drcjar/HospitalControls.ipynb

## HospitalControls.ipynb
{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# quick look at the relationship between socio-economic class and hospital vs community control selection using data from two IPF case-control studies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import xlrd\n",
    "import matplotlib as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_excel('cases_occupation.xlsx')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "df1 = pd.read_excel('controls_occupation.xlsx')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df[df['Gender'] == 'Male']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "df1 = df1[df1['Gender'] == 'Male']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "223"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "196"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "ssec_lookup = pd.read_excel('nssec/nssecderivationtablessoc90.xls') "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "ssec_lookup  = dict(zip(ssec_lookup.soc90, ssec_lookup.ssec))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "nssec_cases = df.iloc[:,1:11].applymap(lambda x: ssec_lookup.get(x)).median(axis=1).astype(int)\n",
    "nssec_controls = df1.iloc[:,1:11].applymap(lambda x: ssec_lookup.get(x)).median(axis=1).astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    0.094170\n",
       "2    0.089686\n",
       "3    0.201794\n",
       "4    0.107623\n",
       "5    0.130045\n",
       "6    0.188341\n",
       "7    0.188341\n",
       "dtype: float64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nssec_cases.value_counts(sort=False, normalize=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    0.239796\n",
       "2    0.234694\n",
       "3    0.107143\n",
       "4    0.127551\n",
       "5    0.122449\n",
       "6    0.122449\n",
       "7    0.045918\n",
       "dtype: float64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nssec_controls.value_counts(sort=False, normalize=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "a = pd.DataFrame(nssec_cases.value_counts(sort=False, normalize=True))\n",
    "b = pd.DataFrame(nssec_controls.value_counts(sort=False, normalize=True))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "c = pd.concat([a,b], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "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>cases</th>\n",
       "      <th>controls</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.094170</td>\n",
       "      <td>0.239796</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.089686</td>\n",
       "      <td>0.234694</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.201794</td>\n",
       "      <td>0.107143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.107623</td>\n",
       "      <td>0.127551</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.130045</td>\n",
       "      <td>0.122449</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.188341</td>\n",
       "      <td>0.122449</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.188341</td>\n",
       "      <td>0.045918</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      cases  controls\n",
       "1  0.094170  0.239796\n",
       "2  0.089686  0.234694\n",
       "3  0.201794  0.107143\n",
       "4  0.107623  0.127551\n",
       "5  0.130045  0.122449\n",
       "6  0.188341  0.122449\n",
       "7  0.188341  0.045918"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c.columns = ['cases', 'controls']\n",
    "c"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "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>cases</th>\n",
       "      <th>controls</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>9.417040</td>\n",
       "      <td>23.979592</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8.968610</td>\n",
       "      <td>23.469388</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>20.179372</td>\n",
       "      <td>10.714286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>10.762332</td>\n",
       "      <td>12.755102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>13.004484</td>\n",
       "      <td>12.244898</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>18.834081</td>\n",
       "      <td>12.244898</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>18.834081</td>\n",
       "      <td>4.591837</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       cases   controls\n",
       "1   9.417040  23.979592\n",
       "2   8.968610  23.469388\n",
       "3  20.179372  10.714286\n",
       "4  10.762332  12.755102\n",
       "5  13.004484  12.244898\n",
       "6  18.834081  12.244898\n",
       "7  18.834081   4.591837"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c = c[['cases', 'controls']]*100\n",
    "c"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "100.0"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c['controls'].sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Percentage')"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = c.plot(kind='bar', title ='Percentage of male participants in each social class for cases and controls\\n 223 cases, 196 controls, Navaratnam 2014 \\nhttps://thorax.bmj.com/content/69/3/207.full')\n",
    "ax.set_xlabel(\"Social class\")\n",
    "ax.set_ylabel(\"Percentage\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('flat_dataframe.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.median_ssec = df.median_ssec.astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "d = df.groupby('case').median_ssec.value_counts(sort=False, normalize=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "d = pd.DataFrame(d).unstack().transpose()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "d.columns = ['controls','cases']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "d.index = [1,2,3,4,5,6,7]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "d = d[['controls','cases']] * 100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "d = d[['cases','controls']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0, 25)"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = d.plot(kind='bar', title ='Percentage of participants in each social class for cases and controls\\n 494 cases, 466 controls, IPFJES 2020')\n",
    "ax.set_xlabel(\"Social class\")\n",
    "ax.set_ylabel(\"Percentage\")\n",
    "ax.set_ylim(0,25)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "100.0"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d['cases'].sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
No results found