content_across_all_repositories.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Barplot of GitHub page content"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First, import all the necessary packages for running this notebook."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pylab as plt\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Then, read in CSV file containg information on elements across all pages of each GitHub repository"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "all_pages = pd.read_csv('data/all_pages.csv', header=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Inspect dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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>organization_or_project_name</th>\n",
       "      <th>getting_started</th>\n",
       "      <th>about_section</th>\n",
       "      <th>how_to_contribute</th>\n",
       "      <th>history</th>\n",
       "      <th>copyright_information</th>\n",
       "      <th>funding_and_acknowledgements</th>\n",
       "      <th>visual_structure_of_repo</th>\n",
       "      <th>recommended_citation</th>\n",
       "      <th>archived_version_and_upcoming_version</th>\n",
       "      <th>resources</th>\n",
       "      <th>description_of_terms</th>\n",
       "      <th>best_practices_guide_to_apply_standard</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Darwin Core</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>EML</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Ecological Trait Data Standard</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>MIxS</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>IGSN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\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",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     organization_or_project_name  getting_started  about_section  \\\n",
       "0                     Darwin Core              1.0            1.0   \n",
       "1                             EML              1.0            1.0   \n",
       "2  Ecological Trait Data Standard              1.0            1.0   \n",
       "3                            MIxS              NaN            1.0   \n",
       "4                            IGSN              NaN            1.0   \n",
       "\n",
       "   how_to_contribute  history  copyright_information  \\\n",
       "0                1.0      NaN                    1.0   \n",
       "1                1.0      1.0                    1.0   \n",
       "2                1.0      NaN                    1.0   \n",
       "3                1.0      1.0                    1.0   \n",
       "4                1.0      NaN                    NaN   \n",
       "\n",
       "   funding_and_acknowledgements  visual_structure_of_repo  \\\n",
       "0                           1.0                       1.0   \n",
       "1                           1.0                       NaN   \n",
       "2                           1.0                       NaN   \n",
       "3                           1.0                       NaN   \n",
       "4                           NaN                       NaN   \n",
       "\n",
       "   recommended_citation  archived_version_and_upcoming_version  resources  \\\n",
       "0                   1.0                                    1.0        NaN   \n",
       "1                   1.0                                    1.0        NaN   \n",
       "2                   1.0                                    1.0        NaN   \n",
       "3                   1.0                                    1.0        NaN   \n",
       "4                   NaN                                    NaN        NaN   \n",
       "\n",
       "   description_of_terms  best_practices_guide_to_apply_standard  \n",
       "0                   1.0                                     NaN  \n",
       "1                   1.0                                     NaN  \n",
       "2                   1.0                                     1.0  \n",
       "3                   1.0                                     NaN  \n",
       "4                   1.0                                     NaN  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_pages.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Caclulate the sum of each column. This function transposes the table and tells us how many (for example) repositories in the review contained a 'getting started' section"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "organization_or_project_name              Darwin CoreEMLEcological Trait Data StandardMI...\n",
      "getting_started                                                                        11.0\n",
      "about_section                                                                          30.0\n",
      "how_to_contribute                                                                      20.0\n",
      "history                                                                                10.0\n",
      "copyright_information                                                                  18.0\n",
      "funding_and_acknowledgements                                                            7.0\n",
      "visual_structure_of_repo                                                                6.0\n",
      "recommended_citation                                                                   10.0\n",
      "archived_version_and_upcoming_version                                                  26.0\n",
      "resources                                                                               6.0\n",
      "description_of_terms                                                                   29.0\n",
      "best_practices_guide_to_apply_standard                                                  6.0\n",
      "dtype: object\n"
     ]
    }
   ],
   "source": [
    "all_pages_totals = all_pages.sum()\n",
    "print(all_pages_totals)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Drop the header since we'll rename later, and inspect the resulting data frame."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "getting_started          11.0\n",
       "about_section            30.0\n",
       "how_to_contribute        20.0\n",
       "history                  10.0\n",
       "copyright_information    18.0\n",
       "dtype: object"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ax_new_labels = all_pages_totals.drop(\"organization_or_project_name\")\n",
    "ax_new_labels.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Convert from a series to a dataframe, and add in new column headers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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>GitHub_elements</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>getting_started</td>\n",
       "      <td>11.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>about_section</td>\n",
       "      <td>30.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>how_to_contribute</td>\n",
       "      <td>20.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>history</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>copyright_information</td>\n",
       "      <td>18.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>funding_and_acknowledgements</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>visual_structure_of_repo</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>recommended_citation</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>archived_version_and_upcoming_version</td>\n",
       "      <td>26.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>resources</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>description_of_terms</td>\n",
       "      <td>29.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>best_practices_guide_to_apply_standard</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                           GitHub_elements count\n",
       "0                          getting_started  11.0\n",
       "1                            about_section  30.0\n",
       "2                        how_to_contribute  20.0\n",
       "3                                  history  10.0\n",
       "4                    copyright_information  18.0\n",
       "5             funding_and_acknowledgements   7.0\n",
       "6                 visual_structure_of_repo   6.0\n",
       "7                     recommended_citation  10.0\n",
       "8    archived_version_and_upcoming_version  26.0\n",
       "9                                resources   6.0\n",
       "10                    description_of_terms  29.0\n",
       "11  best_practices_guide_to_apply_standard   6.0"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_pages_df = pd.DataFrame({'GitHub_elements':ax_new_labels.index, 'count':ax_new_labels.values})\n",
    "all_pages_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Calculate percent out of `32` total github repos.  \n",
    "**Note**: I include code in the denominator that adjusts to the number of repositories in dataframe. For this manuscript, we had 32 repositories."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "32"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(all_pages.index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                           GitHub_elements count percent\n",
      "0                          getting_started  11.0  34.375\n",
      "1                            about_section  30.0   93.75\n",
      "2                        how_to_contribute  20.0    62.5\n",
      "3                                  history  10.0   31.25\n",
      "4                    copyright_information  18.0   56.25\n",
      "5             funding_and_acknowledgements   7.0  21.875\n",
      "6                 visual_structure_of_repo   6.0   18.75\n",
      "7                     recommended_citation  10.0   31.25\n",
      "8    archived_version_and_upcoming_version  26.0   81.25\n",
      "9                                resources   6.0   18.75\n",
      "10                    description_of_terms  29.0  90.625\n",
      "11  best_practices_guide_to_apply_standard   6.0   18.75\n"
     ]
    }
   ],
   "source": [
    "all_pages_df['percent'] = (all_pages_df['count'] / len(all_pages.index)) * 100\n",
    "print(all_pages_df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Sort dataframe based on percentages from highest to lowest. This will make bars ordered from highest to lowest in figure."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                           GitHub_elements count percent\n",
      "1                            about_section  30.0   93.75\n",
      "10                    description_of_terms  29.0  90.625\n",
      "8    archived_version_and_upcoming_version  26.0   81.25\n",
      "2                        how_to_contribute  20.0    62.5\n",
      "4                    copyright_information  18.0   56.25\n",
      "0                          getting_started  11.0  34.375\n",
      "3                                  history  10.0   31.25\n",
      "7                     recommended_citation  10.0   31.25\n",
      "5             funding_and_acknowledgements   7.0  21.875\n",
      "6                 visual_structure_of_repo   6.0   18.75\n",
      "9                                resources   6.0   18.75\n",
      "11  best_practices_guide_to_apply_standard   6.0   18.75\n"
     ]
    }
   ],
   "source": [
    "result_all_pages = all_pages_df.sort_values('percent', ascending=False)\n",
    "print(result_all_pages)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, make plot using seaborn package, specifying that the context of the figure will be in a `paper`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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.set_style(\"white\")\n",
    "sns.set_context(\"paper\", font_scale=1.7)\n",
    "all_pages_barplot = sns.barplot(x=\"GitHub_elements\", \n",
    "                                y = \"percent\", \n",
    "                                data = all_pages_df, \n",
    "                                color=\"#1F968BFF\",\n",
    "                               order = result_all_pages['GitHub_elements'])\n",
    "all_pages_barplot.set(xlabel = \"GitHub site elements\", \n",
    "                      ylabel = \"Percent of sites\")\n",
    "all_pages_barplot.set_xticklabels(all_pages_barplot.get_xticklabels(), \n",
    "                                  rotation=90)\n",
    "all_pages_barplot.set_xticklabels(['about','terms','version','contribute','license','history','citation','getting started','funding','visual structure','resources','use case guide'])\n",
    "sns.despine(top=True)\n",
    "plt.tight_layout(h_pad=2)\n",
    "plt.ylim(0,100)\n",
    "#plt.savefig('figures/all__repositories.jpg', dpi=300)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "sysrevenv",
   "language": "python",
   "name": "sysrevenv"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}