Quick analysis of the distribution of article processing charges (APC) of DOAJ journals

doaj-apc.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Distribution of article processing charges (APC) in DOAJ journals"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "% matplotlib inline\n",
    "import seaborn as sns\n",
    "mpl.style.use('fivethirtyeight')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Get DOAJ data (as CSV)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "doaj_raw = pd.read_csv('https://doaj.org/csv', dtype=object)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "doaj = doaj_raw.rename(columns={\n",
    "        'Journal title': 'journal',\n",
    "        'Publisher': 'publisher',\n",
    "        'Journal article processing charges (APCs)': 'apc',\n",
    "        'APC amount': 'apc_cost',\n",
    "        'Currency': 'currency',\n",
    "        'APC information URL': 'apc_info',\n",
    "        'Tick: Accepted after March 2014': 'doaj_tick',\n",
    "        'DOAJ Seal': 'doaj_seal'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "doaj = doaj[['journal', 'publisher', 'apc', 'apc_cost', 'currency', 'apc_info', 'doaj_tick', 'doaj_seal']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Bug fix: indicate journals which do NOT charge APCs: https://twitter.com/peterdesmet/status/775625199827972097"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "doaj.loc[doaj['apc'].isnull() & doaj['apc_info'].notnull(),['apc', 'apc_cost', 'currency']] = ['No', 0, 'EUR - Euro']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>journal</th>\n",
       "      <th>publisher</th>\n",
       "      <th>apc</th>\n",
       "      <th>apc_cost</th>\n",
       "      <th>currency</th>\n",
       "      <th>apc_info</th>\n",
       "      <th>doaj_tick</th>\n",
       "      <th>doaj_seal</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Revista de Microbiologia</td>\n",
       "      <td>Sociedade Brasileira de Microbiologia</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Anais da Academia Brasileira de Ciências</td>\n",
       "      <td>Academia Brasileira de Ciências</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                    journal  \\\n",
       "0                  Revista de Microbiologia   \n",
       "1  Anais da Academia Brasileira de Ciências   \n",
       "\n",
       "                               publisher  apc apc_cost currency apc_info  \\\n",
       "0  Sociedade Brasileira de Microbiologia  NaN      NaN      NaN      NaN   \n",
       "1        Academia Brasileira de Ciências  NaN      NaN      NaN      NaN   \n",
       "\n",
       "  doaj_tick doaj_seal  \n",
       "0        No        No  \n",
       "1        No        No  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "doaj.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9196"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(doaj)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Convert APC costs to Euro"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Convert costs to numeric\n",
    "doaj['apc_cost'] = pd.to_numeric(doaj['apc_cost'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Conversion rates for the top 10 currencies\n",
    "conversions_to_euro = {\n",
    "    'USD - US Dollar': 0.890235,\n",
    "    'EUR - Euro': 1,\n",
    "    'GBP - Pound Sterling': 1.18584,\n",
    "    'IDR - Rupiah': 0.0000675944,\n",
    "    'BRL - Brazilian Real': 0.273858,\n",
    "    'CHF - Swiss Franc': 0.915115,\n",
    "    'ZAR - Rand': 0.0624428,\n",
    "    'CNY - Yuan Renminbi': 0.133260,\n",
    "    'PLN - Zloty': 0.229815,\n",
    "    'UAH - Hryvnia': 0.0334397\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Convert APC to Euro\n",
    "doaj['apc_cost_euro'] = doaj['currency'].map(conversions_to_euro) * doaj['apc_cost']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>journal</th>\n",
       "      <th>publisher</th>\n",
       "      <th>apc</th>\n",
       "      <th>apc_cost</th>\n",
       "      <th>currency</th>\n",
       "      <th>apc_info</th>\n",
       "      <th>doaj_tick</th>\n",
       "      <th>doaj_seal</th>\n",
       "      <th>apc_cost_euro</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>apc</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>No</th>\n",
       "      <td>2786</td>\n",
       "      <td>2786</td>\n",
       "      <td>2786</td>\n",
       "      <td>2786</td>\n",
       "      <td>2786</td>\n",
       "      <td>2786</td>\n",
       "      <td>2786</td>\n",
       "      <td>2786</td>\n",
       "      <td>2786</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Yes</th>\n",
       "      <td>1661</td>\n",
       "      <td>1661</td>\n",
       "      <td>1661</td>\n",
       "      <td>1660</td>\n",
       "      <td>1660</td>\n",
       "      <td>1546</td>\n",
       "      <td>1661</td>\n",
       "      <td>1661</td>\n",
       "      <td>1609</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>not provided</th>\n",
       "      <td>4749</td>\n",
       "      <td>4749</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4749</td>\n",
       "      <td>4749</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              journal  publisher   apc  apc_cost  currency  apc_info  \\\n",
       "apc                                                                    \n",
       "No               2786       2786  2786      2786      2786      2786   \n",
       "Yes              1661       1661  1661      1660      1660      1546   \n",
       "not provided     4749       4749     0         0         0         0   \n",
       "\n",
       "              doaj_tick  doaj_seal  apc_cost_euro  \n",
       "apc                                                \n",
       "No                 2786       2786           2786  \n",
       "Yes                1661       1661           1609  \n",
       "not provided       4749       4749              0  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "doaj.groupby(doaj['apc'].replace(np.nan,'not provided')).count()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Keep journals with known APC costs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "doaj_with_known_apc = doaj[doaj['apc'].notnull() & doaj['apc_cost_euro'].notnull()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>journal</th>\n",
       "      <th>publisher</th>\n",
       "      <th>apc</th>\n",
       "      <th>apc_cost</th>\n",
       "      <th>currency</th>\n",
       "      <th>apc_info</th>\n",
       "      <th>doaj_tick</th>\n",
       "      <th>doaj_seal</th>\n",
       "      <th>apc_cost_euro</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Acta Mycologica</td>\n",
       "      <td>Polish Botanical Society</td>\n",
       "      <td>Yes</td>\n",
       "      <td>100.0</td>\n",
       "      <td>EUR - Euro</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Alfa: Revista de Lingüística</td>\n",
       "      <td>Universidade Estadual Paulista Júlio de Mesqui...</td>\n",
       "      <td>No</td>\n",
       "      <td>0.0</td>\n",
       "      <td>EUR - Euro</td>\n",
       "      <td>http://www.scielo.br/revistas/alfa/iaboutj.htm</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Archives Animal Breeding</td>\n",
       "      <td>Copernicus Publications</td>\n",
       "      <td>No</td>\n",
       "      <td>0.0</td>\n",
       "      <td>EUR - Euro</td>\n",
       "      <td>http://archives-animal-breeding.net/for_author...</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         journal  \\\n",
       "6                Acta Mycologica   \n",
       "13  Alfa: Revista de Lingüística   \n",
       "17      Archives Animal Breeding   \n",
       "\n",
       "                                            publisher  apc  apc_cost  \\\n",
       "6                            Polish Botanical Society  Yes     100.0   \n",
       "13  Universidade Estadual Paulista Júlio de Mesqui...   No       0.0   \n",
       "17                            Copernicus Publications   No       0.0   \n",
       "\n",
       "      currency                                           apc_info doaj_tick  \\\n",
       "6   EUR - Euro                                                NaN       Yes   \n",
       "13  EUR - Euro     http://www.scielo.br/revistas/alfa/iaboutj.htm       Yes   \n",
       "17  EUR - Euro  http://archives-animal-breeding.net/for_author...       Yes   \n",
       "\n",
       "   doaj_seal  apc_cost_euro  \n",
       "6         No          100.0  \n",
       "13        No            0.0  \n",
       "17       Yes            0.0  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "doaj_with_known_apc.head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Distribution of APC costs for all DOAJ journals\n",
    "\n",
    "For which APC information is known. This includes those who do not charge APC."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x11b6c8a90>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAkAAAAF/CAYAAAChXnbTAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11e4adcc0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig,ax = plt.subplots()\n",
    "sns.distplot(doaj_with_known_apc['apc_cost_euro'], kde=False, ax=ax)\n",
    "ax.set_xlabel('APC costs in €')\n",
    "ax.set_ylabel('Number of DOAJ journals')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Distribution of APC costs for those DOAJ journals who charge them"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x11bb51940>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAjoAAAF/CAYAAABANWOCAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\ncdarAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11ba08198>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig,ax = plt.subplots()\n",
    "sns.distplot(doaj_with_known_apc[doaj_with_known_apc['apc'] == 'Yes']['apc_cost_euro'], kde=False, rug=True, ax=ax)\n",
    "ax.set_xlabel('APC costs in €')\n",
    "ax.set_ylabel('Number of DOAJ journals')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.5.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}