collect_users/analyze_users.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "6617d56b",
   "metadata": {},
   "source": [
    "# Analysis of user collection\n",
    "\n",
    "In this notebook, the retrieved results of the user collection is analyzed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "id": "635b6d3d",
   "metadata": {},
   "outputs": [],
   "source": [
    "import glob\n",
    "import time\n",
    "import sys\n",
    "from pathlib import Path\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0d921730",
   "metadata": {},
   "source": [
    "Configure modules like pandas and plotting libraries."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "id": "3e12160d",
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.rcParams['figure.figsize'] = [12, 8]\n",
    "pd.set_option('display.min_rows', 24)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0efae96b",
   "metadata": {},
   "source": [
    "Folders and constants"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "id": "74fd74bb",
   "metadata": {},
   "outputs": [],
   "source": [
    "fp_figs = Path(\"figs\")\n",
    "\n",
    "annotated_users = Path(\"results\", \"annotated.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "49d80e8d",
   "metadata": {},
   "source": [
    "## Dataset\n",
    "In this section, output data of the various methods is collected and combined. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "id": "a189654b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1 methods/github_org_commit/results/research_org_commits.csv\n",
      "2 methods/github_search/results/github_search_repo.csv\n",
      "3 methods/github_search/results/github_search_topic.csv\n",
      "4 methods/github_search/results/github_search_users.csv\n",
      "5 methods/manual_collection/results/manual_users.csv\n"
     ]
    }
   ],
   "source": [
    "data_files = glob.glob(\"methods/*/results/*.csv\")\n",
    "\n",
    "for i, d in enumerate(data_files):\n",
    "    print(i+1, d)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "206bba3c",
   "metadata": {},
   "source": [
    "All files is loaded and combined in a single dataset. The dataset contains the variables:\n",
    "- `source` The source is the method used to find the user, e.g. paperswithcode, github_search_users, github_search_topics. The name is derived from the file name of the results file.\n",
    "- `service` The service the user profile is found, e.g. github.com, github.warwick.ac.uk, gitlab.\n",
    "- `date` The date of collection. This can be useful when updating results.\n",
    "- `user_id` The user handle of the found user. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "id": "f16f7232",
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>source</th>\n",
       "      <th>user_id</th>\n",
       "      <th>date</th>\n",
       "      <th>service</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>research_org_commits</td>\n",
       "      <td>hallerp</td>\n",
       "      <td>2023-09-06</td>\n",
       "      <td>github</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>research_org_commits</td>\n",
       "      <td>jakobchwastek</td>\n",
       "      <td>2023-09-06</td>\n",
       "      <td>github</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>research_org_commits</td>\n",
       "      <td>lenabolliger</td>\n",
       "      <td>2023-09-06</td>\n",
       "      <td>github</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>research_org_commits</td>\n",
       "      <td>LenaJaeger</td>\n",
       "      <td>2023-09-06</td>\n",
       "      <td>github</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>research_org_commits</td>\n",
       "      <td>prassepaul</td>\n",
       "      <td>2023-09-06</td>\n",
       "      <td>github</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>research_org_commits</td>\n",
       "      <td>sarahkiener</td>\n",
       "      <td>2023-09-06</td>\n",
       "      <td>github</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>research_org_commits</td>\n",
       "      <td>Shuwen27</td>\n",
       "      <td>2023-09-06</td>\n",
       "      <td>github</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>research_org_commits</td>\n",
       "      <td>SiQube</td>\n",
       "      <td>2023-09-06</td>\n",
       "      <td>github</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>research_org_commits</td>\n",
       "      <td>tschirner</td>\n",
       "      <td>2023-09-06</td>\n",
       "      <td>github</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>research_org_commits</td>\n",
       "      <td>Baschdl</td>\n",
       "      <td>2023-09-08</td>\n",
       "      <td>github</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>research_org_commits</td>\n",
       "      <td>fpottbaecker</td>\n",
       "      <td>2023-09-08</td>\n",
       "      <td>github</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>research_org_commits</td>\n",
       "      <td>kryptokommunist</td>\n",
       "      <td>2023-09-08</td>\n",
       "      <td>github</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>657</th>\n",
       "      <td>manual_users</td>\n",
       "      <td>chpfaff98</td>\n",
       "      <td>24.07.23</td>\n",
       "      <td>github.com</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>658</th>\n",
       "      <td>manual_users</td>\n",
       "      <td>dmrwal</td>\n",
       "      <td>24.07.23</td>\n",
       "      <td>github.com</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>659</th>\n",
       "      <td>manual_users</td>\n",
       "      <td>flgebhardt</td>\n",
       "      <td>24.07.23</td>\n",
       "      <td>github.com</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>660</th>\n",
       "      <td>manual_users</td>\n",
       "      <td>duyhung-nguyen</td>\n",
       "      <td>24.07.23</td>\n",
       "      <td>github.com</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>661</th>\n",
       "      <td>manual_users</td>\n",
       "      <td>martinhtrauth</td>\n",
       "      <td>24.07.23</td>\n",
       "      <td>github.com</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>662</th>\n",
       "      <td>manual_users</td>\n",
       "      <td>Hazem0803</td>\n",
       "      <td>24.07.23</td>\n",
       "      <td>github.com</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>663</th>\n",
       "      <td>manual_users</td>\n",
       "      <td>PedroSatorreMulet</td>\n",
       "      <td>24.07.23</td>\n",
       "      <td>github.com</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>664</th>\n",
       "      <td>manual_users</td>\n",
       "      <td>Nandini-ESO</td>\n",
       "      <td>24.07.23</td>\n",
       "      <td>github.com</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>665</th>\n",
       "      <td>manual_users</td>\n",
       "      <td>yuxizhang11</td>\n",
       "      <td>24.07.23</td>\n",
       "      <td>github.com</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>666</th>\n",
       "      <td>manual_users</td>\n",
       "      <td>Diego635</td>\n",
       "      <td>24.07.23</td>\n",
       "      <td>github.com</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>667</th>\n",
       "      <td>manual_users</td>\n",
       "      <td>danieljanko98</td>\n",
       "      <td>24.07.23</td>\n",
       "      <td>github.com</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>668</th>\n",
       "      <td>manual_users</td>\n",
       "      <td>MGx01</td>\n",
       "      <td>24.07.23</td>\n",
       "      <td>github.com</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>669 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                   source            user_id        date     service\n",
       "0    research_org_commits            hallerp  2023-09-06      github\n",
       "1    research_org_commits      jakobchwastek  2023-09-06      github\n",
       "2    research_org_commits       lenabolliger  2023-09-06      github\n",
       "3    research_org_commits         LenaJaeger  2023-09-06      github\n",
       "4    research_org_commits         prassepaul  2023-09-06      github\n",
       "5    research_org_commits        sarahkiener  2023-09-06      github\n",
       "6    research_org_commits           Shuwen27  2023-09-06      github\n",
       "7    research_org_commits             SiQube  2023-09-06      github\n",
       "8    research_org_commits          tschirner  2023-09-06      github\n",
       "9    research_org_commits            Baschdl  2023-09-08      github\n",
       "10   research_org_commits       fpottbaecker  2023-09-08      github\n",
       "11   research_org_commits    kryptokommunist  2023-09-08      github\n",
       "..                    ...                ...         ...         ...\n",
       "657          manual_users          chpfaff98    24.07.23  github.com\n",
       "658          manual_users             dmrwal    24.07.23  github.com\n",
       "659          manual_users         flgebhardt    24.07.23  github.com\n",
       "660          manual_users     duyhung-nguyen    24.07.23  github.com\n",
       "661          manual_users      martinhtrauth    24.07.23  github.com\n",
       "662          manual_users          Hazem0803    24.07.23  github.com\n",
       "663          manual_users  PedroSatorreMulet    24.07.23  github.com\n",
       "664          manual_users        Nandini-ESO    24.07.23  github.com\n",
       "665          manual_users        yuxizhang11    24.07.23  github.com\n",
       "666          manual_users           Diego635    24.07.23  github.com\n",
       "667          manual_users      danieljanko98    24.07.23  github.com\n",
       "668          manual_users              MGx01    24.07.23  github.com\n",
       "\n",
       "[669 rows x 4 columns]"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_user_names = pd.concat(\n",
    "    [pd.read_csv(fp) for fp in data_files], \n",
    "    axis=0, \n",
    "    keys=data_files, \n",
    "    names=[\"source\", \"row\"]\n",
    ") \\\n",
    "    .reset_index(\"source\") \\\n",
    "    .reset_index(drop=True)\n",
    "\n",
    "df_user_names[\"source\"] = df_user_names[\"source\"].apply(lambda x: Path(x).stem)\n",
    "df_user_names"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "50e86f42",
   "metadata": {},
   "source": [
    "## Findability\n",
    "\n",
    "The following results shows how many times user handles are found with each retrieval method. If a user is found with multiple methods, the findability of the user can be consider to be better. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9b7bb1a4",
   "metadata": {},
   "source": [
    "### Cross table user and method\n",
    "\n",
    "Create a cross table with service and user on one axis and the extraction method on the other axis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "id": "1613a425",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
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       "      <th>github_search_repo</th>\n",
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       "      <th rowspan=\"12\" valign=\"top\">github</th>\n",
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       "      <th>FelixDombek</th>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>KatrinSch</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>LenaJaeger</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
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       "    <tr>\n",
       "      <th>MaxOstrowski</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>Shuwen27</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
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       "      <th>SiQube</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
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       "      <th>TScheffler</th>\n",
       "      <td>0</td>\n",
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       "      <th>arne-cl</th>\n",
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       "      <td>0</td>\n",
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       "      <th>fpottbaecker</th>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>hallerp</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>jakobchwastek</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"12\" valign=\"top\">github.com</th>\n",
       "      <th>vasishthlabor</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>verdeev</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>vykimo</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>warpaint97</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>whuiskamp</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>wschwanghart</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>xarxaxdev</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>yadavhimanshu059</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>yanweiser</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>yerkesoul</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>yuxizhang11</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>zentrum-lexikographie</th>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>303 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "source                            github_search_repo  github_search_users  \\\n",
       "service    user_id                                                          \n",
       "github     Baschdl                                 0                    0   \n",
       "           FelixDombek                             0                    0   \n",
       "           KatrinSch                               0                    0   \n",
       "           LenaJaeger                              0                    0   \n",
       "           MaxOstrowski                            0                    0   \n",
       "           Shuwen27                                0                    0   \n",
       "           SiQube                                  0                    0   \n",
       "           TScheffler                              0                    0   \n",
       "           arne-cl                                 0                    0   \n",
       "           fpottbaecker                            0                    0   \n",
       "           hallerp                                 0                    0   \n",
       "           jakobchwastek                           0                    0   \n",
       "...                                              ...                  ...   \n",
       "github.com vasishthlabor                           1                    0   \n",
       "           verdeev                                 0                    1   \n",
       "           vykimo                                  1                    0   \n",
       "           warpaint97                              1                    0   \n",
       "           whuiskamp                               0                    0   \n",
       "           wschwanghart                            2                    1   \n",
       "           xarxaxdev                               0                    0   \n",
       "           yadavhimanshu059                        0                    1   \n",
       "           yanweiser                               1                    0   \n",
       "           yerkesoul                               0                    0   \n",
       "           yuxizhang11                             0                    0   \n",
       "           zentrum-lexikographie                   2                    0   \n",
       "\n",
       "source                            manual_users  research_org_commits  \n",
       "service    user_id                                                    \n",
       "github     Baschdl                           0                     1  \n",
       "           FelixDombek                       0                     1  \n",
       "           KatrinSch                         0                     1  \n",
       "           LenaJaeger                        0                     2  \n",
       "           MaxOstrowski                      0                     1  \n",
       "           Shuwen27                          0                     2  \n",
       "           SiQube                            0                     2  \n",
       "           TScheffler                        0                     1  \n",
       "           arne-cl                           0                     1  \n",
       "           fpottbaecker                      0                     1  \n",
       "           hallerp                           0                     2  \n",
       "           jakobchwastek                     0                     2  \n",
       "...                                        ...                   ...  \n",
       "github.com vasishthlabor                     1                     0  \n",
       "           verdeev                           0                     0  \n",
       "           vykimo                            1                     0  \n",
       "           warpaint97                        1                     0  \n",
       "           whuiskamp                         1                     0  \n",
       "           wschwanghart                      4                     0  \n",
       "           xarxaxdev                         1                     0  \n",
       "           yadavhimanshu059                  3                     0  \n",
       "           yanweiser                         1                     0  \n",
       "           yerkesoul                         2                     0  \n",
       "           yuxizhang11                       1                     0  \n",
       "           zentrum-lexikographie             2                     0  \n",
       "\n",
       "[303 rows x 4 columns]"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_name_method_crosstab = df_user_names \\\n",
    "    .drop(\"date\", axis=1) \\\n",
    "    .groupby([\"service\", \"user_id\", \"source\"]) \\\n",
    "    .size() \\\n",
    "    .unstack(\"source\") \\\n",
    "    .fillna(0) \\\n",
    "    .applymap(int)\n",
    "\n",
    "df_name_method_crosstab"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c1a3ad6a",
   "metadata": {},
   "source": [
    "The following example can be uncommented to find the findabilty of a specific project. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "id": "627cd832",
   "metadata": {},
   "outputs": [],
   "source": [
    "# df_name_method_crosstab.loc[('github.com', 'asreview')]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4dce9541",
   "metadata": {},
   "source": [
    "### Findability per user\n",
    "\n",
    "The following table show which users are found with most collection strategies. The count indicates the number of methods, and the relative score the number relative to the total number of collection strategies. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "id": "804c4aef",
   "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></th>\n",
       "      <th>count</th>\n",
       "      <th>relative</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>service</th>\n",
       "      <th>user_id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"22\" valign=\"top\">github.com</th>\n",
       "      <th>brechtknecht</th>\n",
       "      <td>3</td>\n",
       "      <td>0.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>annameide</th>\n",
       "      <td>3</td>\n",
       "      <td>0.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>wschwanghart</th>\n",
       "      <td>3</td>\n",
       "      <td>0.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mittelmark</th>\n",
       "      <td>3</td>\n",
       "      <td>0.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>UP-RS-ESP</th>\n",
       "      <td>3</td>\n",
       "      <td>0.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fsr-de</th>\n",
       "      <td>3</td>\n",
       "      <td>0.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bptlab</th>\n",
       "      <td>2</td>\n",
       "      <td>0.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>alyonavyshnevska</th>\n",
       "      <td>2</td>\n",
       "      <td>0.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>niklhart</th>\n",
       "      <td>2</td>\n",
       "      <td>0.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>nickpodratz</th>\n",
       "      <td>2</td>\n",
       "      <td>0.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mmrabe</th>\n",
       "      <td>2</td>\n",
       "      <td>0.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>miduku</th>\n",
       "      <td>2</td>\n",
       "      <td>0.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>baudisch</th>\n",
       "      <td>1</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dmrwal</th>\n",
       "      <td>1</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>benlowit</th>\n",
       "      <td>1</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bgailleton</th>\n",
       "      <td>1</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bglaetzer</th>\n",
       "      <td>1</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bgroenks96</th>\n",
       "      <td>1</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>blblblu</th>\n",
       "      <td>1</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>braunfuss</th>\n",
       "      <td>1</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ctiedt</th>\n",
       "      <td>1</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>github</th>\n",
       "      <th>FelixDombek</th>\n",
       "      <td>1</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">github.com</th>\n",
       "      <th>dieknolle3333</th>\n",
       "      <td>1</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>delwarhub</th>\n",
       "      <td>1</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>303 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                             count  relative\n",
       "service    user_id                          \n",
       "github.com brechtknecht          3       0.6\n",
       "           annameide             3       0.6\n",
       "           wschwanghart          3       0.6\n",
       "           mittelmark            3       0.6\n",
       "           UP-RS-ESP             3       0.6\n",
       "           fsr-de                3       0.6\n",
       "           bptlab                2       0.4\n",
       "           alyonavyshnevska      2       0.4\n",
       "           niklhart              2       0.4\n",
       "           nickpodratz           2       0.4\n",
       "           mmrabe                2       0.4\n",
       "           miduku                2       0.4\n",
       "...                            ...       ...\n",
       "           baudisch              1       0.2\n",
       "           dmrwal                1       0.2\n",
       "           benlowit              1       0.2\n",
       "           bgailleton            1       0.2\n",
       "           bglaetzer             1       0.2\n",
       "           bgroenks96            1       0.2\n",
       "           blblblu               1       0.2\n",
       "           braunfuss             1       0.2\n",
       "           ctiedt                1       0.2\n",
       "github     FelixDombek           1       0.2\n",
       "github.com dieknolle3333         1       0.2\n",
       "           delwarhub             1       0.2\n",
       "\n",
       "[303 rows x 2 columns]"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# compute the count\n",
    "df_name_findability = (df_name_method_crosstab > 0) \\\n",
    "    .astype(int) \\\n",
    "    .sum(axis=1) \\\n",
    "    .sort_values(ascending=False) \\\n",
    "    .to_frame(name=\"count\")\n",
    "\n",
    "# compute relative score\n",
    "df_name_findability[\"relative\"] = df_name_findability[\"count\"] / len(data_files)\n",
    "\n",
    "df_name_findability\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "id": "9fdc9cad",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "retrieval_count = df_name_findability[\"count\"].value_counts().to_frame(name=\"Retrieval count\")\n",
    "\n",
    "sns.barplot(\n",
    "    x=retrieval_count.index.astype(str), \n",
    "    y=retrieval_count[\"Retrieval count\"]\n",
    ")\n",
    "plt.title(\"Retrieval count for each user in dataset\")\n",
    "plt.savefig(Path(fp_figs, 'user_collection_user_findability.png'))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a0934aae",
   "metadata": {},
   "source": [
    "### Findability per method\n",
    "\n",
    "This table indicates how succeful a method is collecting users."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "id": "23136aa1",
   "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>count</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>source</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>manual_users</th>\n",
       "      <td>269</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>github_search_users</th>\n",
       "      <td>92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>github_search_repo</th>\n",
       "      <td>85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>research_org_commits</th>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      count\n",
       "source                     \n",
       "manual_users            269\n",
       "github_search_users      92\n",
       "github_search_repo       85\n",
       "research_org_commits     25"
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_method_findability = (df_name_method_crosstab > 0) \\\n",
    "    .astype(int) \\\n",
    "    .sum(axis=0) \\\n",
    "    .sort_values(ascending=False) \\\n",
    "    .to_frame(name=\"count\")\n",
    "\n",
    "df_method_findability"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "id": "21be8f84",
   "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>count</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>source</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>manual_users</th>\n",
       "      <td>269</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>github_search_users</th>\n",
       "      <td>92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>github_search_repo</th>\n",
       "      <td>85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>research_org_commits</th>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      count\n",
       "source                     \n",
       "manual_users            269\n",
       "github_search_users      92\n",
       "github_search_repo       85\n",
       "research_org_commits     25"
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_method_findability"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "id": "68bddfe5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(\n",
    "    x=df_method_findability.index, \n",
    "    y=df_method_findability[\"count\"]\n",
    ")\n",
    "plt.title(\"Retrieval count for each method in dataset\")\n",
    "plt.savefig(Path(fp_figs, 'user_collection_method_findability.png'))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e4311df7",
   "metadata": {},
   "source": [
    "## Filtering of users\n",
    "\n",
    "Not all users collected in the previous steps are relevant to the analysis. Some are not (or no longer) part of the organisation, or excluded for other reasons (for example students)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "id": "f2e9bb52",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['user_id', 'source', 'service', 'date', 'id', 'node_id', 'avatar_url',\n",
      "       'gravatar_id', 'url', 'html_url', 'followers_url', 'following_url',\n",
      "       'gists_url', 'starred_url', 'subscriptions_url', 'organizations_url',\n",
      "       'repos_url', 'events_url', 'received_events_url', 'type', 'site_admin',\n",
      "       'name', 'company', 'blog', 'location', 'email', 'hireable', 'bio',\n",
      "       'twitter_username', 'public_repos', 'public_gists', 'followers',\n",
      "       'following', 'created_at', 'updated_at', 'is_student', 'is_employee',\n",
      "       'is_currently_employed', 'is_research_group', 'final_decision', 'note',\n",
      "       'source_annotated', 'service_annotated', 'date_annotated'],\n",
      "      dtype='object')\n"
     ]
    },
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>user_id</th>\n",
       "      <th>is_student</th>\n",
       "      <th>is_employee</th>\n",
       "      <th>is_currently_employed</th>\n",
       "      <th>is_research_group</th>\n",
       "      <th>final_decision</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>030jmk</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>030jmk</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>87surendra</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Clarkson University</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Computational Linguistics\"</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>a-klaus</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>Computational Linguistics\"</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>a-moi</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>| e-mail:</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>poiaganova@uni-potsdam.de\"</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>abdelwahab86</td>\n",
       "      <td>False</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>556</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>557</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>558</th>\n",
       "      <td>yadavhimanshu059</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>559</th>\n",
       "      <td>yanapalacheva</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>560</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>561</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>562</th>\n",
       "      <td>Natural Language Processing engineer</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>563</th>\n",
       "      <td>Send  me an email:</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>564</th>\n",
       "      <td>yerkezhan.abdullayeva@uni-potsdam.de\"</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>565</th>\n",
       "      <td>yuxizhang11</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>566</th>\n",
       "      <td>zentrum-lexikographie</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>567</th>\n",
       "      <td>zentrum-lexikographie</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>568 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                   user_id is_student  is_employee  \\\n",
       "0                                   030jmk       True          NaN   \n",
       "1                                   030jmk       True          NaN   \n",
       "2                               87surendra        NaN          NaN   \n",
       "3                      Clarkson University        NaN          NaN   \n",
       "4                                      NaN        NaN          NaN   \n",
       "5               Computational Linguistics\"        NaN          NaN   \n",
       "6                                  a-klaus        NaN          NaN   \n",
       "7               Computational Linguistics\"        NaN          NaN   \n",
       "8                                    a-moi        NaN          NaN   \n",
       "9                                | e-mail:        NaN          NaN   \n",
       "10              poiaganova@uni-potsdam.de\"        NaN          NaN   \n",
       "11                            abdelwahab86      False          1.0   \n",
       "..                                     ...        ...          ...   \n",
       "556                                    NaN        NaN          NaN   \n",
       "557                                    NaN        NaN          NaN   \n",
       "558                       yadavhimanshu059      False          NaN   \n",
       "559                          yanapalacheva        NaN          NaN   \n",
       "560                                    NaN        NaN          NaN   \n",
       "561                                    NaN        NaN          NaN   \n",
       "562   Natural Language Processing engineer        NaN          NaN   \n",
       "563                     Send  me an email:        NaN          NaN   \n",
       "564  yerkezhan.abdullayeva@uni-potsdam.de\"        NaN          NaN   \n",
       "565                            yuxizhang11       True          NaN   \n",
       "566                  zentrum-lexikographie      False          NaN   \n",
       "567                  zentrum-lexikographie      False          NaN   \n",
       "\n",
       "     is_currently_employed  is_research_group  final_decision  \n",
       "0                      NaN                NaN             NaN  \n",
       "1                      NaN                NaN             NaN  \n",
       "2                      NaN                NaN             NaN  \n",
       "3                      NaN                NaN             NaN  \n",
       "4                      NaN                NaN             NaN  \n",
       "5                      NaN                NaN             NaN  \n",
       "6                      NaN                NaN             NaN  \n",
       "7                      NaN                NaN             NaN  \n",
       "8                      NaN                NaN             NaN  \n",
       "9                      NaN                NaN             NaN  \n",
       "10                     NaN                NaN             NaN  \n",
       "11                     1.0                NaN             NaN  \n",
       "..                     ...                ...             ...  \n",
       "556                    NaN                NaN             NaN  \n",
       "557                    NaN                NaN             NaN  \n",
       "558                    NaN                NaN             NaN  \n",
       "559                    NaN                NaN             NaN  \n",
       "560                    NaN                NaN             NaN  \n",
       "561                    NaN                NaN             NaN  \n",
       "562                    NaN                NaN             NaN  \n",
       "563                    NaN                NaN             NaN  \n",
       "564                    NaN                NaN             NaN  \n",
       "565                    NaN                NaN             NaN  \n",
       "566                    NaN                1.0             NaN  \n",
       "567                    NaN                1.0             NaN  \n",
       "\n",
       "[568 rows x 6 columns]"
      ]
     },
     "execution_count": 121,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_annotated_users = pd.read_csv(annotated_users, delimiter=';')\n",
    "print(df_annotated_users.columns)\n",
    "\n",
    "df_annotated_users[\n",
    "    [\"user_id\", \n",
    "     \"is_student\", \n",
    "     \"is_employee\", \n",
    "     \"is_currently_employed\", \n",
    "     \"is_research_group\", \n",
    "     \"final_decision\"\n",
    "    ]\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "id": "ad800df2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The number of included users is 0\n"
     ]
    }
   ],
   "source": [
    "df_annotated_users_included = df_annotated_users[df_annotated_users[\"final_decision\"] == 1]\n",
    "\n",
    "print(\"The number of included users is\", len(df_annotated_users_included))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "edc0ecfe",
   "metadata": {},
   "source": [
    "## Properties of users"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "id": "8854f817",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Series([], Name: is_research_group, dtype: int64)"
      ]
     },
     "execution_count": 126,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_annotated_users_included \\\n",
    "    .loc[df_annotated_users_included[\"final_decision\"] == 1, \"is_research_group\"] \\\n",
    "    .fillna(0) \\\n",
    "    .value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "id": "837dc7d9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>user_id</th>\n",
       "      <th>public_repos</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
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       "Empty DataFrame\n",
       "Columns: [user_id, public_repos]\n",
       "Index: []"
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     "execution_count": 127,
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     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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AACQEMwAAACQEMwAAACQEMwAAACQEMwAAACQEMwAAACQEMwAAACQEMwAAACQEMwAAACQEMwAAACQEMwAAACQEMwAAACQEMwAAACQEMwAAACQEMwAAACQEMwAAACQEMwAAACQEMwAAACQEMwAAACQEMwAAACQEMwAAACQEMwAAACQEMwAAACQEMwAAACQEMwAAACQEMwAAACQEMwAAACQEMwAAACQEMwAAACQEMwAAACQEMwAAACQEMwAAACQEMwAAACQEMwAAACQEMwAAACQEMwAAACQEMwAAACQEMwAAACQEMwAAACQEMwAAACQEMwAAACQEMwAAACQEMwAAACQEMwAAACQEMwAAACQEMwAAACQEMwAAACQEMwAAACQEMwAAACQEMwAAACQEMwAAACQEMwAAACQEMwAAACQEMwAAACQEMwAAACQEMwAAACQEMwAAACQEMwAAACQEMwAAACQEMwAAACQEMwAAACQEMwAAACQEMwAAACQEMwAAACQEMwAAACQEMwAAACQEMwAAACQEMwAAACQEMwAAACQEMwAAACQEMwAAACQEMwAAACQEMwAAACRqq33CUkpERGzcuLHapwYAAIBtbO3PrT1aLVUP5vXr10dERGNjY7VPDQAAAP/T+vXro76+vmrnq3ow77333hERsXbt2qoOCp8lGzdujMbGxli3bl3U1dXt7HFgh7DO2R1Y5+wOrHN2Bxs2bIgvfelLrT1aLVUP5i5dPvpYdH19vb+QfO7V1dVZ53zuWefsDqxzdgfWObuDrT1atfNV9WwAAADwOSGYAQAAIFH1YO7Ro0dMnTo1evToUe1Tw2eGdc7uwDpnd2Cdszuwztkd7Kh1XlOq/b3bAAAA8DngLdkAAACQEMwAAACQEMwAAACQEMwAAACQ6FAw33nnnfHlL385evbsGUOGDInFixd/4v6LFi2KIUOGRM+ePePggw+OX/7ylx0aFjpTJev897//fZxwwgmx3377RV1dXQwbNiz+8pe/dOK00DGV/jzf6pFHHona2to44ogjduyAUAWVrvN33303rr766jjwwAOjR48e0b9///j1r3/dSdNCx1S6zu++++44/PDDY4899oi+ffvGeeedF+vXr++kaaFyDz/8cJx22mnRr1+/qKmpifvuu+9Tj6lGh1YczHPmzInJkyfH1VdfHS0tLTFixIg4+eSTY+3aten+a9asiVNOOSVGjBgRLS0t8aMf/SguvfTS+N3vflfxsNBZKl3nDz/8cJxwwgnxwAMPRHNzcxx77LFx2mmnRUtLSydPDu1X6TrfasOGDTFhwoQ4/vjjO2lS6LiOrPMzzzwzHnzwwbjrrrvi2WefjdmzZ8eAAQM6cWqoTKXrfMmSJTFhwoQ4//zzY/ny5TF37tx44okn4oILLujkyaH93n777Tj88MPjjjvuaNf+VevQUqGhQ4eWiRMnttk2YMCAMmXKlHT/H/zgB2XAgAFttl100UXlqKOOqvSpodNUus4zgwYNKtOmTav2aFA1HV3n48aNK9dcc02ZOnVqOfzww3fghLD9Kl3nf/rTn0p9fX1Zv359Z4wHVVHpOr/55pvLwQcf3GbbbbfdVhoaGnbYjFBNEVHmzZv3iftUq0MrusL83nvvRXNzc5x44olttp944onx6KOPpsc89thj2+w/ZsyYWLp0abz//vuV1T10go6s84/bsmVLbNq0Kfbee+8dMSJst46u85kzZ8bq1atj6tSpO3pE2G4dWef3339/NDU1xU033RQHHHBAHHbYYfH9738/Nm/e3BkjQ8U6ss6HDx8eL730UjzwwANRSonXXnst7r333jj11FM7Y2ToFNXq0NpKnvT111+PDz/8MPbff/822/fff/949dVX02NeffXVdP8PPvggXn/99ejbt28lI8AO15F1/nG33HJLvP3223HmmWfuiBFhu3Vkna9atSqmTJkSixcvjtraiv75gJ2iI+v8+eefjyVLlkTPnj1j3rx58frrr8d3v/vdeOONN3yOmc+kjqzz4cOHx9133x3jxo2L//znP/HBBx/E6aefHrfffntnjAydolod2qEv/aqpqWlzv5SyzbZP2z/bDp8lla7zrWbPnh3XXnttzJkzJ77whS/sqPGgKtq7zj/88MM466yzYtq0aXHYYYd11nhQFZX8PN+yZUvU1NTE3XffHUOHDo1TTjklbr311pg1a5arzHymVbLOV6xYEZdeemn85Cc/iebm5vjzn/8ca9asiYkTJ3bGqNBpqtGhFV0i2HfffaNr167b/N+qf/3rX9vU+1Zf/OIX0/1ra2tjn332qeTpoVN0ZJ1vNWfOnDj//PNj7ty5MXr06B05JmyXStf5pk2bYunSpdHS0hKTJk2KiI/CopQStbW1MX/+/DjuuOM6ZXZor478PO/bt28ccMABUV9f37pt4MCBUUqJl156KQ499NAdOjNUqiPr/IYbboijjz46rrzyyoiIGDx4cOy5554xYsSIuP76670DlM+FanVoRVeYu3fvHkOGDIkFCxa02b5gwYIYPnx4esywYcO22X/+/PnR1NQU3bp1q+TpoVN0ZJ1HfHRl+dxzz4177rnHZ4D4zKt0ndfV1cXTTz8dy5Yta71NnDgxvvKVr8SyZcviG9/4RmeNDu3WkZ/nRx99dLz88svx73//u3Xbc889F126dImGhoYdOi90REfW+TvvvBNdurTNgK5du0bE/12Bg11d1Tq0oq8IK6X89re/Ld26dSt33XVXWbFiRZk8eXLZc889ywsvvFBKKWXKlCnl7LPPbt3/+eefL3vssUe57LLLyooVK8pdd91VunXrVu69995Knxo6TaXr/J577im1tbVlxowZ5ZVXXmm9vfXWWzvrJcCnqnSdf5xvyWZXUOk637RpU2loaCjf+ta3yvLly8uiRYvKoYceWi644IKd9RLgU1W6zmfOnFlqa2vLnXfeWVavXl2WLFlSmpqaytChQ3fWS4BPtWnTptLS0lJaWlpKRJRbb721tLS0lBdffLGUsuM6tOJgLqWUGTNmlAMPPLB07969HHnkkWXRokWtj51zzjll1KhRbfZfuHBh+frXv166d+9eDjrooPKLX/yiI08LnaqSdT5q1KgSEdvczjnnnM4fHCpQ6c/z/yaY2VVUus5XrlxZRo8eXXr16lUaGhrK5ZdfXt55551OnhoqU+k6v+2228qgQYNKr169St++fcv48ePLSy+91MlTQ/s99NBDn/jf2zuqQ2tK8b4LAAAA+LgOfUs2AAAAfN4JZgAAAEgIZgAAAEgIZgAAAEgIZgAAAEgIZgAAAEgIZgAAAEgIZgBohxdeeCFqampi2bJl/3OfhQsXRk1NTbz11lsRETFr1qzo06dPp8wHAFSfYAaAHWTcuHHx3HPP7ewxAIAOqt3ZAwDA51WvXr2iV69eO+Tc7733XnTv3n2HnBsA+IgrzADsFo455piYNGlSTJo0Kfr06RP77LNPXHPNNVFKiYiImpqauO+++9oc06dPn5g1a1abbc8880wMHz48evbsGV/96ldj4cKF//M5s7dk33///dHU1BQ9e/aMfffdN8aOHduu+Q866KC4/vrr49xzz436+vr4zne+ExERjz76aIwcOTJ69eoVjY2Ncemll8bbb7/d5rjrrrsuzjrrrNhrr72iX79+cfvtt7c599q1a+Ob3/xm7LXXXlFXVxdnnnlmvPbaa62PP/XUU3HsscdG7969o66uLoYMGRJLly5t19wAsCsTzADsNn7zm99EbW1t/P3vf4/bbrstfvrTn8avfvWris5x5ZVXxhVXXBEtLS0xfPjwOP3002P9+vXtOvaPf/xjjB07Nk499dRoaWmJBx98MJqamtr93DfffHN87Wtfi+bm5vjxj38cTz/9dIwZMybGjh0b//jHP2LOnDmxZMmSmDRp0jbHDR48OJ588sn44Q9/GJdddlksWLAgIiJKKXHGGWfEG2+8EYsWLYoFCxbE6tWrY9y4ca3Hjx8/PhoaGuKJJ56I5ubmmDJlSnTr1q3dcwPALqsAwG5g1KhRZeDAgWXLli2t26666qoycODAUkopEVHmzZvX5pj6+voyc+bMUkopa9asKRFRpk+f3vr4+++/XxoaGsqNN95YSinloYceKhFR3nzzzVJKKTNnziz19fWt+w8bNqyMHz++Q/MfeOCB5Ywzzmiz7eyzzy4XXnhhm22LFy8uXbp0KZs3b2497qSTTmqzz7hx48rJJ59cSill/vz5pWvXrmXt2rWtjy9fvrxERHn88cdLKaX07t27zJo1q0NzA8CuzBVmAHYbRx11VNTU1LTeHzZsWKxatSo+/PDDdp9j2LBhrX+ura2NpqamWLlyZbuOXbZsWRx//PHtH/hjPn41urm5OWbNmhV77bVX623MmDGxZcuWWLNmTTrz1vtbZ165cmU0NjZGY2Nj6+ODBg2KPn36tO5z+eWXxwUXXBCjR4+O6dOnx+rVqzv8GgBgVyKYASA++gxz+f+fZ97q/fffb/ex7bG9XwC25557trm/ZcuWuOiii2LZsmWtt6eeeipWrVoV/fv3/8RzbZ25lJLO/9/br7322li+fHmceuqp8de//jUGDRoU8+bN267XAgC7AsEMwG7jb3/72zb3Dz300OjatWvst99+8corr7Q+tmrVqnjnnXc+8RwffPBBNDc3x4ABA9r1/IMHD44HH3ywg9Nv68gjj4zly5fHIYccss3tv79BO3vdW2ceNGhQrF27NtatW9f6+IoVK2LDhg0xcODA1m2HHXZYXHbZZTF//vwYO3ZszJw5s2qvAwA+qwQzALuNdevWxeWXXx7PPvtszJ49O26//fb43ve+FxERxx13XNxxxx3x5JNPxtKlS2PixInpF1vNmDEj5s2bF88880xcfPHF8eabb8a3v/3tdj3/1KlTY/bs2TF16tRYuXJlPP3003HTTTd1+PVcddVV8dhjj8XFF18cy5Yti1WrVsX9998fl1xySZv9HnnkkbjpppviueeeixkzZsTcuXNbX/fo0aNj8ODBMX78+HjyySfj8ccfjwkTJsSoUaOiqakpNm/eHJMmTYqFCxfGiy++GI888kg88cQTbWIaAD6v/B5mAHYbEyZMiM2bN8fQoUOja9eucckll8SFF14YERG33HJLnHfeeTFy5Mjo169f/PznP4/m5uZtzjF9+vS48cYbo6WlJfr37x9/+MMfYt99923X8x9zzDExd+7cuO6662L69OlRV1cXI0eO7PDrGTx4cCxatCiuvvrqGDFiRJRSon///m2+4Toi4oorrojm5uaYNm1a9O7dO2655ZYYM2ZMRPzfr9O65JJLYuTIkdGlS5c46aSTWn/1VNeuXWP9+vUxYcKEeO2111p/Fda0adM6PDcA7Cpqysc/sAUAn0PHHHNMHHHEEfGzn/1sZ4/SqQ466KCYPHlyTJ48eWePAgC7HG/JBgAAgIRgBoCdbPHixW1+NdTHbwDAzuEt2QCwk23evDn++c9//s/HDznkkE6cBgDYSjADAABAwluyAQAAICGYAQAAICGYAQAAICGYAQAAICGYAQAAICGYAQAAICGYAQAAICGYAQAAIPH/APWLh+vuODJ2AAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 1200x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# create a boxplot with swarm\n",
    "ax = sns.boxplot(x='public_repos', data=df_annotated_users_included)\n",
    "ax = sns.swarmplot(x='public_repos', data=df_annotated_users_included, color=\".25\")\n",
    "\n",
    "# output users with most public repos\n",
    "df_annotated_users_included[['user_id', 'public_repos']].sort_values('public_repos', ascending=False).head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "id": "237f17b4",
   "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>user_id</th>\n",
       "      <th>public_gists</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [user_id, public_gists]\n",
       "Index: []"
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(x='public_gists', data=df_annotated_users_included)\n",
    "df_annotated_users_included[['user_id', 'public_gists']].sort_values('public_gists', ascending=False).head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "id": "0ac0bf4a",
   "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>user_id</th>\n",
       "      <th>followers</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [user_id, followers]\n",
       "Index: []"
      ]
     },
     "execution_count": 129,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = sns.boxplot(x='followers', data=df_annotated_users_included, showfliers=False)\n",
    "\n",
    "df_annotated_users_included[['user_id', 'followers']].sort_values('followers', ascending=False).head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "id": "e85a1a02",
   "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>user_id</th>\n",
       "      <th>following</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [user_id, following]\n",
       "Index: []"
      ]
     },
     "execution_count": 130,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(x=df_annotated_users_included['following'], showfliers = False)\n",
    "df_annotated_users_included[['user_id', 'following']].sort_values('following', ascending=False).head(10)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "05c2b0a4",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1b9e1ec5",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9560d3d1",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "663185da",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.11.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}