ipfjes-analysis-centre-stats

ipfjes-analysis-centre-stats.ipynb

{ "cells": [  {   "cell_type": "code",   "execution_count": 1,   "metadata": {    "collapsed": true   },   "outputs": [],   "source": [    "import pandas as pd\n",    "import psycopg2 as pg\n",    "import pandas.io.sql as psql\n",    "import datetime\n",    "%matplotlib inline"   ]  },  {   "cell_type": "code",   "execution_count": 2,   "metadata": {    "collapsed": true   },   "outputs": [],   "source": [    "# todo automate all inc db prep bit and https://stackoverflow.com/questions/33181846/programmatically-convert-pandas-dataframe-to-markdown-table"   ]  },  {   "cell_type": "code",   "execution_count": 3,   "metadata": {    "collapsed": false   },   "outputs": [],   "source": [    "conn = pg.connect(\"dbname='carlplaying' user='drcjar' password='drcjar_is_not_fake'\")"   ]  },  {   "cell_type": "code",   "execution_count": 4,   "metadata": {    "collapsed": false   },   "outputs": [],   "source": [    "df = psql.read_sql(\"SELECT * FROM ipfjes_demographics\", conn)"   ]  },  {   "cell_type": "code",   "execution_count": 5,   "metadata": {    "collapsed": false   },   "outputs": [],   "source": [    "t = df.updated.max() # date of most recent record in db"   ]  },  {   "cell_type": "code",   "execution_count": 6,   "metadata": {    "collapsed": false   },   "outputs": [],   "source": [    "lookup = pd.read_csv('https://raw.githubusercontent.com/drcjar/ipfjes/master/docs/ipfjes-centre-ids.csv')\n",    "centre_stats = df[~df.hospital_number.str.upper().str.contains('TEST')].hospital_number.str[:2].value_counts().reset_index()\n",    "centre_stats.columns = ['Centre ID', 'Participants Recruited']\n",    "centre_stats['Centre ID'] = centre_stats['Centre ID'].astype(int)\n",    "centre_stats = pd.merge(centre_stats, lookup, on='Centre ID')\n",    "centre_stats = centre_stats[['Principle Investigator', 'Organisation', 'Participants Recruited']]"   ]  },  {   "cell_type": "code",   "execution_count": 7,   "metadata": {    "collapsed": false,    "scrolled": true   },   "outputs": [    {     "name": "stdout",     "output_type": "stream",     "text": [      "centre stats as of 30-10-17\n",      "105 enrolled in 152 days (5/week) \n",      "815 in 578 days to go (10/week)\n"     ]    },    {     "data": {      "text/html": [       "<div>\n",       "<table border=\"1\" class=\"dataframe\">\n",       "  <thead>\n",       "    <tr style=\"text-align: right;\">\n",       "      <th></th>\n",       "      <th>Principle Investigator</th>\n",       "      <th>Organisation</th>\n",       "      <th>Participants Recruited</th>\n",       "    </tr>\n",       "  </thead>\n",       "  <tbody>\n",       "    <tr>\n",       "      <th>0</th>\n",       "      <td>Lisa Spencer</td>\n",       "      <td>Aintree University Hospitals NHS Foundation Trust</td>\n",       "      <td>33</td>\n",       "    </tr>\n",       "    <tr>\n",       "      <th>1</th>\n",       "      <td>Gareth Walters</td>\n",       "      <td>Heart of England NHS Foundation Trust</td>\n",       "      <td>17</td>\n",       "    </tr>\n",       "    <tr>\n",       "      <th>2</th>\n",       "      <td>Nazia Chaudhuri</td>\n",       "      <td>University Hospital of South Manchester</td>\n",       "      <td>16</td>\n",       "    </tr>\n",       "    <tr>\n",       "      <th>3</th>\n",       "      <td>Kim Harrison</td>\n",       "      <td>Morriston Hospital</td>\n",       "      <td>8</td>\n",       "    </tr>\n",       "    <tr>\n",       "      <th>4</th>\n",       "      <td>Huzaifa Adamali</td>\n",       "      <td>North Bristol NHS Trust</td>\n",       "      <td>8</td>\n",       "    </tr>\n",       "    <tr>\n",       "      <th>5</th>\n",       "      <td>Michael Gibbons</td>\n",       "      <td>Royal Devon and Exeter NHS Foundation Trust</td>\n",       "      <td>5</td>\n",       "    </tr>\n",       "    <tr>\n",       "      <th>6</th>\n",       "      <td>Gauri Saini</td>\n",       "      <td>Nottingham University Hospitals NHS Trust</td>\n",       "      <td>5</td>\n",       "    </tr>\n",       "    <tr>\n",       "      <th>7</th>\n",       "      <td>Carl Reynolds</td>\n",       "      <td>Imperial College Healthcare NHS Trust</td>\n",       "      <td>5</td>\n",       "    </tr>\n",       "    <tr>\n",       "      <th>8</th>\n",       "      <td>Owen Dempsey</td>\n",       "      <td>Aberdeen Royal Infirmary</td>\n",       "      <td>3</td>\n",       "    </tr>\n",       "    <tr>\n",       "      <th>9</th>\n",       "      <td>Nik Hirani</td>\n",       "      <td>Royal Infirmary of Edinburgh</td>\n",       "      <td>3</td>\n",       "    </tr>\n",       "    <tr>\n",       "      <th>10</th>\n",       "      <td>Sophie Fletcher</td>\n",       "      <td>Southampton University Hospitals NHS Trust</td>\n",       "      <td>2</td>\n",       "    </tr>\n",       "  </tbody>\n",       "</table>\n",       "</div>"      ],      "text/plain": [       "   Principle Investigator                                       Organisation  \\\n",       "0            Lisa Spencer  Aintree University Hospitals NHS Foundation Trust   \n",       "1          Gareth Walters              Heart of England NHS Foundation Trust   \n",       "2         Nazia Chaudhuri            University Hospital of South Manchester   \n",       "3            Kim Harrison                                 Morriston Hospital   \n",       "4         Huzaifa Adamali                            North Bristol NHS Trust   \n",       "5         Michael Gibbons        Royal Devon and Exeter NHS Foundation Trust   \n",       "6             Gauri Saini          Nottingham University Hospitals NHS Trust   \n",       "7           Carl Reynolds              Imperial College Healthcare NHS Trust   \n",       "8            Owen Dempsey                           Aberdeen Royal Infirmary   \n",       "9              Nik Hirani                       Royal Infirmary of Edinburgh   \n",       "10        Sophie Fletcher         Southampton University Hospitals NHS Trust   \n",       "\n",       "    Participants Recruited  \n",       "0                       33  \n",       "1                       17  \n",       "2                       16  \n",       "3                        8  \n",       "4                        8  \n",       "5                        5  \n",       "6                        5  \n",       "7                        5  \n",       "8                        3  \n",       "9                        3  \n",       "10                       2  "      ]     },     "execution_count": 7,     "metadata": {},     "output_type": "execute_result"    }   ],   "source": [    "start_date = datetime.date(2017, 6, 1)\n",    "end_date = datetime.date(2019, 6, 1)\n",    "elapsed = (datetime.date.today() - start_date).days\n",    "remaining = (end_date - datetime.date.today()).days\n",    "\n",    "print('centre stats as of {}'.format(t.strftime('%d-%m-%y')))\n",    "recruited = centre_stats['Participants Recruited'].sum()\n",    "print('{} enrolled in {} days ({:.0f}/week) \\n{} in {} days to go ({:.0f}/week)'.format(recruited, elapsed, (recruited/elapsed*7),\n",    "                                                           (920 - recruited), remaining, (920 - recruited)/remaining*7))\n",    "centre_stats"   ]  },  {   "cell_type": "code",   "execution_count": 8,   "metadata": {    "collapsed": false   },   "outputs": [],   "source": [    "centre_stats.to_csv('ipfjes-recruitment-{}.csv'.format(t.strftime('%d-%m-%y')), index=False)"   ]  },  {   "cell_type": "code",   "execution_count": 16,   "metadata": {    "collapsed": false   },   "outputs": [],   "source": [    "df.updated = pd.to_datetime(df.updated, utc=True)"   ]  },  {   "cell_type": "code",   "execution_count": 17,   "metadata": {    "collapsed": false   },   "outputs": [],   "source": [    "df.index = df.updated"   ]  },  {   "cell_type": "code",   "execution_count": 18,   "metadata": {    "collapsed": false   },   "outputs": [    {     "data": {      "text/plain": [       "<matplotlib.axes._subplots.AxesSubplot at 0x7fb26bfdb390>"      ]     },     "execution_count": 18,     "metadata": {},     "output_type": "execute_result"    },    {     "data": {      "image/png": 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SAwx8SZIaYOBLktQAA1+SpAYY+JIkNcDAlySpAfsMXcBq2759O3Nzc0OXsSKHHHII69at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\nPVRtY2XP+ze5O/l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     "text/plain": [       "<matplotlib.figure.Figure at 0x7fb24cedd550>"      ]     },     "metadata": {},     "output_type": "display_data"    }   ],   "source": [    "df.resample('M').hospital_number.count().plot(kind='bar')"   ]  },  {   "cell_type": "code",   "execution_count": null,   "metadata": {    "collapsed": true   },   "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": "3.5.2"  } }, "nbformat": 4, "nbformat_minor": 2}