get_data.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import yaml\n",
    "import numpy as np\n",
    "import json\n",
    "import requests\n",
    "import collect_data\n",
    "import re\n",
    "from importlib import reload\n",
    "from bs4 import BeautifulSoup\n",
    "from contextlib import suppress\n",
    "from scipy.stats import ttest_ind\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import warnings\n",
    "sns.set_style('darkgrid')\n",
    "sns.set_palette('colorblind')\n",
    "sns.set_color_codes()\n",
    "sns.set_context('talk')\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Collecting Data From the NIPS 2017 Schedule\n",
    "\n",
    "NIPS 2017 includes canonical links the authors' repository for a given paper.  We use BeautiulSoup to scrape the website's schedule metadata for paper information and the link to the repository.\n",
    "\n",
    "**NOTE**: To collect data, an internet connection is required. Optionally, one can update the GitHub data with a API token. Pulling new data may result in different statistics from data collection at the time of publication."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "soup = BeautifulSoup(requests.get(\"https://nips.cc/Conferences/2017/Schedule\").content, 'html5lib')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "poster_tables = soup.find_all('div', {'class':'maincard narrower Poster'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "papers = pd.DataFrame(index=range(len(poster_tables)),\n",
    "                      columns=['title', 'authors', 'paper_url', 'code_url', 'poster_url'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "for p in papers.index:\n",
    "    title = poster_tables[p].find('div',{'class':'maincardBody'}).contents[0]\n",
    "    authors = poster_tables[p].find('div',{'class':'maincardFooter'}).contents[0]\n",
    "    pdf = collect_data.get_url(poster_tables[p], 'Paper')\n",
    "    repo = collect_data.get_url(poster_tables[p], 'Code')\n",
    "    poster_pdf = collect_data.get_url(poster_tables[p], 'Poster')\n",
    "    papers.loc[p] = pd.Series(dict(zip(papers.columns,\n",
    "                                       [title, authors, pdf, repo, poster_pdf])))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "papers.to_csv('all_papers.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "code_papers = papers[papers['code_url'].notnull()]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We also test the urls for GitHub repos by making a request to the listed webpage"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "code_papers['github'] = code_papers['code_url'].str.contains(\"github.com\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "code_papers['live'] = [collect_data.test_url(url) for url in code_papers['code_url']]\n",
    "code_papers['missing_http'] = np.logical_and(code_papers['live'], code_papers.code_url.str[:4] != 'http')\n",
    "code_papers['code_url'] = ['http://' + code_papers['code_url'].ix[c] if code_papers['missing_http'].ix[c] else code_papers['code_url'].ix[c] for c in code_papers.index]\n",
    "code_papers['gh_full_path'] = np.logical_and(code_papers['github'], \n",
    "                                             code_papers['code_url'].str.rstrip('/')\\\n",
    "                                             .str.split('/').str.len() > 4)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Some papers are not live at the time of testing, so we extract these and manually review to find the webpage"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "code_papers[np.logical_or(code_papers['live'] != True, ~code_papers['gh_full_path'])].to_csv('validate_url.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "live_gh = code_papers[np.logical_and(code_papers['gh_full_path'], code_papers['live'] == True)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We collect scraped data on the existence of files in each repo that can be used by repo2docker by inspecting the GitHub page of each repo for the file.  The file types are listed in `file_types`.\n",
    "\n",
    "GitHub metadata is collected using the GitHub graphql api.  To run this query, one will need to create a [personal access token with GitHub](https://help.github.com/articles/creating-a-personal-access-token-for-the-command-line/).\n",
    "\n",
    "The block of code below collects all the data from GitHub. Again, this does not run except with a personal access token. Repo2docker file information from this block is saved as `gh_r2d_data.csv`, and GitHub metadata is saved as `gh_metadata.csv`.  Note also we exclude repositories that have changed their URL and include in later analysis.\n",
    "\n",
    "We have supressed these exceptions to get the notebook to run the whole way through"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "file_types = ['Dockerfile', 'binder', 'apt.txt', 'environment.yml',\n",
    "          'requirements.txt', 'postBuild', 'setup.py', 'REQUIRE', 'runtime.txt',\n",
    "          'install.R']\n",
    "social_vals = ['description', 'stargazers', 'watchers',\n",
    "           'forks', 'languages', 'repositoryTopics'] \n",
    "r2d_checks = pd.DataFrame(index=live_gh['code_url'], columns=file_types)\n",
    "with suppress(Exception):\n",
    "    for i in r2d_checks.index:\n",
    "        gh_url = re.sub(r\"\\.git$\", \"\", i)\n",
    "        soup_i = BeautifulSoup(requests.get(gh_url).content, \"html5lib\")\n",
    "        r2d_checks.loc[i] = pd.Series(collect_data.request_inspect_for_r2d(i, soup_i, file_types))\n",
    "r2d_checks.to_csv('gh_r2d_data.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To recollect data, you will need to replace the line below with your GitHub personal access token\n",
    "For ease of processing, we strip `.git` extensions to standardize our data collection."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "try:\n",
    "    credentials = yaml.load(open('../binder_study/secrets.yml'))  \n",
    "    gh_metadata = pd.DataFrame(index=live_gh['code_url'], columns=social_vals)\n",
    "    with suppress(Exception):\n",
    "        for i in gh_metadata.index:\n",
    "            gh_url = re.sub(r\"\\.git$\", \"\", i)\n",
    "            split_url = gh_url.split('/')\n",
    "            gh_metadata.loc[i] = pd.Series(collect_data.graphql_social_data(split_url[3],\n",
    "                                    split_url[4], credentials['binder']['key']))\n",
    "    live_gh[live_gh['code_url'].isin(gh_metadata[gh_metadata.isna().all(axis=1)]\\\n",
    "                                     .index.values)].to_csv('change_reponame.csv')\n",
    "    gh_metadata = collect_data.process_gh_api_df(gh_metadata.dropna(how='all'))\n",
    "    gh_metadata[gh_metadata['no_code']].to_csv('no_code.csv')\n",
    "    gh_metadata = gh_metadata[~gh_metadata['no_code']] \n",
    "    gh_metadata.to_csv('gh_metadata.csv')\n",
    "except:\n",
    "    gh_metadata = pd.read_csv('gh_metadata.csv', index_col=0)                                "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We drop data that does not have any programming languages associated with the repo."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "r2d_checks = r2d_checks[r2d_checks.index.isin((~gh_metadata['no_code']).index)]\n",
    "r2d_checks.to_csv('gh_r2d_data.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Analyzing Manually Annotated Data\n",
    "\n",
    "For repositories that do not have automatically accessible repositories available, we manually annotate to find the author's URL. Files with these lables are loaded below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "gh_labeled = pd.read_csv('validate_url_w_labels.csv', index_col=0)\n",
    "changed_url_labeled = pd.read_csv('change_reponame_labeled.csv', index_col=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "r2d_labeled = pd.DataFrame(index=gh_labeled[gh_labeled['labeled_url'].str[:19] == 'https://github.com/']\n",
    "                           ['labeled_url'].append(changed_url_labeled['labeled_url']), columns=file_types)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "gh_metadata_labeled = pd.DataFrame(index=r2d_labeled.index, columns=social_vals)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We now collect data for these labeled URLs using the same methodology to find configuration files and GitHub metadata. Again, an access token can be used to pull the latest GitHub data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "with suppress(Exception):\n",
    "    for i in r2d_labeled.index:\n",
    "        split_url = i.split('/')\n",
    "        soup_i = BeautifulSoup(requests.get(i).content, \"html5lib\")\n",
    "        if i not in r2d_checks.index:\n",
    "            r2d_labeled.loc[i] = pd.Series(collect_data.request_inspect_for_r2d(i, soup_i, file_types))\n",
    "try:\n",
    "    r2d_labeled.to_csv('r2d_labeled.csv')\n",
    "except:\n",
    "    r2d_labeled = pd.read_csv('r2d_labeled.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "try:\n",
    "    for i in gh_metadata_labeled.index:\n",
    "        split_url = i.split('/')\n",
    "        gh_metadata_labeled.loc[i] = pd.Series(collect_data.graphql_social_data(split_url[3], split_url[4], credentials['binder']['key']))\n",
    "    gh_metadata_labeled = collect_data.process_gh_api_df(gh_metadata_labeled)\n",
    "    gh_metadata_labeled.to_csv('gh_labeled.csv')\n",
    "except:\n",
    "    gh_metadata_labeled = pd.read_csv('gh_labeled.csv', index_col=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Labeled and unlabeled datasets are combined below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "try:\n",
    "    total_gh_metadata = pd.concat([gh_metadata, gh_metadata_labeled])\n",
    "    total_r2d = pd.concat([r2d_checks, r2d_labeled])\n",
    "    total_gh_metadata.to_csv('gh_metadata_w_labeled.csv')\n",
    "    total_r2d.to_csv('r2d_w_labeled.csv')\n",
    "except:\n",
    "    total_gh_metadata = pd.read_csv('gh_metadata_w_labeled.csv', index_col=0)\n",
    "    total_r2d = pd.read_csv('r2d_w_labeled.csv', index_col=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Conference Wide Metrics\n",
    "\n",
    "We calculate the percent of papers that have included any code url, a paper url, and a url to a live GitHub page."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "paper_metrics = (len(papers) - papers.isnull().sum()).loc[['code_url','poster_url']]\\\n",
    ".append(live_gh[['gh_full_path']].sum())\n",
    "paper_metrics.index = ['Shared Code', 'Shared Poster', 'Live GitHub Link']\n",
    "(100.*paper_metrics/len(papers)).plot(kind='bar', cmap='coolwarm')\n",
    "plt.ylabel('')\n",
    "plt.xlabel('')\n",
    "plt.title('Percent of NIPS Papers by Repo Metric')\n",
    "plt.yticks(np.arange(0,55,5))\n",
    "plt.xticks(rotation=0)\n",
    "plt.tight_layout()\n",
    "plt.savefig('paper_metrics.pdf')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Below are the raw counts:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Shared Code         248\n",
       "Shared Poster       146\n",
       "Live GitHub Link    193\n",
       "dtype: int64"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "paper_metrics"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Below are the percentages in the above plot."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Shared Code         36.524300\n",
       "Shared Poster       21.502209\n",
       "Live GitHub Link    28.424153\n",
       "dtype: float64"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(100.*paper_metrics/len(papers))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Programming languages that repo2docker supports are popular among NIPS authors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "(100*total_gh_metadata[['Python', 'Julia', 'R']].sum()/len(total_gh_metadata)).plot(kind='bar', cmap='coolwarm')\n",
    "plt.title('Percent of GitHub Repositories by Language')\n",
    "plt.xticks(rotation=0)\n",
    "plt.tight_layout()\n",
    "plt.savefig('pct_languages.pdf')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We plot the primary languages of the repositories. By far, the most popular language is Python."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "primary_languages= total_gh_metadata['primary_language'].value_counts()\n",
    "primary_languages.rename(index={'Jupyter Notebook': 'Jupyter'}, inplace=True)\n",
    "primary_languages.plot(kind='bar', cmap='coolwarm')\n",
    "plt.title('Repository Primary Language')\n",
    "plt.ylabel('Number of Repositories')\n",
    "plt.xticks(rotation=45, horizontalalignment='right')\n",
    "plt.tight_layout()\n",
    "plt.savefig('primary_language.pdf')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For reference, we also include information on papers that were manually labeled.  A minority of them required manual review."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "labeled_url_stats = gh_labeled['labeled_url'].notnull().value_counts()\n",
    "labeled_url_stats.index = ['found url', 'url still missing']\n",
    "labeled_url_stats.loc['changed_url'] = len(changed_url_labeled)\n",
    "labeled_url_stats.index = ['found url', 'url still missing', 'changed url']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "labeled_url_stats.plot(kind='bar',  cmap='coolwarm')\n",
    "plt.ylabel('')\n",
    "plt.xlabel('')\n",
    "plt.title('NIPS Papers by Needing Manual Review')\n",
    "plt.xticks(rotation=0)\n",
    "plt.tight_layout()\n",
    "plt.savefig('manual_review_metrics.pdf')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "679"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(papers)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Reproducing Reults Figures\n",
    "\n",
    "For reference there are 197 papers in our analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "215"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(total_gh_metadata)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We first plot the number of repositories by each type of configuration file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "total_r2d.sum().sort_values(ascending=False).plot(kind='bar', colormap='coolwarm')\n",
    "plt.title('GitHub Repositories by Type of Config File')\n",
    "plt.ylabel('')\n",
    "plt.xticks(rotation=45, horizontalalignment='right')\n",
    "plt.tight_layout()\n",
    "plt.savefig('repos_by_r2d_file.pdf')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Below are the raw values:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "setup.py            24\n",
       "requirements.txt    19\n",
       "Dockerfile           5\n",
       "environment.yml      4\n",
       "install.R            0\n",
       "runtime.txt          0\n",
       "REQUIRE              0\n",
       "postBuild            0\n",
       "apt.txt              0\n",
       "binder               0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "total_r2d.sum().sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Below are the number of repositories that have 0, 1, 2, or 3 configuration files:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "total_r2d.sum(axis=1).value_counts().plot(kind='bar', colormap='coolwarm', rot=0)\n",
    "plt.xlabel('Number of Config Files')\n",
    "plt.ylabel('Number of Repositories')\n",
    "plt.title('Repo2Docker Config File Count')\n",
    "plt.tight_layout()\n",
    "plt.savefig('total_r2d_files_plot.pdf')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The raw values of the plot above are below"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    176\n",
       "1     28\n",
       "2      9\n",
       "3      2\n",
       "dtype: int64"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "total_r2d.sum(axis=1).value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We now label repositories that are repo2docker compatible as repositories that have at least 1 type of configuration file that repo2docker uses."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "total_gh_metadata['r2d_capable'] = total_r2d.sum(axis=1) > 0"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The average number of users that have forked, starred, or watched is significantly higher when we exclude repositories that are part of larger libraries.  We label larger libraries as libraries that point to a specific folder in the repo as the code, rather than the main repository URL.  These libraries are part of larger deep learning repos."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "total_gh_metadata['main_repo'] = np.logical_or(total_gh_metadata.index.str.rstrip('/').str.split('/').str.len() < 6, \n",
    "np.logical_and(total_gh_metadata.index.str.rstrip('/').str.split('/').str.len() == 7, total_gh_metadata.index.str.rstrip('/').str.split('/').str[5] == 'tree'))\n",
    "main_repo = total_gh_metadata[total_gh_metadata['main_repo']]\n",
    "wo_larger_test = ttest_ind(main_repo[main_repo['r2d_capable']][['forks', 'stargazers', 'watchers']],\n",
    "          main_repo[~main_repo['r2d_capable']][['forks', 'stargazers', 'watchers']])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Ttest_indResult(statistic=array([2.15421431, 2.49946935, 2.54987436]), pvalue=array([0.03236211, 0.01320364, 0.01148913]))"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wo_larger_test"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The means of these two groups are listed below:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "gh_means = main_repo.groupby('r2d_capable')[['forks', 'stargazers', 'watchers']].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "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>forks</th>\n",
       "      <th>stargazers</th>\n",
       "      <th>watchers</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>r2d_capable</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>False</th>\n",
       "      <td>28.150289</td>\n",
       "      <td>108.947977</td>\n",
       "      <td>9.387283</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>True</th>\n",
       "      <td>95.358974</td>\n",
       "      <td>411.846154</td>\n",
       "      <td>29.897436</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 forks  stargazers   watchers\n",
       "r2d_capable                                  \n",
       "False        28.150289  108.947977   9.387283\n",
       "True         95.358974  411.846154  29.897436"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gh_means"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Also, the percent difference between repos with and without these configuration files"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "forks         2.387495\n",
       "stargazers    2.780209\n",
       "watchers      2.184887\n",
       "dtype: float64"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(gh_means.loc[True] - gh_means.loc[False])/gh_means.loc[False]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We also plot the means with errorbars when excluding larger libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "r2d_gh_groupby = total_gh_metadata[~total_gh_metadata['larger_library']].groupby(['r2d_capable'])[['forks', 'stargazers', 'watchers']]\n",
    "r2d_gh_groupby.mean().T.plot(kind='bar', yerr=.975*r2d_gh_groupby.sem().T)\n",
    "plt.xlabel('')\n",
    "plt.ylabel('GitHub Users')\n",
    "plt.title('Mean GitHub Engagement (CI=0.95)')\n",
    "plt.xticks(rotation=0)\n",
    "plt.tight_layout()\n",
    "plt.savefig('engagement_excl_lrg.pdf')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Below is the number of repositories in each category. `r2d_capable` means the repo has at least 1 config file used by repo2docker."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "r2d_capable\n",
       "False    173\n",
       "True      39\n",
       "Name: stargazers, dtype: int64"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "main_repo.groupby('r2d_capable')['stargazers'].count()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Below are the larger libraries we've excluded from our analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [],
   "source": [
    "larger_libraries = total_gh_metadata[~total_gh_metadata['main_repo']]\n",
    "larger_libraries.to_csv('larger_libraries.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "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>description</th>\n",
       "      <th>stargazers</th>\n",
       "      <th>watchers</th>\n",
       "      <th>forks</th>\n",
       "      <th>languages</th>\n",
       "      <th>repositoryTopics</th>\n",
       "      <th>n_languages</th>\n",
       "      <th>no_code</th>\n",
       "      <th>primary_language</th>\n",
       "      <th>Python</th>\n",
       "      <th>Julia</th>\n",
       "      <th>R</th>\n",
       "      <th>r2d_capable</th>\n",
       "      <th>larger_library</th>\n",
       "      <th>main_repo</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>https://github.com/tensorflow/models/tree/master/research/pcl_rl</th>\n",
       "      <td>Models and examples built with TensorFlow</td>\n",
       "      <td>41047</td>\n",
       "      <td>2562</td>\n",
       "      <td>22816</td>\n",
       "      <td>[Python, Jupyter Notebook, C++, HTML, Shell, J...</td>\n",
       "      <td>[]</td>\n",
       "      <td>9</td>\n",
       "      <td>False</td>\n",
       "      <td>Python</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>https://github.com/tensorflow/models/tree/master/research/rebar</th>\n",
       "      <td>Models and examples built with TensorFlow</td>\n",
       "      <td>41047</td>\n",
       "      <td>2562</td>\n",
       "      <td>22816</td>\n",
       "      <td>[Python, Jupyter Notebook, C++, HTML, Shell, J...</td>\n",
       "      <td>[]</td>\n",
       "      <td>9</td>\n",
       "      <td>False</td>\n",
       "      <td>Python</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>https://github.com/tensorflow/models/tree/master/research/fivo</th>\n",
       "      <td>Models and examples built with TensorFlow</td>\n",
       "      <td>41047</td>\n",
       "      <td>2562</td>\n",
       "      <td>22816</td>\n",
       "      <td>[Python, Jupyter Notebook, C++, HTML, Shell, J...</td>\n",
       "      <td>[]</td>\n",
       "      <td>9</td>\n",
       "      <td>False</td>\n",
       "      <td>Python</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                                                  description  \\\n",
       "https://github.com/tensorflow/models/tree/maste...  Models and examples built with TensorFlow   \n",
       "https://github.com/tensorflow/models/tree/maste...  Models and examples built with TensorFlow   \n",
       "https://github.com/tensorflow/models/tree/maste...  Models and examples built with TensorFlow   \n",
       "\n",
       "                                                    stargazers  watchers  \\\n",
       "https://github.com/tensorflow/models/tree/maste...       41047      2562   \n",
       "https://github.com/tensorflow/models/tree/maste...       41047      2562   \n",
       "https://github.com/tensorflow/models/tree/maste...       41047      2562   \n",
       "\n",
       "                                                    forks  \\\n",
       "https://github.com/tensorflow/models/tree/maste...  22816   \n",
       "https://github.com/tensorflow/models/tree/maste...  22816   \n",
       "https://github.com/tensorflow/models/tree/maste...  22816   \n",
       "\n",
       "                                                                                            languages  \\\n",
       "https://github.com/tensorflow/models/tree/maste...  [Python, Jupyter Notebook, C++, HTML, Shell, J...   \n",
       "https://github.com/tensorflow/models/tree/maste...  [Python, Jupyter Notebook, C++, HTML, Shell, J...   \n",
       "https://github.com/tensorflow/models/tree/maste...  [Python, Jupyter Notebook, C++, HTML, Shell, J...   \n",
       "\n",
       "                                                   repositoryTopics  \\\n",
       "https://github.com/tensorflow/models/tree/maste...               []   \n",
       "https://github.com/tensorflow/models/tree/maste...               []   \n",
       "https://github.com/tensorflow/models/tree/maste...               []   \n",
       "\n",
       "                                                    n_languages  no_code  \\\n",
       "https://github.com/tensorflow/models/tree/maste...            9    False   \n",
       "https://github.com/tensorflow/models/tree/maste...            9    False   \n",
       "https://github.com/tensorflow/models/tree/maste...            9    False   \n",
       "\n",
       "                                                   primary_language  Python  \\\n",
       "https://github.com/tensorflow/models/tree/maste...           Python    True   \n",
       "https://github.com/tensorflow/models/tree/maste...           Python    True   \n",
       "https://github.com/tensorflow/models/tree/maste...           Python    True   \n",
       "\n",
       "                                                    Julia      R  r2d_capable  \\\n",
       "https://github.com/tensorflow/models/tree/maste...  False  False        False   \n",
       "https://github.com/tensorflow/models/tree/maste...  False  False        False   \n",
       "https://github.com/tensorflow/models/tree/maste...  False  False        False   \n",
       "\n",
       "                                                    larger_library  main_repo  \n",
       "https://github.com/tensorflow/models/tree/maste...            True      False  \n",
       "https://github.com/tensorflow/models/tree/maste...            True      False  \n",
       "https://github.com/tensorflow/models/tree/maste...            True      False  "
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "larger_libraries"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For simplicity, we also plot a boxplot to highlight these outliers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(data=total_gh_metadata[['forks', 'stargazers', 'watchers']], fliersize=5, width=.5)\n",
    "plt.title('Boxplot of Number of Users by GitHub Metric')\n",
    "plt.tight_layout()\n",
    "plt.savefig('gh_metrics_boxplot.pdf')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python [default]",
   "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": "3.6.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}