readme_page_analysis.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Barplot of README page content"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Import the libraries for README page analysis."
   ]
  },
  {
   "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": [
    "Import the CSV file for readme pages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "readme_pages = pd.read_csv('data/readme_pages.csv', header=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Sum the number of elemenets across organizations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "organization_or_project_name             Darwin CoreEMLEcological Trait Data StandardMI...\n",
      "getting_started                                                                       12.0\n",
      "about_section                                                                         29.0\n",
      "how_to_contribute                                                                     16.0\n",
      "history                                                                                6.0\n",
      "copyright_information                                                                 12.0\n",
      "funding_and_acknowledgements                                                           7.0\n",
      "visual_structure_of_repo                                                               7.0\n",
      "recommended_citation                                                                  10.0\n",
      "archived_version_and_upcoming_version                                                 11.0\n",
      "resources                                                                              6.0\n",
      "dtype: object\n"
     ]
    }
   ],
   "source": [
    "readme_pages_totals = readme_pages.sum()\n",
    "print(readme_pages_totals)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Remove the header since we will replace the header in the next step."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "getting_started          12.0\n",
       "about_section            29.0\n",
       "how_to_contribute        16.0\n",
       "history                   6.0\n",
       "copyright_information    12.0\n",
       "dtype: object"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "readme_new_labels = readme_pages_totals.drop(\"organization_or_project_name\")\n",
    "readme_new_labels.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Convert the series to a dataframe, and add in new header for plotting."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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>README_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>12.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>about_section</td>\n",
       "      <td>29.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>how_to_contribute</td>\n",
       "      <td>16.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>history</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>copyright_information</td>\n",
       "      <td>12.0</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>7.0</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>11.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>resources</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         README_elements count\n",
       "0                        getting_started  12.0\n",
       "1                          about_section  29.0\n",
       "2                      how_to_contribute  16.0\n",
       "3                                history   6.0\n",
       "4                  copyright_information  12.0\n",
       "5           funding_and_acknowledgements   7.0\n",
       "6               visual_structure_of_repo   7.0\n",
       "7                   recommended_citation  10.0\n",
       "8  archived_version_and_upcoming_version  11.0\n",
       "9                              resources   6.0"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "readme_pages_df = pd.DataFrame({'README_elements':readme_new_labels.index, 'count':readme_new_labels.values})\n",
    "readme_pages_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Calculate the percent of each element compared to overall number of organizations."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                         README_elements count percent\n",
      "0                        getting_started  12.0    37.5\n",
      "1                          about_section  29.0  90.625\n",
      "2                      how_to_contribute  16.0    50.0\n",
      "3                                history   6.0   18.75\n",
      "4                  copyright_information  12.0    37.5\n",
      "5           funding_and_acknowledgements   7.0  21.875\n",
      "6               visual_structure_of_repo   7.0  21.875\n",
      "7                   recommended_citation  10.0   31.25\n",
      "8  archived_version_and_upcoming_version  11.0  34.375\n",
      "9                              resources   6.0   18.75\n"
     ]
    }
   ],
   "source": [
    "readme_pages_df['percent'] = (readme_pages_df['count'] / (len(readme_pages.index))) * 100\n",
    "print(readme_pages_df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Arrange dataframe into descending order for plotting."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                         README_elements count percent\n",
      "1                          about_section  29.0  90.625\n",
      "2                      how_to_contribute  16.0    50.0\n",
      "0                        getting_started  12.0    37.5\n",
      "4                  copyright_information  12.0    37.5\n",
      "8  archived_version_and_upcoming_version  11.0  34.375\n",
      "7                   recommended_citation  10.0   31.25\n",
      "5           funding_and_acknowledgements   7.0  21.875\n",
      "6               visual_structure_of_repo   7.0  21.875\n",
      "3                                history   6.0   18.75\n",
      "9                              resources   6.0   18.75\n"
     ]
    }
   ],
   "source": [
    "result = readme_pages_df.sort_values('percent', ascending=False)\n",
    "print(result)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Make plot using `seaborn` library"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.0, 100.0)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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",
    "readme_barplot = sns.barplot(x=\"README_elements\", \n",
    "                                y = \"percent\", \n",
    "                                data = readme_pages_df, \n",
    "                                color=\"#482677FF\",\n",
    "                               order = result['README_elements'])\n",
    "readme_barplot.set(xlabel = \"README page elements\", \n",
    "                      ylabel = \"Percent of READMEs\")\n",
    "readme_barplot.set_xticklabels(readme_barplot.get_xticklabels(), \n",
    "                                  rotation=90)\n",
    "readme_barplot.set_xticklabels(['about','contribute','citation','license','version','getting started','funding','visual structure','history','resources'])\n",
    "sns.despine(top=True)\n",
    "plt.tight_layout(h_pad=2)\n",
    "plt.ylim(0,100)\n",
    "#plt.savefig('figures/README_content.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
}