05.unpaywall-trends.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Analyze access color trends using Unpaywall data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import collections\n",
    "\n",
    "import pandas\n",
    "import plotnine\n",
    "import numpy\n",
    "\n",
    "import utils"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>doi</th>\n",
       "      <th>crossref_type</th>\n",
       "      <th>journal_date</th>\n",
       "      <th>unpaywall_access</th>\n",
       "      <th>journal_access</th>\n",
       "      <th>journal_access_evidence</th>\n",
       "      <th>journal_access_license</th>\n",
       "      <th>journal_fully_oa</th>\n",
       "      <th>journal_year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>10.1080/21645515.2017.1330236</td>\n",
       "      <td>journal-article</td>\n",
       "      <td>2017-06-12</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>2017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>10.1088/0004-6256/135/4/1201</td>\n",
       "      <td>journal-article</td>\n",
       "      <td>2008-03-04</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>open (via free pdf)</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>2008</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                             doi    crossref_type journal_date  \\\n",
       "0  10.1080/21645515.2017.1330236  journal-article   2017-06-12   \n",
       "3   10.1088/0004-6256/135/4/1201  journal-article   2008-03-04   \n",
       "\n",
       "   unpaywall_access  journal_access journal_access_evidence  \\\n",
       "0                 1               0                     NaN   \n",
       "3                 1               1     open (via free pdf)   \n",
       "\n",
       "  journal_access_license  journal_fully_oa  journal_year  \n",
       "0                    NaN                 0          2017  \n",
       "3                    NaN                 0          2008  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "access_df = pandas.read_csv(\n",
    "    'data/02.unpaywall-access.tsv.xz',\n",
    "    sep='\\t',\n",
    "    #nrows=100_000,\n",
    ")\n",
    "access_df = access_df.query(\"crossref_type in @utils.get_crossref_types()\")\n",
    "access_df['journal_year'] = access_df.journal_date.str.slice(stop=4).astype('float').astype('Int64')\n",
    "access_df.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>doi</th>\n",
       "      <th>crossref_type</th>\n",
       "      <th>journal_date</th>\n",
       "      <th>unpaywall_access</th>\n",
       "      <th>journal_access</th>\n",
       "      <th>journal_access_evidence</th>\n",
       "      <th>journal_access_license</th>\n",
       "      <th>journal_fully_oa</th>\n",
       "      <th>journal_year</th>\n",
       "      <th>journal_access_category</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>10.1080/21645515.2017.1330236</td>\n",
       "      <td>journal-article</td>\n",
       "      <td>2017-06-12</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>2017</td>\n",
       "      <td>green</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>10.1088/0004-6256/135/4/1201</td>\n",
       "      <td>journal-article</td>\n",
       "      <td>2008-03-04</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>open (via free pdf)</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>2008</td>\n",
       "      <td>hybrid/bronze</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>10.1088/0022-3727/48/43/435001</td>\n",
       "      <td>journal-article</td>\n",
       "      <td>2015-09-29</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>2015</td>\n",
       "      <td>closed</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>10.2478/v10172-012-0058-8</td>\n",
       "      <td>journal-article</td>\n",
       "      <td>2012-01-01</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>open (via free pdf)</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>2012</td>\n",
       "      <td>hybrid/bronze</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>10.2307/40203576</td>\n",
       "      <td>journal-article</td>\n",
       "      <td>2001-01-01</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>2001</td>\n",
       "      <td>closed</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                              doi    crossref_type journal_date  \\\n",
       "0   10.1080/21645515.2017.1330236  journal-article   2017-06-12   \n",
       "3    10.1088/0004-6256/135/4/1201  journal-article   2008-03-04   \n",
       "4  10.1088/0022-3727/48/43/435001  journal-article   2015-09-29   \n",
       "5       10.2478/v10172-012-0058-8  journal-article   2012-01-01   \n",
       "6                10.2307/40203576  journal-article   2001-01-01   \n",
       "\n",
       "   unpaywall_access  journal_access journal_access_evidence  \\\n",
       "0                 1               0                     NaN   \n",
       "3                 1               1     open (via free pdf)   \n",
       "4                 0               0                     NaN   \n",
       "5                 1               1     open (via free pdf)   \n",
       "6                 0               0                     NaN   \n",
       "\n",
       "  journal_access_license  journal_fully_oa  journal_year  \\\n",
       "0                    NaN                 0          2017   \n",
       "3                    NaN                 0          2008   \n",
       "4                    NaN                 0          2015   \n",
       "5                    NaN                 0          2012   \n",
       "6                    NaN                 0          2001   \n",
       "\n",
       "  journal_access_category  \n",
       "0                   green  \n",
       "3           hybrid/bronze  \n",
       "4                  closed  \n",
       "5           hybrid/bronze  \n",
       "6                  closed  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "access_df['journal_access_category'] = 'closed'\n",
    "access_df.loc[access_df.unpaywall_access.astype(bool), 'journal_access_category'] = 'green'\n",
    "access_df.loc[access_df.journal_access.astype(bool), 'journal_access_category'] = 'hybrid/bronze'\n",
    "access_df.loc[access_df.journal_fully_oa.astype(bool), 'journal_access_category'] = 'gold'\n",
    "access_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "closed           70607556\n",
       "hybrid/bronze    14143545\n",
       "green             4156883\n",
       "gold              3325136\n",
       "Name: journal_access_category, dtype: int64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "access_df.journal_access_category.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "journal-article        73453254\n",
       "book-chapter           11733039\n",
       "proceedings-article     5406981\n",
       "reference-entry          789059\n",
       "report                   582956\n",
       "standard                 256910\n",
       "book-part                  6420\n",
       "book-section               4501\n",
       "Name: crossref_type, dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "access_df.crossref_type.value_counts()"
   ]
  },
  {
   "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>journal_year</th>\n",
       "      <th>gold</th>\n",
       "      <th>hybrid/bronze</th>\n",
       "      <th>green</th>\n",
       "      <th>closed</th>\n",
       "      <th>total</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>454</th>\n",
       "      <td>2009</td>\n",
       "      <td>107390</td>\n",
       "      <td>436328</td>\n",
       "      <td>195444</td>\n",
       "      <td>2086751</td>\n",
       "      <td>2825913</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>455</th>\n",
       "      <td>2010</td>\n",
       "      <td>145073</td>\n",
       "      <td>479806</td>\n",
       "      <td>200990</td>\n",
       "      <td>2304330</td>\n",
       "      <td>3130199</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>456</th>\n",
       "      <td>2011</td>\n",
       "      <td>176390</td>\n",
       "      <td>544841</td>\n",
       "      <td>205607</td>\n",
       "      <td>2275931</td>\n",
       "      <td>3202769</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>457</th>\n",
       "      <td>2012</td>\n",
       "      <td>224134</td>\n",
       "      <td>608263</td>\n",
       "      <td>214910</td>\n",
       "      <td>2259071</td>\n",
       "      <td>3306378</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>458</th>\n",
       "      <td>2013</td>\n",
       "      <td>278671</td>\n",
       "      <td>662716</td>\n",
       "      <td>231482</td>\n",
       "      <td>2432098</td>\n",
       "      <td>3604967</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>459</th>\n",
       "      <td>2014</td>\n",
       "      <td>334555</td>\n",
       "      <td>713612</td>\n",
       "      <td>237586</td>\n",
       "      <td>2479865</td>\n",
       "      <td>3765618</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>460</th>\n",
       "      <td>2015</td>\n",
       "      <td>377263</td>\n",
       "      <td>824789</td>\n",
       "      <td>236819</td>\n",
       "      <td>2441398</td>\n",
       "      <td>3880269</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>461</th>\n",
       "      <td>2016</td>\n",
       "      <td>411700</td>\n",
       "      <td>912739</td>\n",
       "      <td>257340</td>\n",
       "      <td>2556727</td>\n",
       "      <td>4138506</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>462</th>\n",
       "      <td>2017</td>\n",
       "      <td>441308</td>\n",
       "      <td>939334</td>\n",
       "      <td>232122</td>\n",
       "      <td>2841960</td>\n",
       "      <td>4454724</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>463</th>\n",
       "      <td>2018</td>\n",
       "      <td>320334</td>\n",
       "      <td>539472</td>\n",
       "      <td>89009</td>\n",
       "      <td>2027480</td>\n",
       "      <td>2976295</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     journal_year    gold  hybrid/bronze   green   closed    total\n",
       "454          2009  107390         436328  195444  2086751  2825913\n",
       "455          2010  145073         479806  200990  2304330  3130199\n",
       "456          2011  176390         544841  205607  2275931  3202769\n",
       "457          2012  224134         608263  214910  2259071  3306378\n",
       "458          2013  278671         662716  231482  2432098  3604967\n",
       "459          2014  334555         713612  237586  2479865  3765618\n",
       "460          2015  377263         824789  236819  2441398  3880269\n",
       "461          2016  411700         912739  257340  2556727  4138506\n",
       "462          2017  441308         939334  232122  2841960  4454724\n",
       "463          2018  320334         539472   89009  2027480  2976295"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def summarize(df):\n",
    "    row = collections.Counter()\n",
    "    counts = collections.Counter(df.journal_access_category)\n",
    "    for category in utils.access_categories_colors:\n",
    "        row[category] = counts[category]\n",
    "    row['total'] = len(df)\n",
    "    return pandas.Series(row)\n",
    "\n",
    "year_df = access_df.groupby('journal_year').apply(summarize).reset_index()\n",
    "year_df.journal_year = year_df.journal_year.astype(int)\n",
    "year_df = year_df.query(\"1950 <= journal_year <= 2018\")\n",
    "year_df.tail(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "year_df.to_csv('data/05.unpaywall-colors-by-year.tsv', sep='\\t', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>journal_year</th>\n",
       "      <th>total</th>\n",
       "      <th>access_category</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1950</td>\n",
       "      <td>133702</td>\n",
       "      <td>gold</td>\n",
       "      <td>454</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1951</td>\n",
       "      <td>143813</td>\n",
       "      <td>gold</td>\n",
       "      <td>481</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   journal_year   total access_category  count\n",
       "0          1950  133702            gold    454\n",
       "1          1951  143813            gold    481"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "year_melt_df = year_df.melt(id_vars=['journal_year', 'total'], var_name='access_category', value_name='count')\n",
    "year_melt_df.access_category = pandas.Categorical(\n",
    "    year_melt_df.access_category,\n",
    "    categories=reversed(list(utils.access_categories_colors))\n",
    ")\n",
    "year_melt_df.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<ggplot: (8777193441337)>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gg_counts = (\n",
    "    plotnine.ggplot(mapping=plotnine.aes(x='journal_year', y='count', fill='access_category'), data=year_melt_df) +\n",
    "    plotnine.geom_bar(stat='identity', size=0, color=None, width=1) +\n",
    "    plotnine.scale_x_continuous(breaks=numpy.arange(1960, 2011, 10), expand=(0, 0), name='') +\n",
    "    plotnine.scale_y_continuous(labels=lambda array: [f'{x:,.0f}' for x in array], name='', expand=(0,0)) +\n",
    "    plotnine.scale_fill_manual(values=utils.access_categories_colors) +\n",
    "    plotnine.theme_bw() +\n",
    "    plotnine.theme(legend_position=(0.3, 0.7), legend_title=plotnine.element_blank())\n",
    ")\n",
    "gg_counts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<ggplot: (-9223363259837533871)>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gg_percents = (\n",
    "    plotnine.ggplot(mapping=plotnine.aes(x='journal_year', y='count', fill='access_category'), data=year_melt_df) +\n",
    "    plotnine.geom_bar(stat='identity', position='fill', size=0, color=None, width=1) +\n",
    "    plotnine.scale_fill_manual(values=utils.access_categories_colors) +\n",
    "    plotnine.scale_x_continuous(breaks=numpy.arange(1960, 2011, 10), expand=(0, 0), name='') +\n",
    "    plotnine.scale_y_continuous(breaks=numpy.arange(0.1, 1, 0.2), labels=lambda array: [f'{x:.0%}' for x in array], expand=(0, 0), name='') +\n",
    "    plotnine.theme_bw() +\n",
    "    plotnine.theme(legend_position=(0.3, 0.7), legend_title=plotnine.element_blank())\n",
    ")\n",
    "gg_percents"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python [conda env:greenblack]",
   "language": "python",
   "name": "conda-env-greenblack-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}