notebooks/04-Bar_Charts.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Bar Charts\n",
    "\n",
    "[Matplotlib Tutorial#Bar Plots](http://www.labri.fr/perso/nrougier/teaching/matplotlib/#bar-plots)\n",
    "\n",
    "In Vega-lite 2, it is not possible to build a chart in which two positive variables are plotted as bars, with the bars going in opposite directions. To do this, one of the variables needs to be scaled by -1."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Imports\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import altair as alt\n",
    "import altair.vega.v3 as vega\n",
    "\n",
    "# For inspecting Altair/Vega structures\n",
    "import json\n",
    "\n",
    "import copy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
       "    .dataframe thead th {\n",
       "        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>variable</th>\n",
       "      <th>value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>y1</td>\n",
       "      <td>0.884916</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>y1</td>\n",
       "      <td>0.754377</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>y1</td>\n",
       "      <td>0.441312</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>y1</td>\n",
       "      <td>0.736333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>y1</td>\n",
       "      <td>0.484930</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   x variable     value\n",
       "0  0       y1  0.884916\n",
       "1  1       y1  0.754377\n",
       "2  2       y1  0.441312\n",
       "3  3       y1  0.736333\n",
       "4  4       y1  0.484930"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create Data\n",
    "n = 12\n",
    "x = np.arange(n)\n",
    "y1 = (1-x/float(n)) * np.random.uniform(0.5, 1.0, n)\n",
    "# scale y2 by -1 to get the bars going in the opposite direction\n",
    "y2 = (1-x/float(n)) * np.random.uniform(0.5, 1.0, n)\n",
    "df = pd.DataFrame({'x':x, 'y1':y1, 'y2':y2}).melt('x')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.vegalite.v2+json": {
       "$schema": "https://vega.github.io/schema/vega-lite/v2.4.1.json",
       "config": {
        "view": {
         "height": 300,
         "width": 400
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       "data": {
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          "x": 0
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         {
          "value": 0.7543766624250499,
          "variable": "y1",
          "x": 1
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         {
          "value": 0.4413117097196614,
          "variable": "y1",
          "x": 2
         },
         {
          "value": 0.7363329008390103,
          "variable": "y1",
          "x": 3
         },
         {
          "value": 0.4849296603655494,
          "variable": "y1",
          "x": 4
         },
         {
          "value": 0.4750632210304177,
          "variable": "y1",
          "x": 5
         },
         {
          "value": 0.35184115413117106,
          "variable": "y1",
          "x": 6
         },
         {
          "value": 0.36654527070057186,
          "variable": "y1",
          "x": 7
         },
         {
          "value": 0.24899542068617445,
          "variable": "y1",
          "x": 8
         },
         {
          "value": 0.16352658814167426,
          "variable": "y1",
          "x": 9
         },
         {
          "value": 0.09105900281645195,
          "variable": "y1",
          "x": 10
         },
         {
          "value": 0.071078706524593,
          "variable": "y1",
          "x": 11
         },
         {
          "value": 0.5423995523722702,
          "variable": "y2",
          "x": 0
         },
         {
          "value": 0.5700327243409081,
          "variable": "y2",
          "x": 1
         },
         {
          "value": 0.5340827974138179,
          "variable": "y2",
          "x": 2
         },
         {
          "value": 0.49583814424102796,
          "variable": "y2",
          "x": 3
         },
         {
          "value": 0.34661509932660584,
          "variable": "y2",
          "x": 4
         },
         {
          "value": 0.29443623527477597,
          "variable": "y2",
          "x": 5
         },
         {
          "value": 0.4790652088593639,
          "variable": "y2",
          "x": 6
         },
         {
          "value": 0.321186894650741,
          "variable": "y2",
          "x": 7
         },
         {
          "value": 0.30709831962635464,
          "variable": "y2",
          "x": 8
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         {
          "value": 0.1944070333119324,
          "variable": "y2",
          "x": 9
         },
         {
          "value": 0.13136052663519332,
          "variable": "y2",
          "x": 10
         },
         {
          "value": 0.054314105450943416,
          "variable": "y2",
          "x": 11
         }
        ]
       },
       "layer": [
        {
         "encoding": {
          "color": {
           "field": "variable",
           "type": "nominal"
          },
          "x": {
           "field": "x",
           "type": "nominal"
          },
          "y": {
           "field": "display_value",
           "type": "quantitative"
          }
         },
         "mark": "bar",
         "transform": [
          {
           "as": "display_value",
           "calculate": "datum.variable == \"y1\" ? datum.value : datum.value * -1"
          }
         ],
         "width": 500
        },
        {
         "encoding": {
          "text": {
           "field": "label",
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          },
          "x": {
           "field": "x",
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          "y": {
           "field": "label_pos",
           "type": "quantitative"
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         "mark": {
          "align": "center",
          "baseline": "bottom",
          "type": "text"
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         "transform": [
          {
           "as": "label_pos",
           "calculate": "datum.variable === \"y1\" ? datum.value : -1 * (datum.value + 0.07)"
          },
          {
           "as": "label",
           "calculate": "format(datum.value, \".2f\")"
          }
         ],
         "width": 500
        }
       ]
      },
      "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": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bars = alt.Chart(width=500).mark_bar().encode(\n",
    "    x='x:N', \n",
    "    y='display_value:Q', \n",
    "    color=alt.Color('variable')\n",
    ").transform_calculate(\n",
    "    display_value='datum.variable == \"y1\" ? datum.value : datum.value * -1',\n",
    ")\n",
    "text = alt.Chart(width=500).mark_text(\n",
    "    align='center',\n",
    "    baseline='bottom',\n",
    ").encode(\n",
    "    x='x:N', y='label_pos:Q',\n",
    "    text='label:N'\n",
    ").transform_calculate(\n",
    "    label_pos='datum.variable === \"y1\" ? datum.value : -1 * (datum.value + 0.07)',\n",
    "    label='format(datum.value, \".2f\")'\n",
    ")\n",
    "alt.layer(bars, text, data=df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It is possible, though, to build a bar chart with two positive variables mapped in different directions in Vega 3."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.vega.v3+json": {
       "$schema": "https://vega.github.io/schema/vega/v3.0.json",
       "autosize": "pad",
       "axes": [
        {
         "encode": {
          "labels": {
           "update": {
            "align": {
             "value": "right"
            },
            "angle": {
             "value": 270
            },
            "baseline": {
             "value": "middle"
            }
           }
          }
         },
         "orient": "bottom",
         "scale": "x",
         "title": "x",
         "zindex": 1
        }
       ],
       "config": {
        "axisY": {
         "minExtent": 30
        }
       },
       "data": [
        {
         "name": "population",
         "values": [
          {
           "value": 0.8849158773457104,
           "variable": "y1",
           "x": 0
          },
          {
           "value": 0.7543766624250499,
           "variable": "y1",
           "x": 1
          },
          {
           "value": 0.4413117097196614,
           "variable": "y1",
           "x": 2
          },
          {
           "value": 0.7363329008390103,
           "variable": "y1",
           "x": 3
          },
          {
           "value": 0.4849296603655494,
           "variable": "y1",
           "x": 4
          },
          {
           "value": 0.4750632210304177,
           "variable": "y1",
           "x": 5
          },
          {
           "value": 0.35184115413117106,
           "variable": "y1",
           "x": 6
          },
          {
           "value": 0.36654527070057186,
           "variable": "y1",
           "x": 7
          },
          {
           "value": 0.24899542068617445,
           "variable": "y1",
           "x": 8
          },
          {
           "value": 0.16352658814167426,
           "variable": "y1",
           "x": 9
          },
          {
           "value": 0.09105900281645195,
           "variable": "y1",
           "x": 10
          },
          {
           "value": 0.071078706524593,
           "variable": "y1",
           "x": 11
          },
          {
           "value": 0.5423995523722702,
           "variable": "y2",
           "x": 0
          },
          {
           "value": 0.5700327243409081,
           "variable": "y2",
           "x": 1
          },
          {
           "value": 0.5340827974138179,
           "variable": "y2",
           "x": 2
          },
          {
           "value": 0.49583814424102796,
           "variable": "y2",
           "x": 3
          },
          {
           "value": 0.34661509932660584,
           "variable": "y2",
           "x": 4
          },
          {
           "value": 0.29443623527477597,
           "variable": "y2",
           "x": 5
          },
          {
           "value": 0.4790652088593639,
           "variable": "y2",
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",
      "text/plain": [
       "<Vega 3 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"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "y_domain = [0, 1.1]\n",
    "bar = {\n",
    "  \"type\": \"rect\",\n",
    "  \"style\": [\"bar\"],\n",
    "  \"encode\": {\n",
    "    \"update\": {\n",
    "      \"fill\": {\"scale\": \"color\", \"field\": \"variable\"},\n",
    "      \"x\": {\"scale\": \"x\", \"field\": \"x\"},\n",
    "      \"width\": {\"scale\": \"x\", \"band\": True},\n",
    "      \"y\": {\"scale\": \"y\", \"field\": \"value_end\"},\n",
    "      \"y2\": {\"scale\": \"y\", \"field\": \"value_start\"}\n",
    "    }\n",
    "  }\n",
    "}\n",
    "label = {\n",
    "  \"type\": \"text\",\n",
    "  \"style\": [\"text\"],\n",
    "  \"encode\": {\n",
    "    \"update\": {\n",
    "      \"align\": {\"value\": \"center\"},\n",
    "      \"baseline\": {\"value\": \"bottom\"},\n",
    "      \"fill\": {\"value\": \"black\"},\n",
    "      \"x\": {\"scale\": \"x\", \"field\": \"x\", \"band\": 0.5},\n",
    "      \"y\": {\"scale\": \"y\", \"signal\": \"datum.value\"},\n",
    "      \"text\": {\"signal\": \"format(datum.value, '.2f')\"}\n",
    "    }\n",
    "  },\n",
    "  \"zindex\": 2\n",
    "}\n",
    "y1_bar = copy.deepcopy(bar)\n",
    "y1_bar['name'] = 'y1_bar'\n",
    "y1_bar['from'] = {\"data\": \"y1\"}\n",
    "y1_label = copy.deepcopy(label)\n",
    "y1_label['name'] = 'y1_label'\n",
    "y1_label['from'] = {\"data\": \"y1\"}\n",
    "y1_label['encode']['update']['baseline']['value'] = 'bottom'\n",
    "\n",
    "y2_bar = copy.deepcopy(bar)\n",
    "y2_bar['name'] = 'y2_bar'\n",
    "y2_bar['from'] = {\"data\": \"y2\"}\n",
    "y2_label = copy.deepcopy(label)\n",
    "y2_label['name'] = 'y2_label'\n",
    "y2_label['from'] = {\"data\": \"y2\"}\n",
    "y2_label['encode']['update']['baseline']['value'] = 'top'\n",
    "\n",
    "y_group_axes = [\n",
    "    {\n",
    "      \"scale\": \"y\",\n",
    "      \"orient\": \"left\",\n",
    "      \"title\": \"value\",\n",
    "      \"tickCount\": {\"signal\": \"ceil(height/80)\"},\n",
    "      \"zindex\": 1\n",
    "    },\n",
    "    {\n",
    "      \"scale\": \"y\",\n",
    "      \"orient\": \"left\",\n",
    "      \"grid\": True,\n",
    "      \"tickCount\": {\"signal\": \"ceil(height/80)\"},\n",
    "      \"gridScale\": \"x\",\n",
    "      \"domain\": False,\n",
    "      \"labels\": False,\n",
    "      \"maxExtent\": 0,\n",
    "      \"minExtent\": 0,\n",
    "      \"ticks\": False,\n",
    "      \"zindex\": 1\n",
    "    }\n",
    "]\n",
    "\n",
    "spec = {\n",
    "  \"$schema\": \"https://vega.github.io/schema/vega/v3.0.json\",\n",
    "  \"autosize\": \"pad\",\n",
    "  \"padding\": 5,\n",
    "  \"width\": 500,\n",
    "  \"height\": 300,\n",
    "  \"style\": \"cell\",\n",
    "  \"data\": [\n",
    "    {\n",
    "      \"name\": \"population\",\n",
    "      \"values\": df.apply(lambda x: x.to_dict(), 1).tolist()\n",
    "    },\n",
    "    {\n",
    "      \"name\": \"data_1\",\n",
    "      \"source\": \"population\",\n",
    "      \"transform\": [\n",
    "        {\n",
    "          \"type\": \"formula\",\n",
    "          \"expr\": \"toNumber(datum[\\\"value\\\"])\",\n",
    "          \"as\": \"value\"\n",
    "        },\n",
    "        {\n",
    "          \"type\": \"stack\",\n",
    "          \"groupby\": [\"x\", \"variable\"],\n",
    "          \"field\": \"value\",\n",
    "          \"sort\": {\"field\": [\"variable\"], \"order\": [\"descending\"]},\n",
    "          \"as\": [\"value_start\", \"value_end\"],\n",
    "          \"offset\": \"zero\"\n",
    "        },\n",
    "        {\n",
    "          \"type\": \"filter\",\n",
    "          \"expr\": \"datum[\\\"value\\\"] !== null && !isNaN(datum[\\\"value\\\"])\"\n",
    "        }\n",
    "      ]\n",
    "    },\n",
    "    {\n",
    "      \"name\": \"y1\",\n",
    "      \"source\": \"data_1\",\n",
    "      \"transform\": [\n",
    "        {\n",
    "          \"type\": \"filter\",\n",
    "          \"expr\": \"datum.variable === 'y1'\"\n",
    "        }\n",
    "      ]\n",
    "    },\n",
    "    {\n",
    "      \"name\": \"y2\",\n",
    "      \"source\": \"data_1\",\n",
    "      \"transform\": [\n",
    "        {\n",
    "          \"type\": \"filter\",\n",
    "          \"expr\": \"datum.variable === 'y2'\"\n",
    "        }\n",
    "      ]\n",
    "    }\n",
    "  ],\n",
    "  \"marks\": [\n",
    "    {\n",
    "      \"type\": \"group\",\n",
    "      \"scales\": [\n",
    "        {\n",
    "          \"name\": \"y\",\n",
    "          \"type\": \"linear\",\n",
    "          \"domain\": y_domain,\n",
    "          \"range\": [{\"signal\": \"height/2\"}, 0],\n",
    "          \"nice\": True,\n",
    "          \"zero\": True\n",
    "        }\n",
    "      ],\n",
    "      \"marks\": [y1_bar, y1_label],\n",
    "      \"axes\": y_group_axes\n",
    "    },\n",
    "    {\n",
    "      \"type\": \"group\",\n",
    "      \"scales\": [\n",
    "        {\n",
    "          \"name\": \"y\",\n",
    "          \"type\": \"linear\",\n",
    "          \"domain\": y_domain,\n",
    "          \"range\": [{\"signal\": \"height/2\"}, {\"signal\": \"height\"}],\n",
    "          \"nice\": True,\n",
    "          \"zero\": True\n",
    "        }\n",
    "      ],\n",
    "      \"axes\": y_group_axes,\n",
    "      \"marks\": [y2_bar, y2_label]\n",
    "    }\n",
    "  ],\n",
    "  \"scales\": [\n",
    "    {\n",
    "      \"name\": \"x\",\n",
    "      \"type\": \"band\",\n",
    "      \"domain\": {\n",
    "        \"fields\": [\n",
    "          {\"data\": \"data_1\", \"field\": \"x\"}\n",
    "        ],\n",
    "        \"sort\": True\n",
    "      },\n",
    "      \"range\": [0, {\"signal\": \"width\"}],\n",
    "      \"paddingInner\": 0.1,\n",
    "      \"paddingOuter\": 0.05\n",
    "    },\n",
    "    {\n",
    "      \"name\": \"color\",\n",
    "      \"type\": \"ordinal\",\n",
    "      \"domain\": {\"data\": \"data_1\", \"field\": \"variable\", \"sort\": True},\n",
    "      \"range\": \"category\"\n",
    "    }\n",
    "  ],\n",
    "  \"axes\": [\n",
    "    {\n",
    "      \"scale\": \"x\",\n",
    "      \"orient\": \"bottom\",\n",
    "      \"title\": \"x\",\n",
    "      \"encode\": {\n",
    "        \"labels\": {\n",
    "          \"update\": {\n",
    "            \"angle\": {\"value\": 270},\n",
    "            \"align\": {\"value\": \"right\"},\n",
    "            \"baseline\": {\"value\": \"middle\"}\n",
    "          }\n",
    "        }\n",
    "      },\n",
    "      \"zindex\": 1\n",
    "    }\n",
    "  ],\n",
    "  \"legends\": [\n",
    "    {\n",
    "      \"fill\": \"color\",\n",
    "      \"title\": \"variable\",\n",
    "      \"encode\": {\"symbols\": {\"update\": {\"shape\": {\"value\": \"square\"}}}}\n",
    "    }\n",
    "  ],\n",
    "  \"config\": {\"axisY\": {\"minExtent\": 30}}\n",
    "}\n",
    "vega.vega(spec)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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