLifmZX4xAzHpY489iGuv3gVgPazANBEaFRs2vAjSLaQya+Y08P4tpbSFQX0MWgxhL8zO/GIGYteNvNPmNpJi2v90b6/r6re1fjKn5mV+MQMza8Tea/EZSTGNdbWsTMAETMAET2JqAxdQRYQImYAImYAJBAhbTIECbm4AJmIAJmMAoi+kBwOuBjcD3gaWzFA47AW8HbgncCjgduADIxb8mljsC3wbuA2zIzL9XA/cHbga8M/HMhd8c4GzgTsDOwAnAdzPhtxtwEXCP1MkLgHcBGheuB54LXAscDRwLbAbOAj64ne6Vdv/uCrw1+XZz4MXAjzPyr4nlYcCbWrjmwk/M3gzcGlBfnwhcnBG/O6T4+yswF3gGoNJrdfPrFkfdxgzpxD0B3csvA37YKf5HWUy/ljprPfBu4Lw0sGyncWLLxzwPUOeOJTH9HrAnkIt/cnTHNIDuk3aOkpjm4t+hwAuApwDzU7BPZuTfg4CTgMOAewOvBR6RgX/y5zXAHsBtUzSuAn4K/FcSz4XAG4CvAAenwWRtigGJ7CCPTv69Nz0sfRp4CHA88JyM/BMP3bsS0n8F9gJ2ycg/ban6m/RAdC9A9/NnMvJvHJCQnpaEfl56eKo7/jrF0RO63JMa+yTmEva904OxYm+bY1TFVJ30oxTsgvLM9PdTBjk6dGl7h/QUtgnQk7hESplCLv7JbQ0OHwV0M0q0rsrIPwmnMiYxu0V6KPlBRv5JrN4CHA5oC7JXAEdk4J8E/ZvAd4C7pNjUoHUM8PMkCnrI1ACn3z07naNZFP3+CwO+Vzr5p8z+b8BUYnhkEv6c/Ds/Pdxpy1KJqQbeXPzTg5CE5N/TjIOyrP0z8u/xaeZrWXrQU1aq2bC6+XWKI8V3pzFX+7gr4frvFO962FSWqjjc6hhVMdXT4pdTIAmInkoeCLx8wAPEdM3Lp08CZwCfz8g/BdmuwGrgq0lM/5KRf+cCd05ipSfHDwNLMvJPD0jySVNEEq2nA9/KyL+ftYjpJUnw/wjcBvhieoDSQ4AGXh1vTL/Xw9X2OFr9a37evsCHgEcD+nsu/r0NeAcg0bo0iakEIhf/fpKWQDQDoSn8+6UlpVz8kz/vAS5Ps3WLAf0Myr/WOLq6yz2pmJNWfCQF3/8lrfiTxfSfBJSZKnu5WwKiFF5TrVsqy2yPUaLlM/4lDbivA96XmX/KVvR0pp8D03rBY4GvZ8JPIv9r4D8TT/WrsoH/ycQ/Tesq3l6VZh70QCKOEtQc4q9VrNTXEvtfpulfrT+vSL/T4KtDU8D6vdZat8fRLqYPT+86PBXQwKa+ls+z7Z/WxJWNKhaVpNwXeH9aA8zBP/XVOuD5ac3+9ml2QQlELv5pVk73i5IK9bNmcf5jQP61x1E3TdBDsB5C9C6BDv1dU/j/sJjeSEAdp7nwK9LgoDR+0FNXnQYfZX3KAI5LAtU8Jxf/Wn1uZqbNNdMc+GltTS/HKAPQACEfFewS0xz8k4jqZQpNR2v9RRmLnog/m4l/rWIlH+Wf7gWtReohT+tXejLXi2cSCU0NaxZn0Gumzbhr9U8ZigZXPcxJtHToRZpc/Gv6rKWbZmaak3/qyyvTw4iyek2f6n2DXPhJRLWOr8xeU6l6j+VRA/CvUxyp7zqNuepLLW1phm5RepjUexDbHKM6zSsQyg50Y/49Bb6egmbjODN1ltapmse/pZcDcvCvm5jmwk/+KaPfL72ApNkFPZzk4p9eitKgoPVcvW2sNUetW+XiX6tYaWpXU5V6GtfYoMG2+TblswANLJpW11Tc9jpa/dPLeXoj9Q/pw/VvDXJ6aMrBv05iqt/l4p/6V2+Wa81Q3yJQNq9ZiFz8u3uaYdI3LNTPeilTa/p1+9ctjrrdk3oJT+vfuoeVyWs5xGK6vUYAf44JmIAJmMDoEBjlzHR0etlXagImYAImMFACFtOB4nXjJmACJmACo0DAYjoKvexrNAETMAETGCgBi+lA8bpxEzABEzCBUSBgMR2FXvY1moAJmIAJDJSAxXSgeN24CZiACZjAKBCwmI5CL/saTaA6AY0R2v3KhwmYwDQELKYODxPIm4D2AP14+uK6PFWpOe3upO30tF1dt0P7J6tcmspsdTu076l2m9GOQvqM1kP7Havc2UsBbZzQfqxJFVG0PV3k0J6/KlkmH3yYwNASsJgObdfZ8REhUFVMXwRopxcJbxUx1RaN2u1IWwq2i6lKs2kzcu0/23E3mD76RtvtqSzYY9LOVX2Y+lQTyIeAxTSfvrAnJtCJgMRUVYRUu1OHBOxjLZmp9iZWQflmIW1lihKn1sxU+5uqjJ62M9RG9SpxpTJcOkeZqUrE6RxtM6dtNbVH9e9anNFex9prtnmoVKG279NepTpUpadZw/MyQDV6JbZqQ5+nvVC1raK2fdQG4w9IexM/KW0Y/onky1EOARMYVgIW02HtOfs9KgQkpsre2g9N82qDctVXfHMSJ5XvUyUV7WXaFNOJVJ/0u6k2qfYulri1iqmyV/1eG6HfIZVkWwksBSR4ErvrWhxQdRnt6apqI9pDVfUef5j2utZm+arNq0xTYvqN5I/2I9Zm+U9MnyEBV2F3XcOpac9T7R3rwwSGkoDFdCi7zU6PEAGJqcq1qeiBjn2S6ElMVQbqg8CfU8FsbUSvggk6pymmqqWqahgqTn4h8EjgU21i+uTUjj5Da5iqbjPdNK+mffW5J6WsVOX4VLVHGbOy2+uB2yUxlSArI1UlGlUqUWWfg9I1NW2an6WHhu1VjWaEQsiXuj0IWEy3B2V/hglUJzDdmqnuX2V8h6TSaBIwVVSZahFTCZwyySekAseaFpaotmamzReQVBtWLyxJlKcT01+k4twnAKpwJLFWBquC3Zr6vVUq9XVVmjZW1Q0VzdaLRlpvPSBVAzkCUJHx5mepKsdfq6OypQnMHgGL6eyx9yebQC8EphNT1W7V2qTexFUpPxWWV51UFbtvZqaavlX9Wa15rgKWp+nWmcRUbSiblGCqGPjVLc42i2A/LU3zygf9aO1WZbN2SRnrb3sUU63BvhBQgW0fJjCUBCymQ9ltdnqECMz0Nq+ySn1NZc+UIaoGqTLC1heQHpG+5qKMU9OzEkgVOP5b21djWjPTPdKLSBJGvTSkupLNQy8wqXh48wWk+yUxv0cSbb2ApKlgrZnqhaaZMlNlp7pOvdTkwwSGkoDFdP3lWdMAAADHSURBVCi7zU6bQM8E9BatinkrM22+BayvzUgsf91zK1ufqAxSYnkvQG/vRg69YawMVuup032NJ/IZtjWBgROwmA4csT/ABGadgKZe9dWaXVO2qKnfc4Je6WWleekt3EhTx6UNG/SClA8TGFoCFtOh7To7bgImYAImkAsBi2kuPWE/TMAETMAEhpaAxXRou86Om4AJmIAJ5ELAYppLT9gPEzABEzCBoSVgMR3arrPjJmACJmACuRCwmObSE/bDBEzABExgaAn8P4Acdy7t3Xu9AAAAAElFTkSuQmCC",
      "text/plain": [
       "<VegaLite 2 object>\n",
       "\n",
       "If you see this message, it means the renderer has not been properly enabled\n",
       "for the frontend that you are using. For more information, see\n",
       "https://altair-viz.github.io/user_guide/troubleshooting.html\n"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "height_p = p.encode(alt.X(\"height_mean\", title=\"Height (cm)\"))\n",
    "height_s = s.transform_calculate(\n",
    "    x=\"datum.height_mean - datum.height_std\", x2=\"datum.height_mean + datum.height_std\"\n",
    ")\n",
    "\n",
    "height = (height_p + height_s).facet(data=d)\n",
    "height"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.vegalite.v2+json": {
       "$schema": "https://vega.github.io/schema/vega-lite/v2.6.0.json",
       "config": {
        "view": {
         "height": 300,
         "width": 400
        }
       },
       "data": {
        "name": "data-b7a2ede0781e9cc99e03d91c8773bde9"
       },
       "datasets": {
        "data-b7a2ede0781e9cc99e03d91c8773bde9": [
         {
          "age_mean": 25.6697247706422,
          "age_std": 6.728952158082983,
          "female": 59,
          "height_mean": 174.30708661417322,
          "height_std": 9.40654264963608,
          "index": "P-3",
          "male": 106,
          "mass_mean": 71.2803937007874,
          "mass_std": 12.548017069778476,
          "n": 165
         },
         {
          "age_mean": 23.5,
          "age_std": 2.5,
          "female": 14,
          "height_mean": 180.5,
          "height_std": 10,
          "index": "DM-14",
          "male": 24,
          "mass_mean": 72.25,
          "mass_std": 11.65,
          "n": 38
         },
         {
          "age_mean": 23.6,
          "age_std": 3.4,
          "female": 0,
          "height_mean": 179,
          "height_std": 4.8,
          "index": "Mcdonald",
          "male": 10,
          "mass_mean": 79.4,
          "mass_std": 12.6,
          "n": 10
         },
         {
          "age_mean": 25.3,
          "age_std": 3.7,
          "female": 0,
          "height_mean": 180,
          "height_std": 5,
          "index": "Shwartz",
          "male": 13,
          "mass_mean": 76.2,
          "mass_std": 11.5,
          "n": 13
         },
         {
          "age_mean": 28.4,
          "age_std": null,
          "female": 6,
          "height_mean": null,
          "height_std": null,
          "index": "Boettcher",
          "male": 9,
          "mass_mean": null,
          "mass_std": null,
          "n": 15
         }
        ]
       },
       "facet": {},
       "spec": {
        "layer": [
         {
          "encoding": {
           "x": {
            "field": "mass_mean",
            "scale": {
             "zero": false
            },
            "title": "Mass (kg)",
            "type": "quantitative"
           },
           "y": {
            "field": "index",
            "title": null,
            "type": "nominal"
           }
          },
          "mark": {
           "color": "grey",
           "size": 200,
           "type": "circle"
          }
         },
         {
          "encoding": {
           "x": {
            "field": "x",
            "scale": {
             "zero": false
            },
            "type": "quantitative"
           },
           "x2": {
            "field": "x2",
            "type": "quantitative"
           },
           "y": {
            "field": "index",
            "type": "nominal"
           }
          },
          "mark": {
           "color": "grey",
           "size": 3,
           "type": "rule"
          },
          "transform": [
           {
            "as": "x",
            "calculate": "datum.mass_mean - datum.mass_std"
           },
           {
            "as": "x2",
            "calculate": "datum.mass_mean + datum.mass_std"
           }
          ]
         }
        ]
       }
      },
      "image/png": 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",
      "text/plain": [
       "<VegaLite 2 object>\n",
       "\n",
       "If you see this message, it means the renderer has not been properly enabled\n",
       "for the frontend that you are using. For more information, see\n",
       "https://altair-viz.github.io/user_guide/troubleshooting.html\n"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mass_p = p.encode(\n",
    "    alt.X(\"mass_mean\", title=\"Mass (kg)\", scale=alt.Scale(zero=False))\n",
    ")\n",
    "mass_s = s.transform_calculate(\n",
    "    x=\"datum.mass_mean - datum.mass_std\", x2=\"datum.mass_mean + datum.mass_std\"\n",
    ")\n",
    "\n",
    "mass = (mass_p + mass_s).facet(data=d)\n",
    "mass"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "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>index</th>\n",
       "      <th>variable</th>\n",
       "      <th>value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>P-3</td>\n",
       "      <td>female</td>\n",
       "      <td>59.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>DM-14</td>\n",
       "      <td>female</td>\n",
       "      <td>14.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Mcdonald</td>\n",
       "      <td>female</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Shwartz</td>\n",
       "      <td>female</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Boettcher</td>\n",
       "      <td>female</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>P-3</td>\n",
       "      <td>male</td>\n",
       "      <td>106.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>DM-14</td>\n",
       "      <td>male</td>\n",
       "      <td>24.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Mcdonald</td>\n",
       "      <td>male</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Shwartz</td>\n",
       "      <td>male</td>\n",
       "      <td>13.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Boettcher</td>\n",
       "      <td>male</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       index variable  value\n",
       "0        P-3   female   59.0\n",
       "1      DM-14   female   14.0\n",
       "2   Mcdonald   female    0.0\n",
       "3    Shwartz   female    0.0\n",
       "4  Boettcher   female    6.0\n",
       "5        P-3     male  106.0\n",
       "6      DM-14     male   24.0\n",
       "7   Mcdonald     male   10.0\n",
       "8    Shwartz     male   13.0\n",
       "9  Boettcher     male    9.0"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sex = d.melt(id_vars='index', value_vars=['female', 'male'])\n",
    "\n",
    "sex"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.vegalite.v2+json": {
       "$schema": "https://vega.github.io/schema/vega-lite/v2.6.0.json",
       "config": {
        "view": {
         "height": 300,
         "width": 400
        }
       },
       "data": {
        "name": "data-cfafce5795134a635cab5674aa1fa3e4"
       },
       "datasets": {
        "data-cfafce5795134a635cab5674aa1fa3e4": [
         {
          "index": "P-3",
          "value": 59,
          "variable": "female"
         },
         {
          "index": "DM-14",
          "value": 14,
          "variable": "female"
         },
         {
          "index": "Mcdonald",
          "value": 0,
          "variable": "female"
         },
         {
          "index": "Shwartz",
          "value": 0,
          "variable": "female"
         },
         {
          "index": "Boettcher",
          "value": 6,
          "variable": "female"
         },
         {
          "index": "P-3",
          "value": 106,
          "variable": "male"
         },
         {
          "index": "DM-14",
          "value": 24,
          "variable": "male"
         },
         {
          "index": "Mcdonald",
          "value": 10,
          "variable": "male"
         },
         {
          "index": "Shwartz",
          "value": 13,
          "variable": "male"
         },
         {
          "index": "Boettcher",
          "value": 9,
          "variable": "male"
         }
        ]
       },
       "encoding": {
        "color": {
         "field": "variable",
         "scale": {
          "domain": [
           "female",
           "male"
          ],
          "range": [
           "#e45756",
           "#4c78a8"
          ]
         },
         "type": "nominal"
        },
        "x": {
         "field": "value",
         "title": "n",
         "type": "quantitative"
        },
        "y": {
         "field": "index",
         "title": null,
         "type": "nominal"
        }
       },
       "mark": "bar"
      },
      "image/png": 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",
      "text/plain": [
       "<VegaLite 2 object>\n",
       "\n",
       "If you see this message, it means the renderer has not been properly enabled\n",
       "for the frontend that you are using. For more information, see\n",
       "https://altair-viz.github.io/user_guide/troubleshooting.html\n"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "n = d.melt(id_vars=\"index\", value_vars=[\"female\", \"male\"])\n",
    "\n",
    "participant = (\n",
    "    alt.Chart(n)\n",
    "    .mark_bar()\n",
    "    .encode(\n",
    "        alt.Y(\"index\", title=None),\n",
    "        alt.X(\"value\", title=\"n\"),\n",
    "        alt.Color(\n",
    "            \"variable\",\n",
    "            scale=alt.Scale(domain=[\"female\", \"male\"], range=[\"#e45756\", \"#4c78a8\"]),\n",
    "        ),\n",
    "    )\n",
    ")\n",
    "\n",
    "participant"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.vegalite.v2+json": {
       "$schema": "https://vega.github.io/schema/vega-lite/v2.6.0.json",
       "config": {
        "view": {
         "height": 300,
         "width": 400
        }
       },
       "datasets": {
        "data-b7a2ede0781e9cc99e03d91c8773bde9": [
         {
          "age_mean": 25.6697247706422,
          "age_std": 6.728952158082983,
          "female": 59,
          "height_mean": 174.30708661417322,
          "height_std": 9.40654264963608,
          "index": "P-3",
          "male": 106,
          "mass_mean": 71.2803937007874,
          "mass_std": 12.548017069778476,
          "n": 165
         },
         {
          "age_mean": 23.5,
          "age_std": 2.5,
          "female": 14,
          "height_mean": 180.5,
          "height_std": 10,
          "index": "DM-14",
          "male": 24,
          "mass_mean": 72.25,
          "mass_std": 11.65,
          "n": 38
         },
         {
          "age_mean": 23.6,
          "age_std": 3.4,
          "female": 0,
          "height_mean": 179,
          "height_std": 4.8,
          "index": "Mcdonald",
          "male": 10,
          "mass_mean": 79.4,
          "mass_std": 12.6,
          "n": 10
         },
         {
          "age_mean": 25.3,
          "age_std": 3.7,
          "female": 0,
          "height_mean": 180,
          "height_std": 5,
          "index": "Shwartz",
          "male": 13,
          "mass_mean": 76.2,
          "mass_std": 11.5,
          "n": 13
         },
         {
          "age_mean": 28.4,
          "age_std": null,
          "female": 6,
          "height_mean": null,
          "height_std": null,
          "index": "Boettcher",
          "male": 9,
          "mass_mean": null,
          "mass_std": null,
          "n": 15
         }
        ],
        "data-cfafce5795134a635cab5674aa1fa3e4": [
         {
          "index": "P-3",
          "value": 59,
          "variable": "female"
         },
         {
          "index": "DM-14",
          "value": 14,
          "variable": "female"
         },
         {
          "index": "Mcdonald",
          "value": 0,
          "variable": "female"
         },
         {
          "index": "Shwartz",
          "value": 0,
          "variable": "female"
         },
         {
          "index": "Boettcher",
          "value": 6,
          "variable": "female"
         },
         {
          "index": "P-3",
          "value": 106,
          "variable": "male"
         },
         {
          "index": "DM-14",
          "value": 24,
          "variable": "male"
         },
         {
          "index": "Mcdonald",
          "value": 10,
          "variable": "male"
         },
         {
          "index": "Shwartz",
          "value": 13,
          "variable": "male"
         },
         {
          "index": "Boettcher",
          "value": 9,
          "variable": "male"
         }
        ]
       },
       "vconcat": [
        {
         "hconcat": [
          {
           "data": {
            "name": "data-b7a2ede0781e9cc99e03d91c8773bde9"
           },
           "facet": {},
           "spec": {
            "layer": [
             {
              "encoding": {
               "x": {
                "field": "age_mean",
                "title": "Age (year)",
                "type": "quantitative"
               },
               "y": {
                "field": "index",
                "title": null,
                "type": "nominal"
               }
              },
              "mark": {
               "color": "grey",
               "size": 200,
               "type": "circle"
              }
             },
             {
              "encoding": {
               "x": {
                "field": "x",
                "scale": {
                 "zero": false
                },
                "type": "quantitative"
               },
               "x2": {
                "field": "x2",
                "type": "quantitative"
               },
               "y": {
                "field": "index",
                "type": "nominal"
               }
              },
              "mark": {
               "color": "grey",
               "size": 3,
               "type": "rule"
              },
              "transform": [
               {
                "as": "x",
                "calculate": "datum.age_mean - datum.age_std"
               },
               {
                "as": "x2",
                "calculate": "datum.age_mean + datum.age_std"
               }
              ]
             }
            ]
           }
          },
          {
           "data": {
            "name": "data-cfafce5795134a635cab5674aa1fa3e4"
           },
           "encoding": {
            "color": {
             "field": "variable",
             "scale": {
              "domain": [
               "female",
               "male"
              ],
              "range": [
               "#e45756",
               "#4c78a8"
              ]
             },
             "type": "nominal"
            },
            "x": {
             "field": "value",
             "title": "n",
             "type": "quantitative"
            },
            "y": {
             "field": "index",
             "title": null,
             "type": "nominal"
            }
           },
           "mark": "bar"
          }
         ]
        },
        {
         "hconcat": [
          {
           "data": {
            "name": "data-b7a2ede0781e9cc99e03d91c8773bde9"
           },
           "facet": {},
           "spec": {
            "layer": [
             {
              "encoding": {
               "x": {
                "field": "mass_mean",
                "scale": {
                 "zero": false
                },
                "title": "Mass (kg)",
                "type": "quantitative"
               },
               "y": {
                "field": "index",
                "title": null,
                "type": "nominal"
               }
              },
              "mark": {
               "color": "grey",
               "size": 200,
               "type": "circle"
              }
             },
             {
              "encoding": {
               "x": {
                "field": "x",
                "scale": {
                 "zero": false
                },
                "type": "quantitative"
               },
               "x2": {
                "field": "x2",
                "type": "quantitative"
               },
               "y": {
                "field": "index",
                "type": "nominal"
               }
              },
              "mark": {
               "color": "grey",
               "size": 3,
               "type": "rule"
              },
              "transform": [
               {
                "as": "x",
                "calculate": "datum.mass_mean - datum.mass_std"
               },
               {
                "as": "x2",
                "calculate": "datum.mass_mean + datum.mass_std"
               }
              ]
             }
            ]
           }
          },
          {
           "data": {
            "name": "data-b7a2ede0781e9cc99e03d91c8773bde9"
           },
           "facet": {},
           "spec": {
            "layer": [
             {
              "encoding": {
               "x": {
                "field": "height_mean",
                "title": "Height (cm)",
                "type": "quantitative"
               },
               "y": {
                "field": "index",
                "title": null,
                "type": "nominal"
               }
              },
              "mark": {
               "color": "grey",
               "size": 200,
               "type": "circle"
              }
             },
             {
              "encoding": {
               "x": {
                "field": "x",
                "scale": {
                 "zero": false
                },
                "type": "quantitative"
               },
               "x2": {
                "field": "x2",
                "type": "quantitative"
               },
               "y": {
                "field": "index",
                "type": "nominal"
               }
              },
              "mark": {
               "color": "grey",
               "size": 3,
               "type": "rule"
              },
              "transform": [
               {
                "as": "x",
                "calculate": "datum.height_mean - datum.height_std"
               },
               {
                "as": "x2",
                "calculate": "datum.height_mean + datum.height_std"
               }
              ]
             }
            ]
           }
          }
         ]
        }
       ]
      },
      "image/png": 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",
      "text/plain": [
       "<VegaLite 2 object>\n",
       "\n",
       "If you see this message, it means the renderer has not been properly enabled\n",
       "for the frontend that you are using. For more information, see\n",
       "https://altair-viz.github.io/user_guide/troubleshooting.html\n"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(age | participant) & (mass | height)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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