contribution_guidelines.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Script for analyzing and plotting contribution guidelines\n",
    "In this script, we summarize the ways that organizations hosting data standard content on GitHub ask their users for feedback."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First, import libraries we need for this analysis and plotting."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "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": [
    "Read in data for analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "contribution_pages = pd.read_csv('data/contribution_guidelines.csv', header=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Inspect data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
       "    .dataframe thead th {\n",
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       "</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>mention_contributing</th>\n",
       "      <th>outline_for_contributing_workflow</th>\n",
       "      <th>instructions_for_branching</th>\n",
       "      <th>github_tutorial</th>\n",
       "      <th>issue_template</th>\n",
       "      <th>code_of_conduct</th>\n",
       "      <th>notes</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>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</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>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</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>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>MIxS</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>IGSN</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",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     organization_or_project_name  mention_contributing  \\\n",
       "0                     Darwin Core                   1.0   \n",
       "1                             EML                   1.0   \n",
       "2  Ecological Trait Data Standard                   1.0   \n",
       "3                            MIxS                   1.0   \n",
       "4                            IGSN                   1.0   \n",
       "\n",
       "   outline_for_contributing_workflow  instructions_for_branching  \\\n",
       "0                                NaN                         NaN   \n",
       "1                                1.0                         1.0   \n",
       "2                                NaN                         NaN   \n",
       "3                                NaN                         NaN   \n",
       "4                                NaN                         NaN   \n",
       "\n",
       "   github_tutorial  issue_template  code_of_conduct notes  \n",
       "0              NaN             1.0              NaN   NaN  \n",
       "1              NaN             NaN              NaN   NaN  \n",
       "2              NaN             1.0              NaN   NaN  \n",
       "3              NaN             1.0              NaN   NaN  \n",
       "4              NaN             NaN              NaN   NaN  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "contribution_pages.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Delete the notes column since this is not part of our analysis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "del contribution_pages['notes']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ensure that notes column was deleted."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>organization_or_project_name</th>\n",
       "      <th>mention_contributing</th>\n",
       "      <th>outline_for_contributing_workflow</th>\n",
       "      <th>instructions_for_branching</th>\n",
       "      <th>github_tutorial</th>\n",
       "      <th>issue_template</th>\n",
       "      <th>code_of_conduct</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>NaN</td>\n",
       "      <td>NaN</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>NaN</td>\n",
       "      <td>NaN</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>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>MIxS</td>\n",
       "      <td>1.0</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",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>IGSN</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",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     organization_or_project_name  mention_contributing  \\\n",
       "0                     Darwin Core                   1.0   \n",
       "1                             EML                   1.0   \n",
       "2  Ecological Trait Data Standard                   1.0   \n",
       "3                            MIxS                   1.0   \n",
       "4                            IGSN                   1.0   \n",
       "\n",
       "   outline_for_contributing_workflow  instructions_for_branching  \\\n",
       "0                                NaN                         NaN   \n",
       "1                                1.0                         1.0   \n",
       "2                                NaN                         NaN   \n",
       "3                                NaN                         NaN   \n",
       "4                                NaN                         NaN   \n",
       "\n",
       "   github_tutorial  issue_template  code_of_conduct  \n",
       "0              NaN             1.0              NaN  \n",
       "1              NaN             NaN              NaN  \n",
       "2              NaN             1.0              NaN  \n",
       "3              NaN             1.0              NaN  \n",
       "4              NaN             NaN              NaN  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "contribution_pages.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Calculate sum for each of the different contribution elements"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "organization_or_project_name         Darwin CoreEMLEcological Trait Data StandardMI...\n",
      "mention_contributing                                                              18.0\n",
      "outline_for_contributing_workflow                                                 10.0\n",
      "instructions_for_branching                                                         8.0\n",
      "github_tutorial                                                                    3.0\n",
      "issue_template                                                                     8.0\n",
      "code_of_conduct                                                                    3.0\n",
      "dtype: object\n"
     ]
    }
   ],
   "source": [
    "contribution_pages_totals = contribution_pages.sum()\n",
    "print(contribution_pages_totals)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Drop the header since we rename in next step."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "mention_contributing                 18.0\n",
       "outline_for_contributing_workflow    10.0\n",
       "instructions_for_branching            8.0\n",
       "github_tutorial                       3.0\n",
       "issue_template                        8.0\n",
       "dtype: object"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "contribution_new_labels = contribution_pages_totals.drop(\"organization_or_project_name\")\n",
    "contribution_new_labels.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Rename header and conver to a dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Contributing_elements</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
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       "      <td>8.0</td>\n",
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       "      <th>3</th>\n",
       "      <td>github_tutorial</td>\n",
       "      <td>3.0</td>\n",
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       "      <th>4</th>\n",
       "      <td>issue_template</td>\n",
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       "</table>\n",
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      ],
      "text/plain": [
       "               Contributing_elements count\n",
       "0               mention_contributing  18.0\n",
       "1  outline_for_contributing_workflow  10.0\n",
       "2         instructions_for_branching   8.0\n",
       "3                    github_tutorial   3.0\n",
       "4                     issue_template   8.0\n",
       "5                    code_of_conduct   3.0"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "contributing_pages_df = pd.DataFrame({'Contributing_elements':contribution_new_labels.index, 'count':contribution_new_labels.values})\n",
    "contributing_pages_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create a new column showing percentages of each contributing element"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "               Contributing_elements count percent\n",
      "0               mention_contributing  18.0   56.25\n",
      "1  outline_for_contributing_workflow  10.0   31.25\n",
      "2         instructions_for_branching   8.0    25.0\n",
      "3                    github_tutorial   3.0   9.375\n",
      "4                     issue_template   8.0    25.0\n",
      "5                    code_of_conduct   3.0   9.375\n"
     ]
    }
   ],
   "source": [
    "contributing_pages_df['percent'] = (contributing_pages_df['count'] / len(contribution_pages.index)) * 100\n",
    "print(contributing_pages_df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Prepare for plotting by soring the percents from max to min"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "               Contributing_elements count percent\n",
      "0               mention_contributing  18.0   56.25\n",
      "1  outline_for_contributing_workflow  10.0   31.25\n",
      "2         instructions_for_branching   8.0    25.0\n",
      "4                     issue_template   8.0    25.0\n",
      "3                    github_tutorial   3.0   9.375\n",
      "5                    code_of_conduct   3.0   9.375\n"
     ]
    }
   ],
   "source": [
    "result = contributing_pages_df.sort_values('percent', ascending=False)\n",
    "print(result)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Make bar plot using `seaborn` package"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "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=1.6)\n",
    "contributing_barplot = sns.barplot(x=\"Contributing_elements\", \n",
    "                                y = \"percent\", \n",
    "                                data = contributing_pages_df, \n",
    "                                color=\"maroon\",\n",
    "                               order = result['Contributing_elements'])\n",
    "contributing_barplot.set(xlabel = \"Contributing elements\", \n",
    "                      ylabel = \"Percent of sites\")\n",
    "contributing_barplot.set_xticklabels(contributing_barplot.get_xticklabels(), \n",
    "                                  rotation=90)\n",
    "contributing_barplot.set_xticklabels(['mention contributing','workflow','branching','issue templates','GitHub tutorial','code of conduct'])\n",
    "sns.despine(top=True)\n",
    "plt.ylim(0,100)\n",
    "plt.tight_layout(h_pad=2)\n",
    "#plt.savefig('figures/contributing.jpg', dpi=300)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Analysis of repositories that ask for issues and/or PRs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, import issues vs. pull request data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "issue_and_pr = pd.read_csv('data/issues_vs_prs.csv', header=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Calcualte the sum of different feedback options."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "organization_or_project_name     Darwin CoreEMLEcological Trait Data StandardMI...\n",
      "only_allows_issues                                                             9.0\n",
      "both_pull_requests_and_issues                                                 13.0\n",
      "unclear_how_to_contribute                                                     10.0\n",
      "dtype: object\n"
     ]
    }
   ],
   "source": [
    "issue_and_pr_totals = issue_and_pr.sum()\n",
    "print(issue_and_pr_totals)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Drop the header of the data frame since we will change this before plotting the results."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "only_allows_issues                9.0\n",
       "both_pull_requests_and_issues    13.0\n",
       "unclear_how_to_contribute        10.0\n",
       "dtype: object"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "issues_new_labels = issue_and_pr_totals.drop(\"organization_or_project_name\")\n",
    "issues_new_labels.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Insptect the new dataframe once you have added a new header."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "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>ways_to_submit_issues</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>only_allows_issues</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>both_pull_requests_and_issues</td>\n",
       "      <td>13.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>unclear_how_to_contribute</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           ways_to_submit_issues count\n",
       "0             only_allows_issues   9.0\n",
       "1  both_pull_requests_and_issues  13.0\n",
       "2      unclear_how_to_contribute  10.0"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "issues_df = pd.DataFrame({'ways_to_submit_issues':issues_new_labels.index, 'count':issues_new_labels.values})\n",
    "issues_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Calculate percent of each method of providing feedback."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "           ways_to_submit_issues count percent\n",
      "0             only_allows_issues   9.0  28.125\n",
      "1  both_pull_requests_and_issues  13.0  40.625\n",
      "2      unclear_how_to_contribute  10.0   31.25\n"
     ]
    }
   ],
   "source": [
    "issues_df['percent'] = (issues_df['count'] / len(issue_and_pr.index)) * 100\n",
    "print(issues_df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Order results from high to low percentages."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "           ways_to_submit_issues count percent\n",
      "1  both_pull_requests_and_issues  13.0  40.625\n",
      "2      unclear_how_to_contribute  10.0   31.25\n",
      "0             only_allows_issues   9.0  28.125\n"
     ]
    }
   ],
   "source": [
    "result_issues = issues_df.sort_values('percent', ascending=False)\n",
    "print(result_issues)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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