mesothelioma.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Mesothelioma data visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import io\n",
    "import pathlib\n",
    "import re\n",
    "\n",
    "import geopandas\n",
    "import geoplot\n",
    "import matplotlib\n",
    "import pandas"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Read United States state geometry data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "try:\n",
    "    from quilt.data.ResidentMario import geoplot_data\n",
    "except ModuleNotFoundError:\n",
    "    # Install data from https://quiltdata.com/package/ResidentMario/geoplot_data\n",
    "    import quilt\n",
    "    quilt.install(\n",
    "        package='ResidentMario/geoplot_data',\n",
    "        hash='1f5c34c8e6afdaef0b73b1e3681855fa91b1bdd677b98550b0ce0402a344026f',\n",
    "    )\n",
    "    from quilt.data.ResidentMario import geoplot_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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>id</th>\n",
       "      <th>adm1_code</th>\n",
       "      <th>geometry</th>\n",
       "      <th>State</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>USA-3514</td>\n",
       "      <td>POLYGON ((-89.59940899999999 48.010274, -89.48...</td>\n",
       "      <td>Minnesota</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>USA-3515</td>\n",
       "      <td>POLYGON ((-111.194189 44.561156, -111.291548 4...</td>\n",
       "      <td>Montana</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  id adm1_code                                           geometry      State\n",
       "0  0  USA-3514  POLYGON ((-89.59940899999999 48.010274, -89.48...  Minnesota\n",
       "1  1  USA-3515  POLYGON ((-111.194189 44.561156, -111.291548 4...    Montana"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "contiguous_usa = geopandas.read_file(geoplot_data.contiguous_usa())\n",
    "# From https://residentmario.github.io/geoplot/examples/nyc-parking-tickets.html\n",
    "contiguous_usa['State'] = [\n",
    "    'Minnesota', 'Montana', 'North Dakota', 'Idaho', 'Washington', 'Arizona',\n",
    "    'California', 'Colorado', 'Nevada', 'New Mexico', 'Oregon', 'Utah', 'Wyoming',\n",
    "    'Arkansas', 'Iowa', 'Kansas', 'Missouri', 'Nebraska', 'Oklahoma', 'South Dakota',\n",
    "    'Louisiana', 'Texas', 'Connecticut', 'Massachusetts', 'New Hampshire',\n",
    "    'Rhode Island', 'Vermont', 'Alabama', 'Florida', 'Georgia', 'Mississippi',\n",
    "    'South Carolina', 'Illinois', 'Indiana', 'Kentucky', 'North Carolina', 'Ohio',\n",
    "    'Tennessee', 'Virginia', 'Wisconsin', 'West Virginia', 'Delaware', 'District of Columbia',\n",
    "    'Maryland', 'New Jersey', 'New York', 'Pennsylvania', 'Maine', 'Michigan',\n",
    "]\n",
    "\n",
    "contiguous_usa.head(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Read CDC Wonder mortality data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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>Notes</th>\n",
       "      <th>State</th>\n",
       "      <th>State Code</th>\n",
       "      <th>Deaths</th>\n",
       "      <th>Population</th>\n",
       "      <th>Age-Adjusted Rate</th>\n",
       "      <th>Age-Adjusted Rate Lower 95% Confidence Interval</th>\n",
       "      <th>Age-Adjusted Rate Upper 95% Confidence Interval</th>\n",
       "      <th>Age-Adjusted Rate Standard Error</th>\n",
       "      <th>Crude Rate</th>\n",
       "      <th>Crude Rate Lower 95% Confidence Interval</th>\n",
       "      <th>Crude Rate Upper 95% Confidence Interval</th>\n",
       "      <th>Crude Rate Standard Error</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Wisconsin</td>\n",
       "      <td>55</td>\n",
       "      <td>1013</td>\n",
       "      <td>89226178</td>\n",
       "      <td>1.04022</td>\n",
       "      <td>0.97677</td>\n",
       "      <td>1.10678</td>\n",
       "      <td>0.03288</td>\n",
       "      <td>1.13532</td>\n",
       "      <td>1.06647</td>\n",
       "      <td>1.20744</td>\n",
       "      <td>0.03567</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Wyoming</td>\n",
       "      <td>56</td>\n",
       "      <td>102</td>\n",
       "      <td>8547706</td>\n",
       "      <td>1.16936</td>\n",
       "      <td>0.95122</td>\n",
       "      <td>1.42270</td>\n",
       "      <td>0.11706</td>\n",
       "      <td>1.19330</td>\n",
       "      <td>0.97299</td>\n",
       "      <td>1.44859</td>\n",
       "      <td>0.11815</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>Total</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>40096</td>\n",
       "      <td>4793002373</td>\n",
       "      <td>0.80698</td>\n",
       "      <td>0.79906</td>\n",
       "      <td>0.81495</td>\n",
       "      <td>0.00405</td>\n",
       "      <td>0.83655</td>\n",
       "      <td>0.82838</td>\n",
       "      <td>0.84478</td>\n",
       "      <td>0.00418</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Notes      State State Code  Deaths  Population  Age-Adjusted Rate  \\\n",
       "49    NaN  Wisconsin         55    1013    89226178            1.04022   \n",
       "50    NaN    Wyoming         56     102     8547706            1.16936   \n",
       "51  Total        NaN        NaN   40096  4793002373            0.80698   \n",
       "\n",
       "    Age-Adjusted Rate Lower 95% Confidence Interval  \\\n",
       "49                                          0.97677   \n",
       "50                                          0.95122   \n",
       "51                                          0.79906   \n",
       "\n",
       "    Age-Adjusted Rate Upper 95% Confidence Interval  \\\n",
       "49                                          1.10678   \n",
       "50                                          1.42270   \n",
       "51                                          0.81495   \n",
       "\n",
       "    Age-Adjusted Rate Standard Error  Crude Rate  \\\n",
       "49                           0.03288     1.13532   \n",
       "50                           0.11706     1.19330   \n",
       "51                           0.00405     0.83655   \n",
       "\n",
       "    Crude Rate Lower 95% Confidence Interval  \\\n",
       "49                                   1.06647   \n",
       "50                                   0.97299   \n",
       "51                                   0.82838   \n",
       "\n",
       "    Crude Rate Upper 95% Confidence Interval  Crude Rate Standard Error  \n",
       "49                                   1.20744                    0.03567  \n",
       "50                                   1.44859                    0.11815  \n",
       "51                                   0.84478                    0.00418  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "path = pathlib.Path('data/mortality.txt')\n",
    "tsv, *docs = re.split(r'^\"---\"$', path.read_text(), flags=re.MULTILINE)\n",
    "mortality_df = pandas.read_table(io.StringIO(tsv), dtype={'State Code': str})\n",
    "mortality_df.tail(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe5588880b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# https://residentmario.github.io/geoplot/choropleth.html\n",
    "plot = geoplot.choropleth(\n",
    "    contiguous_usa.merge(mortality_df),\n",
    "    hue='Age-Adjusted Rate',\n",
    "    projection=geoplot.crs.AlbersEqualArea(),\n",
    "    cmap='viridis',\n",
    "    linewidth=0.5,\n",
    "    edgecolor='white',\n",
    "    k=None,\n",
    "    legend=True,\n",
    "    figsize=(7, 3)\n",
    ")\n",
    "title = plot.set_title('Mesothelioma deaths per 100,000 people\\n(per year from 1999–2014, age-adjusted)')\n",
    "\n",
    "matplotlib.pyplot.savefig('figure/mortality.svg', bbox_inches='tight', pad_inches=0.1, transparent=True)\n",
    "matplotlib.pyplot.savefig('figure/mortality.png', bbox_inches='tight', pad_inches=0.1, transparent=True, dpi=300)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python [conda env:mesothelioma]",
   "language": "python",
   "name": "conda-env-mesothelioma-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}