allsr.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "3950fb0a",
   "metadata": {},
   "outputs": [],
   "source": [
    "import openpyxl\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import numpy as np\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "02e5ee68",
   "metadata": {},
   "outputs": [],
   "source": [
    "from helpers import verify_worksheet, replace_nulls, sum_features_per_software, keep_top_software"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8bc8e102",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "a3432428",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 1. READ EXCEL FILE\n",
    "x_file = 'data/SRsoftwareextraction.xlsx'\n",
    "sheet_name = 'adjudicatedvalues'\n",
    "\n",
    "verify_worksheet(x_file, sheet_name)\n",
    "orig_df = pd.read_excel(x_file, sheet_name, engine='openpyxl')\n",
    "replace_nulls(orig_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "3f697605",
   "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>name</th>\n",
       "      <th>type</th>\n",
       "      <th>field</th>\n",
       "      <th>search</th>\n",
       "      <th>import</th>\n",
       "      <th>expertaddition</th>\n",
       "      <th>tiabscreen</th>\n",
       "      <th>distinctscreen</th>\n",
       "      <th>dualscreen</th>\n",
       "      <th>fulltextpdf</th>\n",
       "      <th>...</th>\n",
       "      <th>comments</th>\n",
       "      <th>functional</th>\n",
       "      <th>free</th>\n",
       "      <th>cloud</th>\n",
       "      <th>living</th>\n",
       "      <th>training</th>\n",
       "      <th>support</th>\n",
       "      <th>flexibility</th>\n",
       "      <th>publicouputs</th>\n",
       "      <th>riskofbias</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>SRDR</td>\n",
       "      <td>full process</td>\n",
       "      <td>healthcare</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>SWIFT-Active Screener</td>\n",
       "      <td>screening</td>\n",
       "      <td>multidiscipline</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>SWIFT-Review</td>\n",
       "      <td>search and extraction</td>\n",
       "      <td>multidiscipline</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>SyRF</td>\n",
       "      <td>screening and extraction</td>\n",
       "      <td>healthcare</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>SysRev</td>\n",
       "      <td>full process</td>\n",
       "      <td>multidiscipline</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 35 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                     name                      type            field  search  \\\n",
       "19                   SRDR              full process       healthcare     0.0   \n",
       "20  SWIFT-Active Screener                 screening  multidiscipline     0.0   \n",
       "21           SWIFT-Review     search and extraction  multidiscipline     0.0   \n",
       "22                   SyRF  screening and extraction       healthcare     0.0   \n",
       "23                 SysRev              full process  multidiscipline     1.0   \n",
       "\n",
       "    import  expertaddition  tiabscreen  distinctscreen  dualscreen  \\\n",
       "19     1.0             0.0         0.0             0.0         0.0   \n",
       "20     1.0             1.0         1.0             1.0         1.0   \n",
       "21     1.0             0.0         1.0             0.0         0.0   \n",
       "22     1.0             0.0         1.0             1.0         1.0   \n",
       "23     1.0             1.0         1.0             0.0         1.0   \n",
       "\n",
       "    fulltextpdf  ...  comments  functional  free  cloud  living  training  \\\n",
       "19          0.0  ...       1.0         1.0   1.0    1.0     1.0       1.0   \n",
       "20          1.0  ...       0.0         1.0   0.0    1.0     0.0       1.0   \n",
       "21          0.0  ...       0.0         1.0   1.0    0.0     0.0       1.0   \n",
       "22          0.0  ...       0.0         1.0   1.0    0.0     0.0       0.0   \n",
       "23          1.0  ...       1.0         1.0   1.0    1.0     1.0       0.0   \n",
       "\n",
       "    support  flexibility  publicouputs  riskofbias  \n",
       "19      1.0          1.0           1.0         0.0  \n",
       "20      1.0          0.0           0.0         0.0  \n",
       "21      1.0          0.0           0.0         0.0  \n",
       "22      0.0          0.0           0.0         1.0  \n",
       "23      1.0          1.0           1.0         0.0  \n",
       "\n",
       "[5 rows x 35 columns]"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Drop empty rows\n",
    "orig_df.dropna(axis=0, how= 'all', thresh=None, subset=None, inplace=True)\n",
    "orig_df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "b618477c",
   "metadata": {},
   "outputs": [],
   "source": [
    "orig_df.to_excel('data/orig_df.xlsx', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "44c6ae46",
   "metadata": {},
   "outputs": [],
   "source": [
    "# SUMMARIZE ALL DATA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "b2e9b087",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>type</th>\n",
       "      <th>field</th>\n",
       "      <th>search</th>\n",
       "      <th>import</th>\n",
       "      <th>expertaddition</th>\n",
       "      <th>tiabscreen</th>\n",
       "      <th>distinctscreen</th>\n",
       "      <th>dualscreen</th>\n",
       "      <th>fulltextpdf</th>\n",
       "      <th>...</th>\n",
       "      <th>cloud</th>\n",
       "      <th>living</th>\n",
       "      <th>training</th>\n",
       "      <th>support</th>\n",
       "      <th>flexibility</th>\n",
       "      <th>publicouputs</th>\n",
       "      <th>riskofbias</th>\n",
       "      <th>total_features</th>\n",
       "      <th>number_of_features</th>\n",
       "      <th>percent_of_features</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>SRDR</td>\n",
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       "      <td>18.0</td>\n",
       "      <td>0.56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>SWIFT-Active Screener</td>\n",
       "      <td>screening</td>\n",
       "      <td>multidiscipline</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>32</td>\n",
       "      <td>18.0</td>\n",
       "      <td>0.56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>SWIFT-Review</td>\n",
       "      <td>search and extraction</td>\n",
       "      <td>multidiscipline</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>32</td>\n",
       "      <td>11.0</td>\n",
       "      <td>0.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>SyRF</td>\n",
       "      <td>screening and extraction</td>\n",
       "      <td>healthcare</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>32</td>\n",
       "      <td>13.0</td>\n",
       "      <td>0.41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>SysRev</td>\n",
       "      <td>full process</td>\n",
       "      <td>multidiscipline</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>32</td>\n",
       "      <td>24.0</td>\n",
       "      <td>0.75</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 38 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                     name                      type            field  search  \\\n",
       "19                   SRDR              full process       healthcare     0.0   \n",
       "20  SWIFT-Active Screener                 screening  multidiscipline     0.0   \n",
       "21           SWIFT-Review     search and extraction  multidiscipline     0.0   \n",
       "22                   SyRF  screening and extraction       healthcare     0.0   \n",
       "23                 SysRev              full process  multidiscipline     1.0   \n",
       "\n",
       "    import  expertaddition  tiabscreen  distinctscreen  dualscreen  \\\n",
       "19     1.0             0.0         0.0             0.0         0.0   \n",
       "20     1.0             1.0         1.0             1.0         1.0   \n",
       "21     1.0             0.0         1.0             0.0         0.0   \n",
       "22     1.0             0.0         1.0             1.0         1.0   \n",
       "23     1.0             1.0         1.0             0.0         1.0   \n",
       "\n",
       "    fulltextpdf  ...  cloud  living  training  support  flexibility  \\\n",
       "19          0.0  ...    1.0     1.0       1.0      1.0          1.0   \n",
       "20          1.0  ...    1.0     0.0       1.0      1.0          0.0   \n",
       "21          0.0  ...    0.0     0.0       1.0      1.0          0.0   \n",
       "22          0.0  ...    0.0     0.0       0.0      0.0          0.0   \n",
       "23          1.0  ...    1.0     1.0       0.0      1.0          1.0   \n",
       "\n",
       "    publicouputs  riskofbias  total_features  number_of_features  \\\n",
       "19           1.0         0.0              32                18.0   \n",
       "20           0.0         0.0              32                18.0   \n",
       "21           0.0         0.0              32                11.0   \n",
       "22           0.0         1.0              32                13.0   \n",
       "23           1.0         0.0              32                24.0   \n",
       "\n",
       "    percent_of_features  \n",
       "19                 0.56  \n",
       "20                 0.56  \n",
       "21                 0.34  \n",
       "22                 0.41  \n",
       "23                 0.75  \n",
       "\n",
       "[5 rows x 38 columns]"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sum_df = sum_features_per_software(orig_df, 5) # Keep if there are at least 5 non-null features\n",
    "sum_df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "c365a8bc",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>type</th>\n",
       "      <th>field</th>\n",
       "      <th>search</th>\n",
       "      <th>import</th>\n",
       "      <th>expertaddition</th>\n",
       "      <th>tiabscreen</th>\n",
       "      <th>distinctscreen</th>\n",
       "      <th>dualscreen</th>\n",
       "      <th>fulltextpdf</th>\n",
       "      <th>...</th>\n",
       "      <th>cloud</th>\n",
       "      <th>living</th>\n",
       "      <th>training</th>\n",
       "      <th>support</th>\n",
       "      <th>flexibility</th>\n",
       "      <th>publicouputs</th>\n",
       "      <th>riskofbias</th>\n",
       "      <th>total_features</th>\n",
       "      <th>number_of_features</th>\n",
       "      <th>percent_of_features</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>SysRev</td>\n",
       "      <td>full process</td>\n",
       "      <td>multidiscipline</td>\n",
       "      <td>1.0</td>\n",
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       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>32</td>\n",
       "      <td>24.0</td>\n",
       "      <td>0.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Cadima</td>\n",
       "      <td>screening</td>\n",
       "      <td>multidiscipline</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>32</td>\n",
       "      <td>23.0</td>\n",
       "      <td>0.72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>JBI SUMARI</td>\n",
       "      <td>full process</td>\n",
       "      <td>multidiscipline</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>32</td>\n",
       "      <td>23.0</td>\n",
       "      <td>0.72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Covidence</td>\n",
       "      <td>full process</td>\n",
       "      <td>healthcare</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>32</td>\n",
       "      <td>23.0</td>\n",
       "      <td>0.72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>SRDB.PRO</td>\n",
       "      <td>full process</td>\n",
       "      <td>healthcare</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>32</td>\n",
       "      <td>22.0</td>\n",
       "      <td>0.69</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 38 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         name          type            field  search  import  expertaddition  \\\n",
       "3      SysRev  full process  multidiscipline     1.0     1.0             1.0   \n",
       "4      Cadima     screening  multidiscipline     0.0     1.0             0.0   \n",
       "5  JBI SUMARI  full process  multidiscipline     1.0     1.0             1.0   \n",
       "6   Covidence  full process       healthcare     0.0     1.0             1.0   \n",
       "7    SRDB.PRO  full process       healthcare     1.0     1.0             1.0   \n",
       "\n",
       "   tiabscreen  distinctscreen  dualscreen  fulltextpdf  ...  cloud  living  \\\n",
       "3         1.0             0.0         1.0          1.0  ...    1.0     1.0   \n",
       "4         1.0             1.0         1.0          1.0  ...    1.0     0.0   \n",
       "5         1.0             1.0         1.0          1.0  ...    1.0     0.0   \n",
       "6         1.0             1.0         1.0          1.0  ...    1.0     0.0   \n",
       "7         1.0             1.0         1.0          1.0  ...    0.0     0.0   \n",
       "\n",
       "   training  support  flexibility  publicouputs  riskofbias  total_features  \\\n",
       "3       0.0      1.0          1.0           1.0         0.0              32   \n",
       "4       1.0      1.0          1.0           1.0         1.0              32   \n",
       "5       1.0      1.0          1.0           1.0         1.0              32   \n",
       "6       1.0      1.0          1.0           0.0         1.0              32   \n",
       "7       1.0      1.0          1.0           0.0         1.0              32   \n",
       "\n",
       "   number_of_features  percent_of_features  \n",
       "3                24.0                 0.75  \n",
       "4                23.0                 0.72  \n",
       "5                23.0                 0.72  \n",
       "6                23.0                 0.72  \n",
       "7                22.0                 0.69  \n",
       "\n",
       "[5 rows x 38 columns]"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Keep only the top 8 softwares\n",
    "df = keep_top_software(sum_df, \"number_of_features\", 8)\n",
    "df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "80bff109",
   "metadata": {},
   "outputs": [],
   "source": [
    "name = df['name']\n",
    "percent = df['percent_of_features']\n",
    "number = df['number_of_features']\n",
    "\n",
    "captions = []"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "6883672c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(14, 10))\n",
    "ax = sns.barplot(x=name, y=percent, data=sum_df, palette=\"Blues_r\")\n",
    "ax.set_ylabel('Percent of Features', fontsize=14)\n",
    "ax.set_xlabel('SR Software', fontsize=14)\n",
    "ax.set_title('Features of SR Software', fontsize=18)\n",
    "ax.set_xticklabels(ax.get_xticklabels(), rotation=45)\n",
    "plt.show()\n",
    "\n",
    "captions.append(\"fig 1. \"+\"Percent of Features for each SR Software (with a min of 5 non-NA features)\")\n",
    "ax.get_figure().savefig('output/fig1-percent-of-features-top8.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "6651d1be",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Export original dataframe to breakup by feature class."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "cf3af72a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Write all the figure captions to a text file\n",
    "with open('output/captions.txt', 'w') as f:\n",
    "    for line in captions:\n",
    "        f.write(line)\n",
    "        f.write('\\n')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "e2ee0e50",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1368x1080 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# KEEP THE TOP 16\n",
    "# Keep only the top 8 softwares\n",
    "df = keep_top_software(sum_df, \"percent_of_features\", 16)\n",
    "df.tail()\n",
    "\n",
    "name = df['name']\n",
    "percent = df['percent_of_features']\n",
    "number = df['number_of_features']\n",
    "\n",
    "plt.figure(figsize=(19, 15))\n",
    "ax = sns.barplot(x=name, y=percent, data=sum_df, palette=\"Blues_r\")\n",
    "ax.set_ylabel('Percent of Features', fontsize=20)\n",
    "ax.set_xlabel('Systematic Review Software', fontsize=20)\n",
    "ax.set_title('Features of Systematic Review Software', fontsize=24)\n",
    "ax.set_xticklabels(ax.get_xticklabels(), rotation=45)\n",
    "plt.show()\n",
    "\n",
    "captions.append(\"fig 8. \"+\"Percent of Features for each SR Software (with a min of 5 non-NA features)\")\n",
    "figname = \"fig8\"\n",
    "ax.get_figure().savefig('output/fig8-percent-of-features-top16.png')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bdc74330",
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   "execution_count": null,
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  {
   "cell_type": "code",
   "execution_count": null,
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