cmrivers/open_access_impact.py

## open_access_impact.py
{
 "metadata": {
  "name": "open_access_impact_factor_Mar"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": "# Code notes\n\n* This code accompanies [this blog post](http://www.caitlinrivers.com/open-source.html).\n\n* Source data is available at [Journal Metrics](http://www.journalmetrics.com/values.php) and at [Directory of Open Access Journals](www.doaj.org/doaj?func=csv).\n\n* If you reuse any code or figures, please credit [Caitlin Rivers](www.caitlinrivers.com) and include a link to my work. I'd also love if you let me know @cmyeaton.\n\n* Plot colors:\n    * open access: _green_\n    * closed access: _blue/purple_\n    * combination or comparison: _magenta_"
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "from __future__ import division\n\nimport pandas as pd\n#pd.set_printoptions(max_rows=100, max_columns=10)\npd.set_option(\"display.max_rows\", 100)\npd.set_option(\"display.max_columns\", 10)\nimport numpy as np\nfrom scipy import stats\nimport matplotlib.pyplot as plt",
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 89
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "impact = pd.read_csv('../../SNIP_SJR_complete_1999_2011new_SNIP_and_SJR_v1_Oct_2012.csv')\nopen_access = pd.read_csv('../data/open_access_journals.csv')",
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 90
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": "The ISSN data are somewhat messy. The open_access ISSN numbers have a - in the middle. Some of the impact ISSNs have trailing spaces. "
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "def rm_issn_punc(x):\n    import re\n    punc = re.sub(\"(@[A-Za-z0-9]+)|([^0-9A-Za-z \\t])|(\\w+:\\/\\/\\S+)\",\" \", x)\n    space = ''.join(punc.split(\" \"))\n    return space\n\n\ndef strip_space(x):\n    try:\n        new = int(x)\n    except Exception as e:\n        new = str(x).strip(' ') \n    \n    return new\n\n\ndef membership(x):\n    blean = True\n    open_lst = np.array(open_access.issn)\n    if x in open_lst:\n        blean = False\n    return blean\n\n\nopen_access['issn_'] = open_access['ISSN'].map(rm_issn_punc)\nopen_access['issn'] = open_access['issn_'].map(strip_space)\n\nimpact['issn'] = impact['Print ISSN'].map(strip_space)\n\nimpact.issn = impact.issn.replace('nan', np.nan)\nopen_access.issn = open_access.issn.replace('nan', np.nan)\n\nimpact = impact.drop_duplicates(cols='issn')\nopen_access = open_access.drop_duplicates(cols='issn')\n\nimpact['closed'] = impact['issn'].map(membership)\nclosed = impact[impact.closed != False]",
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 91
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "impact.closed.value_counts()",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 92,
       "text": "True     28235\nFalse     2539"
      }
     ],
     "prompt_number": 92
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": "Merged the open access list and the impact db by issn number to avoid character encoding differences (e.g. see row 18 below)"
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "matches = pd.merge(impact, open_access, left_on=impact['issn'], right_on=['issn']).drop_duplicates()\nmatches = matches.dropna(how='all')\nmatches = matches.drop_duplicates(cols='Title')\nmatches['Country'] = matches['Country_x']\nmatches[['Source Title', 'Title']].head(20)",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Source Title</th>\n      <th>Title</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0 </th>\n      <td>                                AACL Bioflux</td>\n      <td> Aquaculture, Aquarium, Conservation & Legislation</td>\n    </tr>\n    <tr>\n      <th>1 </th>\n      <td>               Abstract and Applied Analysis</td>\n      <td>                     Abstract and Applied Analysis</td>\n    </tr>\n    <tr>\n      <th>2 </th>\n      <td>                                    Academia</td>\n      <td> Academia : Revista Latinoamericana de Administ...</td>\n    </tr>\n    <tr>\n      <th>3 </th>\n      <td>                                      ACIMED</td>\n      <td>                                            ACIMED</td>\n    </tr>\n    <tr>\n      <th>4 </th>\n      <td>                                        ACME</td>\n      <td> ACME : An International e-Journal for Critical...</td>\n    </tr>\n    <tr>\n      <th>5 </th>\n      <td>           Acoustical Science and Technology</td>\n      <td>                 Acoustical Science and Technology</td>\n    </tr>\n    <tr>\n      <th>6 </th>\n      <td>                              Acta Adriatica</td>\n      <td>                                    Acta Adriatica</td>\n    </tr>\n    <tr>\n      <th>7 </th>\n      <td>                 Acta Agriculturae Slovenica</td>\n      <td>                       Acta Agriculturae Slovenica</td>\n    </tr>\n    <tr>\n      <th>8 </th>\n      <td>                              Acta Amazonica</td>\n      <td>                                    Acta Amazonica</td>\n    </tr>\n    <tr>\n      <th>9 </th>\n      <td>                    Acta Biochimica Polonica</td>\n      <td>                          Acta Biochimica Polonica</td>\n    </tr>\n    <tr>\n      <th>10</th>\n      <td>                              Acta Bioethica</td>\n      <td>                                    Acta Bioethica</td>\n    </tr>\n    <tr>\n      <th>11</th>\n      <td>                   Acta Biologica Colombiana</td>\n      <td>                        Acta Biol\u00c3\u00b3gica Colombiana</td>\n    </tr>\n    <tr>\n      <th>12</th>\n      <td> Acta Biologica Cracoviensia Series Botanica</td>\n      <td>       Acta Biologica Cracoviensia Series Botanica</td>\n    </tr>\n    <tr>\n      <th>13</th>\n      <td>     Acta Bioquimica Clinica Latinoamericana</td>\n      <td>         Acta Bioqu\u00c3\u00admica Cl\u00c3\u00adnica Latinoamericana</td>\n    </tr>\n    <tr>\n      <th>14</th>\n      <td>                     Acta Botanica Brasilica</td>\n      <td>                           Acta Botanica Brasilica</td>\n    </tr>\n    <tr>\n      <th>15</th>\n      <td>                      Acta Botanica Croatica</td>\n      <td>                            Acta Botanica Croatica</td>\n    </tr>\n    <tr>\n      <th>16</th>\n      <td>                    Acta Botanica Malacitana</td>\n      <td>                          Acta Botanica Malacitana</td>\n    </tr>\n    <tr>\n      <th>17</th>\n      <td>                      Acta Botanica Mexicana</td>\n      <td>                           Acta Bot\u00c3\u00a1nica Mexicana</td>\n    </tr>\n    <tr>\n      <th>18</th>\n      <td>                   Acta Botanica Venezuelica</td>\n      <td>                       Acta Bot\u00c3\u00a1nica Venezu\u00c3\u00a9lica</td>\n    </tr>\n    <tr>\n      <th>19</th>\n      <td>                      Acta Chimica Slovenica</td>\n      <td>                            Acta Chimica Slovenica</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
       "output_type": "pyout",
       "prompt_number": 93,
       "text": "                                   Source Title  \\\n0                                  AACL Bioflux   \n1                 Abstract and Applied Analysis   \n2                                      Academia   \n3                                        ACIMED   \n4                                          ACME   \n5             Acoustical Science and Technology   \n6                                Acta Adriatica   \n7                   Acta Agriculturae Slovenica   \n8                                Acta Amazonica   \n9                      Acta Biochimica Polonica   \n10                               Acta Bioethica   \n11                    Acta Biologica Colombiana   \n12  Acta Biologica Cracoviensia Series Botanica   \n13      Acta Bioquimica Clinica Latinoamericana   \n14                      Acta Botanica Brasilica   \n15                       Acta Botanica Croatica   \n16                     Acta Botanica Malacitana   \n17                       Acta Botanica Mexicana   \n18                    Acta Botanica Venezuelica   \n19                       Acta Chimica Slovenica   \n\n                                                Title  \n0   Aquaculture, Aquarium, Conservation & Legislation  \n1                       Abstract and Applied Analysis  \n2   Academia : Revista Latinoamericana de Administ...  \n3                                              ACIMED  \n4   ACME : An International e-Journal for Critical...  \n5                   Acoustical Science and Technology  \n6                                      Acta Adriatica  \n7                         Acta Agriculturae Slovenica  \n8                                      Acta Amazonica  \n9                            Acta Biochimica Polonica  \n10                                     Acta Bioethica  \n11                         Acta Biol\u00c3\u00b3gica Colombiana  \n12        Acta Biologica Cracoviensia Series Botanica  \n13          Acta Bioqu\u00c3\u00admica Cl\u00c3\u00adnica Latinoamericana  \n14                            Acta Botanica Brasilica  \n15                             Acta Botanica Croatica  \n16                           Acta Botanica Malacitana  \n17                            Acta Bot\u00c3\u00a1nica Mexicana  \n18                        Acta Bot\u00c3\u00a1nica Venezu\u00c3\u00a9lica  \n19                             Acta Chimica Slovenica  "
      }
     ],
     "prompt_number": 93
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "print 'Closed: {0}, Open: {1}, Full list: {2}'.format(len(closed.issn.dropna().unique()), len(open_access.issn.dropna().unique()), len(impact.issn.dropna().unique()))",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": "Closed: 28234, Open: 8597, Full list: 30773\n"
      }
     ],
     "prompt_number": 94
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": "##Top impact journals overall\n\ndefinitions from http://www.journalmetrics.com/faq.php"
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": "**BY SNIP**\n\nSNIP, or Source-Normalized Impact per Paper, measures a source\u2019s contextual citation impact. It takes into account characteristics of the source's subject field, especially the frequency at which authors cite other papers in their reference lists, the speed at which citation impact matures, and the extent to which the database used in the assessment covers the field\u2019s literature. SNIP is the ratio of a source's average citation count per paper, and the \u2018citation potential\u2019 of its subject field. It aims to allow direct comparison of sources in different subject fields."
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "top_impact_all = impact[['Source Title', '2011 SNIP2']].copy()\ntop_impact_all = pd.DataFrame(top_impact_all.sort('2011 SNIP2', ascending=False).dropna(), columns=['Source Title', '2011 SNIP2'])\ntop_impact_all['2011 SJR2'] = impact['2011 SJR2']\ntop_impact_all['Difference'] = top_impact_all['2011 SNIP2'] - top_impact_all['2011 SJR2']",
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 95
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "top_impact_all.head(15)",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Source Title</th>\n      <th>2011 SNIP2</th>\n      <th>2011 SJR2</th>\n      <th>Difference</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>4874 </th>\n      <td>            CA - A Cancer Journal for Clinicians</td>\n      <td> 41.082</td>\n      <td> 24.976</td>\n      <td> 16.106</td>\n    </tr>\n    <tr>\n      <th>10365</th>\n      <td> Foundations and Trends in Information Retrieval</td>\n      <td> 32.028</td>\n      <td> 10.411</td>\n      <td> 21.617</td>\n    </tr>\n    <tr>\n      <th>26384</th>\n      <td>                       Reviews of Modern Physics</td>\n      <td> 22.129</td>\n      <td> 36.194</td>\n      <td>-14.065</td>\n    </tr>\n    <tr>\n      <th>110  </th>\n      <td>                           ACM Computing Surveys</td>\n      <td> 17.848</td>\n      <td>  9.926</td>\n      <td>  7.922</td>\n    </tr>\n    <tr>\n      <th>16434</th>\n      <td>                Journal of Engineering Education</td>\n      <td> 16.072</td>\n      <td>  1.358</td>\n      <td> 14.714</td>\n    </tr>\n    <tr>\n      <th>22052</th>\n      <td>                 New England Journal of Medicine</td>\n      <td> 14.971</td>\n      <td>  9.740</td>\n      <td>  5.231</td>\n    </tr>\n    <tr>\n      <th>25177</th>\n      <td>                   Progress in Materials Science</td>\n      <td> 13.535</td>\n      <td> 10.127</td>\n      <td>  3.408</td>\n    </tr>\n    <tr>\n      <th>2280 </th>\n      <td>                     Annual Review of Psychology</td>\n      <td> 12.013</td>\n      <td>  8.137</td>\n      <td>  3.876</td>\n    </tr>\n    <tr>\n      <th>1004 </th>\n      <td>                             Advances in Physics</td>\n      <td> 11.750</td>\n      <td> 26.216</td>\n      <td>-14.466</td>\n    </tr>\n    <tr>\n      <th>12309</th>\n      <td>       IEEE Communications Surveys and Tutorials</td>\n      <td> 11.584</td>\n      <td>  6.315</td>\n      <td>  5.269</td>\n    </tr>\n    <tr>\n      <th>23931</th>\n      <td>                                 Physics Reports</td>\n      <td> 11.363</td>\n      <td> 10.761</td>\n      <td>  0.602</td>\n    </tr>\n    <tr>\n      <th>5538 </th>\n      <td>                                Chemical Reviews</td>\n      <td> 11.350</td>\n      <td> 15.866</td>\n      <td> -4.516</td>\n    </tr>\n    <tr>\n      <th>16321</th>\n      <td>                  Journal of Economic Literature</td>\n      <td> 10.738</td>\n      <td> 13.121</td>\n      <td> -2.383</td>\n    </tr>\n    <tr>\n      <th>2257 </th>\n      <td>                     Annual Review of Immunology</td>\n      <td> 10.680</td>\n      <td> 31.166</td>\n      <td>-20.486</td>\n    </tr>\n    <tr>\n      <th>25160</th>\n      <td>       Progress in Energy and Combustion Science</td>\n      <td> 10.579</td>\n      <td>  6.430</td>\n      <td>  4.149</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
       "output_type": "pyout",
       "prompt_number": 96,
       "text": "                                          Source Title  2011 SNIP2  2011 SJR2  \\\n4874              CA - A Cancer Journal for Clinicians      41.082     24.976   \n10365  Foundations and Trends in Information Retrieval      32.028     10.411   \n26384                        Reviews of Modern Physics      22.129     36.194   \n110                              ACM Computing Surveys      17.848      9.926   \n16434                 Journal of Engineering Education      16.072      1.358   \n22052                  New England Journal of Medicine      14.971      9.740   \n25177                    Progress in Materials Science      13.535     10.127   \n2280                       Annual Review of Psychology      12.013      8.137   \n1004                               Advances in Physics      11.750     26.216   \n12309        IEEE Communications Surveys and Tutorials      11.584      6.315   \n23931                                  Physics Reports      11.363     10.761   \n5538                                  Chemical Reviews      11.350     15.866   \n16321                   Journal of Economic Literature      10.738     13.121   \n2257                       Annual Review of Immunology      10.680     31.166   \n25160        Progress in Energy and Combustion Science      10.579      6.430   \n\n       Difference  \n4874       16.106  \n10365      21.617  \n26384     -14.065  \n110         7.922  \n16434      14.714  \n22052       5.231  \n25177       3.408  \n2280        3.876  \n1004      -14.466  \n12309       5.269  \n23931       0.602  \n5538       -4.516  \n16321      -2.383  \n2257      -20.486  \n25160       4.149  "
      }
     ],
     "prompt_number": 96
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": "**BY SJR**\n\nSJR, or SCImago Journal Rank, is a measure of the scientific prestige of scholarly sources.\n\nSJR assigns relative scores to all of the sources in a citation network. Its methodology is inspired by the Google PageRank algorithm, in that not all citations are equal. A source transfers its own 'prestige', or status, to another source through the act of citing it. A citation from a source with a relatively high SJR is worth more than a citation from a source with a lower SJR."
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "top_impact_all.sort('2011 SJR2', ascending=False).dropna().head(15)",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Source Title</th>\n      <th>2011 SNIP2</th>\n      <th>2011 SJR2</th>\n      <th>Difference</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>26384</th>\n      <td>            Reviews of Modern Physics</td>\n      <td> 22.129</td>\n      <td> 36.194</td>\n      <td>-14.065</td>\n    </tr>\n    <tr>\n      <th>2257 </th>\n      <td>          Annual Review of Immunology</td>\n      <td> 10.680</td>\n      <td> 31.166</td>\n      <td>-20.486</td>\n    </tr>\n    <tr>\n      <th>1004 </th>\n      <td>                  Advances in Physics</td>\n      <td> 11.750</td>\n      <td> 26.216</td>\n      <td>-14.466</td>\n    </tr>\n    <tr>\n      <th>4874 </th>\n      <td> CA - A Cancer Journal for Clinicians</td>\n      <td> 41.082</td>\n      <td> 24.976</td>\n      <td> 16.106</td>\n    </tr>\n    <tr>\n      <th>2232 </th>\n      <td>        Annual Review of Biochemistry</td>\n      <td>  8.276</td>\n      <td> 23.856</td>\n      <td>-15.580</td>\n    </tr>\n    <tr>\n      <th>21729</th>\n      <td>                      Nature Genetics</td>\n      <td>  7.211</td>\n      <td> 19.919</td>\n      <td>-12.708</td>\n    </tr>\n    <tr>\n      <th>5311 </th>\n      <td>                                 Cell</td>\n      <td>  6.579</td>\n      <td> 19.779</td>\n      <td>-13.200</td>\n    </tr>\n    <tr>\n      <th>2265 </th>\n      <td>        Annual Review of Neuroscience</td>\n      <td>  7.609</td>\n      <td> 17.014</td>\n      <td> -9.405</td>\n    </tr>\n    <tr>\n      <th>2254 </th>\n      <td>            Annual Review of Genetics</td>\n      <td>  5.113</td>\n      <td> 16.628</td>\n      <td>-11.515</td>\n    </tr>\n    <tr>\n      <th>25646</th>\n      <td>       Quarterly Journal of Economics</td>\n      <td>  6.621</td>\n      <td> 16.230</td>\n      <td> -9.609</td>\n    </tr>\n    <tr>\n      <th>5538 </th>\n      <td>                     Chemical Reviews</td>\n      <td> 11.350</td>\n      <td> 15.866</td>\n      <td> -4.516</td>\n    </tr>\n    <tr>\n      <th>21732</th>\n      <td>                     Nature Materials</td>\n      <td>  7.960</td>\n      <td> 15.413</td>\n      <td> -7.453</td>\n    </tr>\n    <tr>\n      <th>2276 </th>\n      <td>       Annual Review of Plant Biology</td>\n      <td> 10.257</td>\n      <td> 14.740</td>\n      <td> -4.483</td>\n    </tr>\n    <tr>\n      <th>21709</th>\n      <td>                               Nature</td>\n      <td>  8.647</td>\n      <td> 14.548</td>\n      <td> -5.901</td>\n    </tr>\n    <tr>\n      <th>21731</th>\n      <td>                    Nature Immunology</td>\n      <td>  3.912</td>\n      <td> 14.286</td>\n      <td>-10.374</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
       "output_type": "pyout",
       "prompt_number": 97,
       "text": "                               Source Title  2011 SNIP2  2011 SJR2  Difference\n26384             Reviews of Modern Physics      22.129     36.194     -14.065\n2257            Annual Review of Immunology      10.680     31.166     -20.486\n1004                    Advances in Physics      11.750     26.216     -14.466\n4874   CA - A Cancer Journal for Clinicians      41.082     24.976      16.106\n2232          Annual Review of Biochemistry       8.276     23.856     -15.580\n21729                       Nature Genetics       7.211     19.919     -12.708\n5311                                   Cell       6.579     19.779     -13.200\n2265          Annual Review of Neuroscience       7.609     17.014      -9.405\n2254              Annual Review of Genetics       5.113     16.628     -11.515\n25646        Quarterly Journal of Economics       6.621     16.230      -9.609\n5538                       Chemical Reviews      11.350     15.866      -4.516\n21732                      Nature Materials       7.960     15.413      -7.453\n2276         Annual Review of Plant Biology      10.257     14.740      -4.483\n21709                                Nature       8.647     14.548      -5.901\n21731                     Nature Immunology       3.912     14.286     -10.374"
      }
     ],
     "prompt_number": 97
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": "## Language of open access journals"
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "open_lang = open_access.Language.value_counts().head(10)/len(open_access)*100\nopen_lang",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 98,
       "text": "English                55.810166\nSpanish                 6.036990\nPortuguese              3.315110\nSpanish, English        3.222054\nEnglish, French         1.395836\nPortuguese, English     1.291148\nPortuguese, Spanish     1.116669\nEnglish, Spanish        1.058509\nFrench                  1.046877\nEnglish                 0.849133"
      }
     ],
     "prompt_number": 98
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "plt.figure()\nopen_lang.plot(kind='bar', title='Most common languages, open source journals (%)', color='green', alpha=.3);\nplt.show()",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "display_data",
       "png": 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wjl/SP3m9sLAwBAUFAYBcL8tSrXHYCxYsQIcOHXDy5En4+PjA2toaUVFRWLly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\nhBAd8f/urUH4xfjdyQAAAABJRU5ErkJggg==\n"
      }
     ],
     "prompt_number": 99
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": "## Most common keywords in open access journals"
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "open_lang = open_access.Keyword.value_counts().head(15)/len(open_access)*100\nopen_lang",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 100,
       "text": "health sciences          0.988717\nmedicine                 0.523438\neducation                0.442015\nmathematics              0.418751\nmedical sciences         0.348959\npsychology               0.348959\nhuman sciences           0.325695\nphilosophy               0.279167\nbiological sciences      0.255903\nsocial sciences          0.232639\nchemistry                0.209375\nagricultural sciences    0.209375\nlaw                      0.162848\npublic health            0.162848\nengineering              0.151216"
      }
     ],
     "prompt_number": 100
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "open_lang.plot(kind='bar', title='Most common keywords, open source journals (%)', color='green', alpha=.3);",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "display_data",
       "png": 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Zy159FJ+8GYZhGH245q0AL8MwTFFwzVvB8IVQhmFelxLLJqdPn0azZs3QqFEj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\nuH37ttmR87a2NotPmlKDiW7xvnbtGsLCwrBjxw4AgMlk4mW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      }
     ],
     "prompt_number": 101
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "open_access.to_csv('open_access.csv')",
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 102
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": "## Breakdown by start year"
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "plt.figure()\nopen_access['Start Year'].hist(range=(1980, 2012), bins=30, color='green', alpha=.3)\nplt.title('Histogram of start year, open access journals');",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "display_data",
       "png": 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wYRQUFFjnT0hIwNfXl4CAALZt22ZIeCGEEMapcUGYPHkyXl5e7N27l7179+Lv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\nygAAAABJRU5ErkJggg==\n"
      }
     ],
     "prompt_number": 103
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": "##Oldest OA journals"
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "timeline = open_access.sort('Start Year')\ntimeline[['Title', 'Start Year', 'End Year']].head(10)",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Title</th>\n      <th>Start Year</th>\n      <th>End Year</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>914 </th>\n      <td> Bijdragen Tot de Taal-, Land- en Volkenkunde v...</td>\n      <td> 1853</td>\n      <td> 1948</td>\n    </tr>\n    <tr>\n      <th>6652</th>\n      <td>                  Psyche : A Journal of Entomology</td>\n      <td> 1874</td>\n      <td>  NaN</td>\n    </tr>\n    <tr>\n      <th>2616</th>\n      <td>                                  Fishery Bulletin</td>\n      <td> 1881</td>\n      <td>  NaN</td>\n    </tr>\n    <tr>\n      <th>1222</th>\n      <td> Bulletin of the American Museum of Natural His...</td>\n      <td> 1881</td>\n      <td>  NaN</td>\n    </tr>\n    <tr>\n      <th>7868</th>\n      <td>                     South African Medical Journal</td>\n      <td> 1884</td>\n      <td>  NaN</td>\n    </tr>\n    <tr>\n      <th>1225</th>\n      <td>     Bulletin of the Geological Society of Denmark</td>\n      <td> 1894</td>\n      <td>  NaN</td>\n    </tr>\n    <tr>\n      <th>5544</th>\n      <td>              Mem\u00c3\u00b3rias do Instituto Oswaldo Cruz.</td>\n      <td> 1909</td>\n      <td>  NaN</td>\n    </tr>\n    <tr>\n      <th>4561</th>\n      <td>                               Journal of Genetics</td>\n      <td> 1910</td>\n      <td>  NaN</td>\n    </tr>\n    <tr>\n      <th>1232</th>\n      <td>       Bulletin of the Medical Library Association</td>\n      <td> 1911</td>\n      <td> 2001</td>\n    </tr>\n    <tr>\n      <th>5773</th>\n      <td>                         Nieuwe West-Indische Gids</td>\n      <td> 1919</td>\n      <td> 1991</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
       "output_type": "pyout",
       "prompt_number": 104,
       "text": "                                                  Title  Start Year  End Year\n914   Bijdragen Tot de Taal-, Land- en Volkenkunde v...        1853      1948\n6652                   Psyche : A Journal of Entomology        1874       NaN\n2616                                   Fishery Bulletin        1881       NaN\n1222  Bulletin of the American Museum of Natural His...        1881       NaN\n7868                      South African Medical Journal        1884       NaN\n1225      Bulletin of the Geological Society of Denmark        1894       NaN\n5544               Mem\u00c3\u00b3rias do Instituto Oswaldo Cruz.        1909       NaN\n4561                                Journal of Genetics        1910       NaN\n1232        Bulletin of the Medical Library Association        1911      2001\n5773                          Nieuwe West-Indische Gids        1919      1991"
      }
     ],
     "prompt_number": 104
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": "## Fees"
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "fee = open_access['Publication fee'].value_counts()/len(open_access)*100\nfee",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 105,
       "text": "No                     66.418518\nYes                    27.939979\nConditional             2.954519\nInformation missing     2.512504"
      }
     ],
     "prompt_number": 105
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "fee.plot(kind='bar', title='Histogram of fee required (%)', color='green', alpha=.3, rot=0);",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "display_data",
       "png": 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Cc1h5zE6e0WexWjF6bmzYREQ6wYatgnNYecxOntFnsVoxem5s2EREOsGGrYJz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\ndOL/AQ6zC6nQiwqwAAAAAElFTkSuQmCC\n"
      }
     ],
     "prompt_number": 106
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": "# Comparing open and closed access"
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": " There are 6,059 open access journals that do not have a ranking, and will therefore not be included in the analysis."
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "len(open_access) - len(matches)",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 107,
       "text": "6059"
      }
     ],
     "prompt_number": 107
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": "### By discipline"
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "closed_field = closed[['Physical sciences', 'Life sciences', 'Social sciences']].count()/len(closed)\nopen_field = matches[['Physical sciences', 'Life sciences', 'Social sciences']].count()/len(matches)",
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 108
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "closed_field.plot(color='red', kind='bar', alpha=.7);",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "display_data",
       "png": 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siM7mcDgA/H61R6ILCUcxXvbqq6/ipZdeQkxMjNssl8Eug8PhwKZNm7Bp0yac\nOnUKTqcTiqLgqaee0ru0bm/t2rV4/vnnsXnzZvWKm4qiqDdGod/xiN3LrrnmGmzatAn+/v56l0Jt\nmDZtGqqrq5GSkuL2izc/P1/HqggAhg8fjsLCQowYMcLt3cHUGo/YvWzgwIE4duwYg10om82Gb7/9\nlsEh0EUXXYT4+Hj2pgMY7F4WEBCA2NhY3HTTTeqpWoqi4NVXX9W5MgKAUaNGYd26dTwTRqAbbrgB\n48ePx2233aa+F0RRFEycOFHnyuThKMbLlixZov5dURR1hnvffffpVxSpYmJisHv3bkRERLiFx/bt\n23WujHJycgC09ONMb775pg7VyMZg1wnvoCST1uUDTCaTV+sgOh8cVnkZ76Akm8lkQq9evbBx40aY\nTCb06dMHPPaRgXdQ6jgGu5fxDkqytXUHpXvuuUfnqgjgHZTOBYPdy3gHJdl4ByW5eAeljuNZMV7G\nOyjJxjsoycU7KHUcj9i9jHdQko13UJKLd1DqOJ4VQ3QW3kFJttOnT3MM0w4Gu5fk5eWhsLDQ7cUf\nF0VRsHz5ch2qIpLv7bffxuTJk/HKK6+4ncPueg/IzJkzdaxOJs7YveTee+8F0PY1R85+wwV538iR\nI7Fx40b07du3VT94oSl9NTY2Amg50YDPlY7hEbuXHTt2DL1791Zvv9bU1ISTJ0+qZ2EQEZ0vvnjq\nZTfeeCNOnDihrhsbG3HTTTfpWBGdqayszO3o/OjRoygvL9exInK577773G4E/8svv2DKlCk6ViQX\ng93LTpw4gb59+6prf39/nictyLRp09z606dPH561JMS2bdvcbgTfr18/t/sH0+8Y7F6WlJTkdmf1\n0tJSJCUl6VgRnalnz55obm5W183NzbykgBCRkZH47rvv1PWePXv4HgMNnLF72a5du/Dwww+jrq4O\nTqcTl1xyCRYuXAiz2ax3aQTg6aefhsPhwPTp0+F0OlFUVARfX1/MnTtX79K6vdWrV+ORRx7BjTfe\nCKfTibVr12LBggVIT0/XuzRxGOw6qa2thaIobpcXIP3V19ejqKgIy5Ytg9PpxK233oq8vDwEBwfr\nXRqh5TWp//3vfwCAsWPH4uKLL9a5IpkY7F724YcfIjMzEwEBAZg/fz62bt2Kxx9/HFdeeaXepRGJ\ntnfvXkRERMDPzw9bt27Frl27cPvtt8PHh2dtn40zdi979tlnERAQgB07dmDp0qUYNWoUZsyYoXdZ\n9JtZs2ZTW55IAAAIzElEQVSpZ8XccccduPrqq91eEyH9TJw4ET4+Pqirq8Ntt90Gm83Gs2I0MNi9\nzNfXF0DLnZQefvhhZGdn4+DBgzpXRS6ff/45AgICsGrVKiiKgnXr1uHll1/WuyxCyxvFfHx88Oab\nb8JisWDhwoWoqqrSuyyR+H8YLxs6dCgmT56MiooKPPfcczh58iSampr0Lot+47r+yDvvvIP7778f\nl112mdu506Sf8PBwvPHGGygpKcGaNWsAwO09IfQ7HrF72X/+8x9MmTIF69evh5+fH3755Re89NJL\nepdFv7nrrrsQHR2N/fv3IyMjA3V1dejVq5feZRFaboJy4MABzJs3D2FhYdi3bx8mT56sd1ki8cVT\norM0NjaqZ1scP34cdrsdYWFhOldF1HEMdiIig+EohojIYBjsREQGw7NivKSt63y78HrfspSXl2Pd\nunX4+9//jv3796O2thaJiYl6l9VttXVzGhfepKZtnLETneH555/Ht99+iy1btqCqqgqHDx9Geno6\nvv76a71L67asVqvHx1NTU71Sx4WER+w6aWhoQENDg7oeOHCgjtWQS2lpKTZu3Ij4+HgAQHBwMH79\n9Vedq+reGNznjjN2L1u/fj1GjRqFiIgIDBs2DCaTCWPGjNG7LPrN5Zdf7hbkVVVVuOqqq3SsiFyq\nq6vxxBNPYNiwYRg0aBAGDRqEqKgovcsSicHuZS+88ALeeustXHHFFairq0NJSQmSk5P1Lot+Y7FY\nkJWVhbq6Otx///3IysrCI488ondZhJZLKsfFxeH06dP4+OOPMWbMGDz44IN6lyUSZ+xeNmzYMGze\nvBl//vOfsXr1avj7+8NsNvOaFzrbt28fBg0aBKDlDUqfffYZmpubcfPNN6N37946V0cAEBcXhy1b\ntmDo0KHqax7Dhw/Htm3bdK5MHs7YvSw4OBh2ux1jxozBpEmTEBERwZtsCDBp0iR88803GD16NL74\n4gvceuutepdEZ+nduzeampqQkpKC559/HoMGDXK7jSH9jkfsXnb8+HH4+fmhZ8+esFqtqKmpwfjx\n49GnTx+9S+vWRo8ejRtuuAGLFy/GzJkz3W6HpygKZs6cqWN1BAAVFRUwm804ceIE5s+fj5qaGkyf\nPh1DhgzRuzRxeMTuZXV1dQgLC0Pv3r2RmpqKEydO4KeffmKw62zp0qVYtmwZmpqaeHNxoVzvJfD3\n98ecOXP0LUY4HrF7WXx8PDZt2qReHvbUqVMYOXIkz5MW4rPPPsNf/vIXvcugM+Tl5aGwsLDNNyrx\nDUpt4xG7lzU3N6uhDrRc/5vnSevv7bffxuTJk7Fr1y5UVVVxFCPIvffeCwDIz89v9ZjWu7m7Owa7\nl6WlpWH+/PmYOnUqnE4nXn/9dYwePVrvsrq9xsZGAIDdbmdYCON6s9jw4cPRu3dv9OzZEwDQ1NSE\nkydP6lmaWBzFeNmBAwcwe/ZsrFu3Dk6nE6NGjcJzzz2Hyy+/XO/SSMO//vUvPProo3qX0e1dd911\nWLt2rXomjN1uR0ZGBr766iudK5OHwa4Th8MB4Pd7oJJcAwYMwIEDB/Quo9sbOnRoq3PWr732WuzY\nsUOniuTiKMZLXDPcV155xe2/+k6nkzNcog5ISkrCihUrcPPNNwNoua5PUlKSzlXJxGD3Eq0ZrivY\nicizGTNm4OGHH8bjjz8Op9OJSy65BAsXLtS7LJE4iiGC5+vlNzY2oqmpycsVkZaffvoJAHDppZfq\nXIlcvAiYlz3++OPqTTXuuOMOXH311VixYoXOVdGxY8dgt9vb/MNQl+H06dP47LPPMGfOHDzzzDNY\ntWoVTp8+rXdZIjHYvWz16tUICAjAqlWroCgK1q1bh5dfflnvsojEKywsRHFxMdLS0pCamopFixah\nsLBQ77JE4ozdy1xvTnrnnXdw//3347LLLsORI0d0ropIvvfffx82m0292mZWVhaSk5PbfONSd8cj\ndi+76667EB0djf379yMjIwN1dXXo1auX3mURiWcymbB9+3Z1vWPHDphMJv0KEowvnurgxIkT6lHH\n8ePHYbfbERYWpnNVRLJ98803ePDBB9X3gPTq1QsLFy5U35lKv2Owe1l8fDymTJmCu+66C/369dO7\nHKILzsGDBwEAl112mc6VyMVRjJe9//77qKmpQUJCAu68806sXr0a/N1KpK2iogI//vijut66dSv+\n9re/YcGCBer7Q8gdj9h10tzcjBUrVuChhx5Cjx49MG3aNMycOZO3YSM6S1xcHL744gsEBwfj+++/\nx8iRI/HCCy+grKwMfn5+KCgo0LtEcXjEroNt27Zh5syZmDVrFm699VZ89NFHUBQF6enpepdGJE5T\nUxOCg4MBAK+++ipycnKQk5ODf//739i0aZPO1cnE0x29LD4+HoGBgXjggQcwb948+Pn5AWi5cl1l\nZaXO1RHJ069fPzQ2NuLiiy/Gp59+iv/+978AAB8fHxw7dkzn6mRisHvZhx9+iCuuuKLNxz7++GMv\nV0Mk3z333IPrrrsOl1xyCa644gokJCQAAL777jsEBQXpXJ1MnLF7mcPhwKZNm7Bp0yb1JgGKouCp\np57SuTIiuQ4ePIg9e/YgJSVFvabPnj17cOzYMQwbNkzn6uThEbuXTZ8+HdXV1UhJSVFvGEBEnl12\n2WWtTm+86qqrdKpGPh6xe1lMTAy+/fZb9OjB162JqGswXbxs1KhRWLdund5lEJGB8YjdS6699loA\nLeevV1VVISIiQn3hR1EUt2tgEBGdDwa7l1RXVwNoCfG2/sl5MSMi6iwMdi9xOBxYvXo1NmzYgIyM\nDKSkpHDOTkRdgsniJU8++SQWLFiA/v37Y+7cuXwbNBF1GR6xe0l8fDzKysrg6+uLI0eO4JZbbsH6\n9ev1LouIDIhH7F7S3NwMX19fAEBQUJB631Mios7GI3Yv6dmzJy6++GJ1febNNhRFYdATUadhsBMR\nGQxHMUREBsNgJyIyGAY7EZHBMNiJiAyGwU5EZDAMdiIig2GwExEZzP8D4r/MMjWy57AAAAAASUVO\nRK5CYII=\n"
      }
     ],
     "prompt_number": 109
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "tmp = pd.DataFrame(open_field, columns=['Open access'])\ntmp['Closed access'] = closed_field",
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 110
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "plt.figure()\ntmp.plot(kind='bar', color=['green', 'red'], alpha=.5, title='Comparison of discipline', rot=0);",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "display_data",
       "png": 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gODiY/v37s3Xr1gborhBCiLo02JeniYmJxMfH891336nPHThwABsbG7Kysujb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\niIEQZ6A0AAAAAElFTkSuQmCC\n"
      }
     ],
     "prompt_number": 111
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": "### By country of origin"
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "countries = pd.DataFrame(closed.Country.value_counts(), columns=['closed'])\ncountries['open'] = open_access.Country.value_counts()\ncountries['proportion_oa'] = countries['open']/countries['closed']",
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 112
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "countries_sorted = countries.sort('proportion_oa', ascending=False)\ncountries_sorted[countries_sorted.proportion_oa >= 0][:10]",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>closed</th>\n      <th>open</th>\n      <th>proportion_oa</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>Colombia</th>\n      <td>   6</td>\n      <td> 208</td>\n      <td> 34.666667</td>\n    </tr>\n    <tr>\n      <th>Costa Rica</th>\n      <td>   1</td>\n      <td>  26</td>\n      <td> 26.000000</td>\n    </tr>\n    <tr>\n      <th>Egypt</th>\n      <td>  20</td>\n      <td> 351</td>\n      <td> 17.550000</td>\n    </tr>\n    <tr>\n      <th>Chile</th>\n      <td>  17</td>\n      <td> 142</td>\n      <td>  8.352941</td>\n    </tr>\n    <tr>\n      <th>Indonesia</th>\n      <td>   6</td>\n      <td>  45</td>\n      <td>  7.500000</td>\n    </tr>\n    <tr>\n      <th>Peru</th>\n      <td>   4</td>\n      <td>  29</td>\n      <td>  7.250000</td>\n    </tr>\n    <tr>\n      <th>Cuba</th>\n      <td>   7</td>\n      <td>  50</td>\n      <td>  7.142857</td>\n    </tr>\n    <tr>\n      <th>Brazil</th>\n      <td> 121</td>\n      <td> 806</td>\n      <td>  6.661157</td>\n    </tr>\n    <tr>\n      <th>Venezuela</th>\n      <td>  16</td>\n      <td>  85</td>\n      <td>  5.312500</td>\n    </tr>\n    <tr>\n      <th>Tunisia</th>\n      <td>   2</td>\n      <td>  10</td>\n      <td>  5.000000</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
       "output_type": "pyout",
       "prompt_number": 113,
       "text": "            closed  open  proportion_oa\nColombia         6   208      34.666667\nCosta Rica       1    26      26.000000\nEgypt           20   351      17.550000\nChile           17   142       8.352941\nIndonesia        6    45       7.500000\nPeru             4    29       7.250000\nCuba             7    50       7.142857\nBrazil         121   806       6.661157\nVenezuela       16    85       5.312500\nTunisia          2    10       5.000000"
      }
     ],
     "prompt_number": 113
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "plt.figure()\ncountries.closed.head(10).plot(color='red', kind='bar', alpha=.5)\ncountries.open.head(10).plot(color='green', kind='bar', alpha=.6, title='Comparison of top journal producers (%)\\nGreen=Open access, red=Closed access', rot=30);",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "display_data",
       "png": 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KpUhNTYWDgwM2bNgAAMjKysL69esRHR2N8PBwBAcHC+eaNWsW5syZg4SEBMTE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\nAAAAAElFTkSuQmCC\n"
      }
     ],
     "prompt_number": 114
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "countries_sorted",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 115,
       "text": "<class 'pandas.core.frame.DataFrame'>\nIndex: 114 entries, Moldova, Republic of to Netherlands\nData columns:\nclosed           114  non-null values\nopen             91  non-null values\nproportion_oa    91  non-null values\ndtypes: float64(2), int64(1)"
      }
     ],
     "prompt_number": 115
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "countries_sorted.proportion_oa[countries_sorted.proportion_oa > 0].head(10).plot(kind='bar', color ='g', rot=30, alpha=.5,\ntitle ='Countries with highest proportion of OA journals (%)');",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "display_data",
       "png": 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JCUjmqnJHR0eYm5sDAExNTWFpaSk9Pvj6+kIZroQ5UrarA6E65ZDZsjJYme32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\nqysHDBjAd+/ecd++fTQ3N+fvv//ORo0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hISFYtGgRSpQogc6dO0vf89bl368tsL/pSBK/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\nOB4WSz3GxgAAAABJRU5ErkJggg==\n"
      }
     ],
     "prompt_number": 116
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": "### Comparing SNIP distribution"
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "snip_dist = pd.DataFrame(closed['2011 SNIP2'], columns=['2011 closed SNIP'])\nsnip_dist['2011 open SNIP'] = matches['2011 SNIP2']",
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 117
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": "All SNIP scores (outliers clipped)"
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "snip_dist[snip_dist['2011 closed SNIP'] <15].boxplot(sym='m+');",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "display_data",
       "png": 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CjUaDiIgImM1mbNmyBS+//DImTpyIUaNGoXXr1jh9+nSd17h7926jYkpLS0Na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\n"
      }
     ],
     "prompt_number": 118
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "snip_dist.describe()",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>2011 closed SNIP</th>\n      <th>2011 open SNIP</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>count</th>\n      <td> 16617.000000</td>\n      <td> 1906.000000</td>\n    </tr>\n    <tr>\n      <th>mean</th>\n      <td>     0.831351</td>\n      <td>    0.570842</td>\n    </tr>\n    <tr>\n      <th>std</th>\n      <td>     0.980973</td>\n      <td>    0.520289</td>\n    </tr>\n    <tr>\n      <th>min</th>\n      <td>     0.000000</td>\n      <td>    0.000000</td>\n    </tr>\n    <tr>\n      <th>25%</th>\n      <td>     0.266000</td>\n      <td>    0.203000</td>\n    </tr>\n    <tr>\n      <th>50%</th>\n      <td>     0.675000</td>\n      <td>    0.480500</td>\n    </tr>\n    <tr>\n      <th>75%</th>\n      <td>     1.118000</td>\n      <td>    0.806750</td>\n    </tr>\n    <tr>\n      <th>max</th>\n      <td>    41.082000</td>\n      <td>    4.814000</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
       "output_type": "pyout",
       "prompt_number": 119,
       "text": "       2011 closed SNIP  2011 open SNIP\ncount      16617.000000     1906.000000\nmean           0.831351        0.570842\nstd            0.980973        0.520289\nmin            0.000000        0.000000\n25%            0.266000        0.203000\n50%            0.675000        0.480500\n75%            1.118000        0.806750\nmax           41.082000        4.814000"
      }
     ],
     "prompt_number": 119
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": "### Comparing SJR distributions"
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "sjr_dist = pd.DataFrame(closed['2011 SJR2'], columns=['2011 closed SJR'])\nsjr_dist['2011 open SJR'] = matches['2011 SJR2']",
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 120
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": "Outliers clipped"
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "sjr_dist[sjr_dist['2011 closed SJR']<15].boxplot(sym='m+');",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "display_data",
       "png": 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PP4wTJ05AlmV88cUXTT5vwYIFmDJlCh544AG88847qKur8xif85qPi4vz6evT\nGyZ2N65du4ZHH30Ur776Kjp16gQgcEM4N23ahLlz56Jjx44A0KCHLIRATk4Oxo0bh5/85Cfo1asX\nxo4di5ycHPTr1w/5+fl49tlnUVBQgC5duuC///0vPv30Uzz00ENISkrCypUr8dlnn6GmpgZHjhzB\n1KlT0b59ezz22GPNxtK1a1fY7XasWLECFRUV6N+/P44fP97keVVVVUhKSoLJZEK7du3cvh9pR81r\nePv27bBarQgJCcGjjz6KL774QulpP/jgg+jRowfuueceJCYm4vPPP8fmzZuRn5+PoUOHYtiwYfj2\n22+xe/duAI7fEn/+858jJCQEo0ePbjaxp6en4+uvv8b06dOxdu1ajBgxotm43n//fSQkJGDQoEFY\nsWJFs7+RtEZM7M2ora3FlClT8Nhjj2Hy5MnK+aFDh+LkyZMAgJMnT2Lo0KF++TwhhMf/cI3XXBZC\nwGAwICYmBna7HQMHDsTjjz+ON998E/X19YiOjsbRo0dx9OhRHD9+HB9++GGL/0OnpaXhjTfewLx5\n87Bx48Ymj0dERODo0aO4cOECLl++jG3btrXo/Smw1L6GAfc/NFzPO38jqK+vh9VqVa7TU6dO4Ve/\n+hWAhh2bDh06oLq6utn3jYyMhNVqxa5du3Dp0iWcPXu2yXOmTZuGwsJC7Nq1C88//zxu3Ljh89en\nJ0zsjQghMGvWLPTv3x8LFy5s8NiwYcOwevVqVFVVYfXq1UhJSWnyWl9MnToVK1euVC668vJy5TGD\nwYDx48dj586duHjxotKzefDBB/Hdd9/BYDBgxowZWLBgAex2O2JiYnD33Xdj8+bNEEKgtrYWRUVF\nCAsLw+DBg7F582bU1tbi3XffbTaWo0eP4ttvvwUA3Lx5E4cOHUJqaqrb2Lt06YJVq1bhd7/7XZu5\nMRXstLiGJ06ciHXr1uHWrVvYuHEjUlNTlfs0O3bsQGlpKc6ePYuCggIMHz4c06ZNw+bNm5WS4IUL\nF1BaWur15+3YsUMpvxQXFyMkJAR9+vRp8jzn15OWloaJEydi+fLlPn19uiOogX379gmDwSAGDhwo\nJEkSkiSJnJwcIYQQV69eFQ899JDo3bu3mDx5srh27Zryuj59+ojo6GgRGRkpTCaTOHnypBBCiEWL\nFgmTySTatWsnTCaTyMrKavKZN27cEC+//LIYNGiQGDhwoNiyZYsQQgiz2SyuXLkihBBi48aNwmKx\niLS0NPHPRl4cAAABgUlEQVSvf/1LCCHEjh07xIABA0RSUpKYMGGCOH36tBBCiFOnTomnnnpKDBgw\nQCQkJIi3335bCCHEV199JR5++GGRkJAgMjMzRWJiYpNYcnNzxeDBg0X//v1FamqqeO6555THevXq\nJaqrq4UQQnTu3LnB6yZNmiQ2bNjgw784+ZsW13BFRYVYsmSJGDhwoMjIyBBnzpwRQgiRmZkpnnji\nCZGSkiKGDh0qcnNzldds3rxZPPjggyIxMVGkpqaKs2fPiq+//rrBdbls2bJmP++ZZ54R999/vxg4\ncKCYMGGC2L59uxBCiI8++kj8+te/FkIIkZ2dLebNm6e85ttvvxUmk0lcv37d539bvQjY1njUujjr\nmVzsjVoiKysLkZGRePbZZ1X5vKVLl+KnP/2pMq69rWIphn7Uhx9+iMceewx//OMftQ6FdEit+RgZ\nGRkoLi5Genq6Kp8XzNhjJyJqZdhjJyJqZZjYiYhaGSZ2IqJWhomdiKiVYWInImplmNiJiFqZ/w/L\nM6T+EKu2tAAAAABJRU5ErkJggg==\n"
      }
     ],
     "prompt_number": 121
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "sjr_dist.describe()",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>2011 closed SJR</th>\n      <th>2011 open SJR</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>count</th>\n      <td> 16881.000000</td>\n      <td> 1970.000000</td>\n    </tr>\n    <tr>\n      <th>mean</th>\n      <td>     0.639512</td>\n      <td>    0.383745</td>\n    </tr>\n    <tr>\n      <th>std</th>\n      <td>     1.142131</td>\n      <td>    0.558075</td>\n    </tr>\n    <tr>\n      <th>min</th>\n      <td>     0.000000</td>\n      <td>    0.000000</td>\n    </tr>\n    <tr>\n      <th>25%</th>\n      <td>     0.140000</td>\n      <td>    0.128000</td>\n    </tr>\n    <tr>\n      <th>50%</th>\n      <td>     0.319000</td>\n      <td>    0.217000</td>\n    </tr>\n    <tr>\n      <th>75%</th>\n      <td>     0.743000</td>\n      <td>    0.408750</td>\n    </tr>\n    <tr>\n      <th>max</th>\n      <td>    36.194000</td>\n      <td>    7.581000</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
       "output_type": "pyout",
       "prompt_number": 122,
       "text": "       2011 closed SJR  2011 open SJR\ncount     16881.000000    1970.000000\nmean          0.639512       0.383745\nstd           1.142131       0.558075\nmin           0.000000       0.000000\n25%           0.140000       0.128000\n50%           0.319000       0.217000\n75%           0.743000       0.408750\nmax          36.194000       7.581000"
      }
     ],
     "prompt_number": 122
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": "# Open access impact over time"
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "def find_snip(db):\n    snip_out = db[['1999 SNIP2', '2000 SNIP2', '2001 SNIP2', '2002 SNIP2', '2003 SNIP2', '2004 SNIP2', '2005 SNIP2', '2006 SNIP2',\n                      '2007 SNIP2', '2008 SNIP2', '2009 SNIP2', '2010 SNIP2', '2011 SNIP2']]\n    \n    return snip_out\n\n\ndef select_snip_by_era(country_db):\n    snip = find_snip(country_db)\n    \n    era1 = snip[country_db['Start Year'] < 1996]\n    era2 = snip[(country_db['Start Year'] >= 1996) & (country_db['Start Year'] <= 2001)]\n    era3 = snip[country_db['Start Year'] > 2001]\n    \n    return snip, era1, era2, era3\n\n\ndef find_sjr(db):\n    sjr_out = db[['1999 SJR2', '2000 SJR2', '2001 SJR2', '2002 SJR2', '2003 SJR2', '2004 SJR2', '2005 SJR2', '2006 SJR2',\n                      '2007 SJR2', '2008 SJR2', '2009 SJR2', '2010 SJR2', '2011 SJR2']]\n    return sjr_out\n\n\ndef select_sjr_by_era(country_db):\n    sjr = find_sjr(country_db)\n\n    era1 = sjr[country_db['Start Year'] < 1996]\n    era2 = sjr[(country_db['Start Year'] >= 1996) & (country_db['Start Year'] <= 2001)]\n    era3 = sjr[country_db['Start Year'] > 2001]\n    \n    return sjr, era1, era2, era3",
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 123
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "open_years = find_snip(matches)\nclosed_years = find_snip(closed)",
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 124
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "plt.figure()\nopen_years.median().plot(style='g');\nclosed_years.median().plot(style='--', title='Median SNIP score\\nGreen = Open access\\nRed = Closed access', rot=30, color='r');",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "display_data",
       "png": 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YNw54/hzYtatCr+pijFVC2dnAkiVAUBDw1VfyH7M6pbsGSgzHTqUKiZOTE5Yv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\n"
      }
     ],
     "prompt_number": 125
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "clean = pd.DataFrame(open_years.median(), columns=['open'])\nclean['closed'] = closed_years.median()\nclean['median_difference'] = clean.closed - clean.open",
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 126
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "plt.figure()\nclean.median_difference.plot(title='Median difference (closed median - open median)', rot=30, style='m');",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "display_data",
       "png": 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7We0hM+BzTBiTKjG0ndyRSFRRdBHuvXMPimwF3P/tDos+FkKXxBhrAmJoO3lo\nS0u0PU5r4m2Cbie7wXGxI6InRCN2WmyTDXdJfQya8+k2qecTA+5IJEwmk8F2gi16xvSEYRtDRHaO\nxIOND0AK3vNjjGkOD201IzzcxZj0iKHt5I6kmSEiZP6UifgF8bAaZAXXr1zRom0LoctijDWQGNpO\nHtrSErGM0zbFcJdYsjUVzqfbpJ5PDLgjaaYMzAzQ4esO8AnzQdavWbjS44pobpTFGNMtPLTFeLiL\nMR0mhraT90gYH93FGGsU7ki0RBfGaRs63KUL2RqD8+k2qecTA+5I2FO0eTIjY0z38RwJq5GiQIHk\nlclI387X7mJMjMTQdnJHwuqET2ZkTJzE0Hby0JaW6Po4bU3DXbqerTacT7dJPZ8YcEfC6uyZR3cp\neU+SseaMh7ZYg1UMd5GS4HPKBzJ9njthTNvE0HbyHglrsIrhLhCQtjVN6HIYYwLhjkRLpDpOK5PJ\nkD41HYlLEyF/JBe6nCYh1W1XgfOxxuKOhDWacQdj2L5ii8RPEoUuhTEmAJ4jYRqhyFXgkucldDnU\nBWbdzYQuh7FmQwxtJ++RMI0wsDSAyyqX8sl3Ff9BwFhzwh2Jlkh5nLYiW7tp7QAC0nekC1uQhkl5\n2wGcjzUedyRMY2R6Mrh/647ExYlQ5CqELocxpiU8R8I07s7MO9Az0oP7BnehS2FM8sTQdvIeCdM4\n11WuyNiTgcKbhUKXwhjTAu5ItETK47RPZjNsbQiXlf+beJfAXqaUtx3A+VjjcUfCmoTdDDsoi5TI\n2JMhdCmMsSbGcySsyeRdyMPtl2/DP8YfBuYGQpfDmCSJoe3kPRLWZCx6W8B6iDWSP0sWuhTGWBPi\njkRLpDxOW1M213+6In17Ooqii7RXkIZJedsBnI81HnckrEm1aNsCTp86IW5enOC734yxpsFzJKzJ\nkYJw2e8ynJc6o83LbYQuhzFJEUPbyXskrMnJDMrPeI+bHwdlkVLochhjGsYdiZZIeZy2Ltks+1vC\nop8Fklfp3sS7lLcdwPlY49WpIwkPD4eXlxfc3d2xcePGpx7fvXs3unXrhm7dumHSpEm4e/eu+jFn\nZ2d07doVvr6+8Pf311zlTOd0+LoD0oLT8PjeY6FLYYxpUJ3mSHx9fbFhwwY4OTlh6NChOHv2LGxs\nbNSPX7hwAd7e3rCwsMCOHTtw4sQJ/Oc//wEAuLi44MqVK7C2tq65EBGM87Gmd3/NfeScykGXQ10g\nk/E93hkY4Zo1AAAgAElEQVRrLDG0nbXukeTl5QEA+vfvDycnJwwZMgQXL16ssk7v3r1hYWEBABgx\nYgTCwsKqPC50SCYe7ee1R0liCR79/kjoUhhjGlJrRxIZGQlPT0/1sre3NyIiIp65/pYtWzBy5Ej1\nskwmQ1BQEMaMGYODBw82slzdJeVx2vpk02uhB/eN7oh7Lw7KYt2YeJfytgM4H2s8jV634sSJE9i1\naxfOnz+v/tm5c+dgZ2eHmJgYjBw5Ev7+/mjXrl21z582bRqcnZ0BAJaWlvDx8UFgYCCAv78Murp8\n/fp1UdUj5LLVICvEOsYiaXYSJoZMFLweXuZlXVoODQ3F9u3bAUDdXgqt1jmSvLw8BAYG4tq1awCA\nuXPnYtiwYRgxYkSV9W7evIlx48bhyJEjcHNzq/a15s+fDy8vL8yYMePpQkQwzse0pySlBFf8rsAv\n0g/GLsZCl8OYzhJD21nr0FbF3Ed4eDiSkpJw/PhxBAQEVFknJSUF48ePx+7du6t0Io8fP0ZBQQEA\nIDMzE0ePHsWwYcM0WT/TUUaORrCfb4/49+OFLoUx1kh1Ovx3/fr1mDlzJgYNGoTZs2fDxsYGwcHB\nCA4OBgCsXLkS2dnZePvtt6sc5pueno5+/frBx8cHEydOxIIFC+Dg4NB0aUSsYtdUihqazWGBA4pu\nF+HRYXFPvEt52wGcjzVeneZIBgwYgJiYmCo/mzlzpvrfW7duxdatW596nqurq3pugLEn6bXUg9sG\nN8S9GwerICvoteTzYxnTRXytLSa4qNFRMO9lDqfFTkKXwpjOEUPbyX8CMsG5rXPD/TX3UXK/ROhS\nGGMNwB2Jlkh5nLax2YxdjdH+nfaIXyDOiXcpbzuA87HG446EiYLjR44oiCxAzskcoUthjNUTz5Ew\n0cj6LQsJSxLQ40YP6Bny3ziM1YUY2k7+bWWi0Xp0axg5GuHhvx4KXQpjrB64I9ESKY/TaiqbTCaD\n27/ckPJlCkrTSjXympog5W0HcD7WeNyRMFFp1bEV7GbYIWFRgtClMMbqiOdImOgoi5S45HUJXru9\nYNnPUuhyGBM1MbSdvEfCREffRB8d1nTAvXfugRT8xwVjYscdiZZIeZy2KbK1+UcbGNoYIvW7VI2/\ndn1JedsBnK+xSpJLkBaShpjJMYjsFonCG4VN+n5ipNH7kTCmKTKZDO4b3XF9wHW0eaUNWti2ELok\nxgAAZRllyD2di5yTOcg9lQtFvgJWQVawDLKEaXdTxLwWA79IP+gb6wtdqtbwHAkTtbgFcVDkKuC5\nzbP2lRlrAoo8BXLDc5F7Mhc5p3JQmlIKi/4W5Z3HQEuYdDKBTE8GoPy24tETo9HSriXc1ld/XyZN\nE0PbyR0JEzVFvgKXvC6h8y+dYd7LXOhyWDOgLFYi/3w+ck7lIPdkLopuFcG8lzksgyxhNdAKZt3N\nIDOQPfP58mw5LvtchsdWD1gPsW7yesXQdvIciZZIeRy6KbMZmBugw1f/m3hXCvPLIuVtB3A+UhDy\nLuQh+YtkXA+6jvNtziPxk0TIZDK4rHJBn6w+6HaiG5yWOME8wLzGTgQADK0N4bndE7FvxEKeJddg\nEvHiORImerav2SI1OBVp29Lw3FvPCV0O03GkIhRFFan3OHLP5MLI2QhWA63gMN8BFv0tYGDeuKbR\nKsgKbV9tizsz76DTz50gk9Xc+eg6HtpiOqHwRiFuDLkB/2h/GLY2FLocpkOICCXxJcg5mVPeeZzO\nhYGFgXqoyjLQskkO5lCVqnDF/wrs37WH3XQ7jb9+BTG0ndyRMJ1xb275eSUdN3cUuhQmICKCqkgF\nRa4Cirz//fe/fyvzlFV+Ln8kR35EPkhOsBpoVd5xBFnCyNFIK7UW3SrC9Reuwy/CD8YdjJvkPcTQ\ndnJHoiWhoaEIDAwUuowmoa1s8hw5Ir0i0eXPLjDzM2vy96sg5W0HaD+fqlRVY+Ov/nfu/x6r/LP/\nrS9rKYOBhQEMLA3K//+/f+tb6Ff9uaUBrimvYeiUoYINLz3Y8AAZ/82A7xnfWudXGkIMbSfPkTCd\nYWhlCNevXBH1UlT5cMFbdjC04mEuXaAqUSEtJA33v76P0gelTzf+T3QIxi7G1f7cwKJ8/frcZsAo\n1EjQOYr2c9vj0aFHSP4iGc7LnAWroynxHgnTOQVXC/Bg/QM8+v0RbCfZwv5de7Tq2Eroslg1lIVK\npAan4v4392HmawbHJY4w720u+cnnJ5WmluKy72V0OdBF44exi6Ht5I6E6azStFKkbkpFanAqzP3N\nYf++PSyDLJtdIyVG8hw5Hm58iIffPoRloCWcljjB1MdU6LIElbk/EwmLEtDjeg/om2rurHcxtJ18\nHomWSPlYfaGytbRrCZfPXNAruRdsxtgg7t04XO52GWk/pEFVotLY+0h52wGazVf2VxkSPkrARbeL\nKEksge8ZX3T6qZOgnYhYtl+bcW1g0d8Cce/FCV2KxnFHwnSevrE+7N60Q4+oHuiwpgMyf87EBacL\nSFyWiLK/yoQur1kouV+Ce/Pu4ZLXJSgKFOhxpQc8QzzRyoOHHCtz2+CGnNM5yPw1U+hSNIqHtpgk\nFcUU4eGGh8jYmwGb0Tawf98ept2a99BKUyiOK0bKP1OQuT8Tdv9nB/v59mhp11LoskQt70Iebo+9\nje7XumvksxJD28kdCZM0+SM5UrekIvXfqTDuaAz79+zR+qXW6ovssYYpjCpEyqoU5BzPwXNznoP9\nPHs+UbQeEpclouBiAboc7tLoOT0xtJ08tKUlYhmnbQpizmbY2hBOi50QkBgAuzftkPxZMi55XMKD\njQ+gLFTW6TXEnE8T6pMv/1I+okZH4ebgmzD1MUVAQgBcVriIuhMR4/Zz+sSp/ICEbx8KXYpGcEfC\nmgU9Qz20ndQWfpf84LndE3lheYhwjkD8wniUpJQIXZ6oERFyQ3NxY/AN3H75NqwHWyMgMQCOHzo2\n+ppUzZWeoR68dnkheWUyim4XCV1Oo/HQFmu2ihOL8fDbh0jfng6rgVawf98eFr0thC5LNIgI2Yez\nkfxFMuSZcjh+5Ii2k9tCrwX//akpaVvT8PDfD+EX4Qe9lg37XMXQdnJHwpo9Rb4C6SHpePCvB2jR\npgXs37OHzXibep09LSWkJGTuz0TKqhSQkuC0xAlt/tEGMn2eV9I0IsLtsbdh3NEYHVZ3aNBriKHt\nbJ6/KQIQ4zitpuh6NgNzA9i/a4+AuwFw/MgRqd+l4qLrRaR8lQJ5jlzn89WmIp9KrkL6jnREdo7E\n/TX34bzCGT2u94DtRFud7kTEvP1kMhk6ft8Rf+3+Czmnc4Qup8F4gJOx/5Hpy2AzxgY2Y2zUl2G5\n6HoRWVOzgEChq2s6qjIVHm5+iPur78PIxQju37rzFQK0qEWbFvDc5onYqbHocaOHTl4/joe2GKtB\ncVwxrgRcQc+bPdGyvfTOj8j8NRP35tyDmZ8ZHD925DkiAd2bew/yLDm8fvSqVycuhraTOxLGahH/\nYTwUjxTw2OohdCkaJc+R45LHJXT6pRMs+1kKXU6zpyxW4kr3K3D62AltX2tb5+eJoe3kORItEfM4\nbWNJORsAJPZNRNbBLBRF6/5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      }
     ],
     "prompt_number": 127
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "clean",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>open</th>\n      <th>closed</th>\n      <th>median_difference</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>1999 SNIP2</th>\n      <td> 0.3340</td>\n      <td> 0.6810</td>\n      <td> 0.3470</td>\n    </tr>\n    <tr>\n      <th>2000 SNIP2</th>\n      <td> 0.3640</td>\n      <td> 0.6750</td>\n      <td> 0.3110</td>\n    </tr>\n    <tr>\n      <th>2001 SNIP2</th>\n      <td> 0.2895</td>\n      <td> 0.6090</td>\n      <td> 0.3195</td>\n    </tr>\n    <tr>\n      <th>2002 SNIP2</th>\n      <td> 0.3040</td>\n      <td> 0.5890</td>\n      <td> 0.2850</td>\n    </tr>\n    <tr>\n      <th>2003 SNIP2</th>\n      <td> 0.3310</td>\n      <td> 0.6010</td>\n      <td> 0.2700</td>\n    </tr>\n    <tr>\n      <th>2004 SNIP2</th>\n      <td> 0.3890</td>\n      <td> 0.6170</td>\n      <td> 0.2280</td>\n    </tr>\n    <tr>\n      <th>2005 SNIP2</th>\n      <td> 0.4330</td>\n      <td> 0.6500</td>\n      <td> 0.2170</td>\n    </tr>\n    <tr>\n      <th>2006 SNIP2</th>\n      <td> 0.4600</td>\n      <td> 0.6310</td>\n      <td> 0.1710</td>\n    </tr>\n    <tr>\n      <th>2007 SNIP2</th>\n      <td> 0.3970</td>\n      <td> 0.6130</td>\n      <td> 0.2160</td>\n    </tr>\n    <tr>\n      <th>2008 SNIP2</th>\n      <td> 0.3890</td>\n      <td> 0.6170</td>\n      <td> 0.2280</td>\n    </tr>\n    <tr>\n      <th>2009 SNIP2</th>\n      <td> 0.3990</td>\n      <td> 0.6285</td>\n      <td> 0.2295</td>\n    </tr>\n    <tr>\n      <th>2010 SNIP2</th>\n      <td> 0.4040</td>\n      <td> 0.6400</td>\n      <td> 0.2360</td>\n    </tr>\n    <tr>\n      <th>2011 SNIP2</th>\n      <td> 0.4745</td>\n      <td> 0.6750</td>\n      <td> 0.2005</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
       "output_type": "pyout",
       "prompt_number": 128,
       "text": "              open  closed  median_difference\n1999 SNIP2  0.3340  0.6810             0.3470\n2000 SNIP2  0.3640  0.6750             0.3110\n2001 SNIP2  0.2895  0.6090             0.3195\n2002 SNIP2  0.3040  0.5890             0.2850\n2003 SNIP2  0.3310  0.6010             0.2700\n2004 SNIP2  0.3890  0.6170             0.2280\n2005 SNIP2  0.4330  0.6500             0.2170\n2006 SNIP2  0.4600  0.6310             0.1710\n2007 SNIP2  0.3970  0.6130             0.2160\n2008 SNIP2  0.3890  0.6170             0.2280\n2009 SNIP2  0.3990  0.6285             0.2295\n2010 SNIP2  0.4040  0.6400             0.2360\n2011 SNIP2  0.4745  0.6750             0.2005"
      }
     ],
     "prompt_number": 128
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "matches[['1999 SNIP2', '2011 SNIP2']].describe()",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>1999 SNIP2</th>\n      <th>2011 SNIP2</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>count</th>\n      <td> 459.000000</td>\n      <td> 2134.000000</td>\n    </tr>\n    <tr>\n      <th>mean</th>\n      <td>   0.445412</td>\n      <td>    0.564373</td>\n    </tr>\n    <tr>\n      <th>std</th>\n      <td>   0.484183</td>\n      <td>    0.509639</td>\n    </tr>\n    <tr>\n      <th>min</th>\n      <td>   0.000000</td>\n      <td>    0.000000</td>\n    </tr>\n    <tr>\n      <th>25%</th>\n      <td>   0.147000</td>\n      <td>    0.201250</td>\n    </tr>\n    <tr>\n      <th>50%</th>\n      <td>   0.334000</td>\n      <td>    0.474500</td>\n    </tr>\n    <tr>\n      <th>75%</th>\n      <td>   0.592500</td>\n      <td>    0.807000</td>\n    </tr>\n    <tr>\n      <th>max</th>\n      <td>   3.797000</td>\n      <td>    4.814000</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
       "output_type": "pyout",
       "prompt_number": 129,
       "text": "       1999 SNIP2   2011 SNIP2\ncount  459.000000  2134.000000\nmean     0.445412     0.564373\nstd      0.484183     0.509639\nmin      0.000000     0.000000\n25%      0.147000     0.201250\n50%      0.334000     0.474500\n75%      0.592500     0.807000\nmax      3.797000     4.814000"
      }
     ],
     "prompt_number": 129
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "matches[['1999 SNIP2', '2011 SNIP2']].median()",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 130,
       "text": "1999 SNIP2    0.3340\n2011 SNIP2    0.4745"
      }
     ],
     "prompt_number": 130
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "matches['1999 SNIP2'].hist()",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 131,
       "text": "<matplotlib.axes.AxesSubplot at 0x10e5b1d90>"
      },
      {
       "output_type": "display_data",
       "png": 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SlJ2drZaWFkkDgz4nJydyXlZWVuQxAIB/xoz0C0bSDw108UNZJynjzu+/LqlAUtmdY+fO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\n7KGJlG98bgAAAABJRU5ErkJggg==\n"
      }
     ],
     "prompt_number": 131
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "open_years_sjr = find_sjr(matches)\nclosed_years_sjr = find_sjr(closed)",
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 132
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "open_years_sjr.median().plot(style='green');\nclosed_years_sjr.median().plot(style='--', title='Median SJR score\\nGreen=closed journals\\nBlue=open journals', rot=30);",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "display_data",
       "png": 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YY1KUnQ0sXQosXw7MmQN89ZW8P6OiqGwfh97/Gt/Cw8ORkpKC4OBgODk5KWyT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\n"
      }
     ],
     "prompt_number": 133
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "sjr_diff = closed_years_sjr.median() - open_years_sjr.median()\nsjr_diff",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 134,
       "text": "1999 SJR2    0.113\n2000 SJR2    0.121\n2001 SJR2    0.126\n2002 SJR2    0.131\n2003 SJR2    0.139\n2004 SJR2    0.151\n2005 SJR2    0.169\n2006 SJR2    0.193\n2007 SJR2    0.112\n2008 SJR2    0.123\n2009 SJR2    0.125\n2010 SJR2    0.109\n2011 SJR2    0.103"
      }
     ],
     "prompt_number": 134
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "sjr_diff.plot(title='Closed journal SJR advantage over open journal', rot=30, ylim=(.0, .75), style='m');",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "display_data",
       "png": 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R3PtVOZ+0yTmfnLOJjYsGY4wxg/GYBmOMSQyPaTDGGJMELhpGJPd+Vc4nbXLO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\nxgzGRYMxxpjBuGgwxhgz2P8DRzVGXW8LPw8AAAAASUVORK5CYII=\n"
      }
     ],
     "prompt_number": 135
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "matches[['1999 SJR2', '2011 SJR2']].describe()",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>1999 SJR2</th>\n      <th>2011 SJR2</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>count</th>\n      <td> 2201.000000</td>\n      <td> 2201.000000</td>\n    </tr>\n    <tr>\n      <th>mean</th>\n      <td>    0.077542</td>\n      <td>    0.378843</td>\n    </tr>\n    <tr>\n      <th>std</th>\n      <td>    0.300900</td>\n      <td>    0.542297</td>\n    </tr>\n    <tr>\n      <th>min</th>\n      <td>    0.000000</td>\n      <td>    0.000000</td>\n    </tr>\n    <tr>\n      <th>25%</th>\n      <td>    0.000000</td>\n      <td>    0.128000</td>\n    </tr>\n    <tr>\n      <th>50%</th>\n      <td>    0.000000</td>\n      <td>    0.216000</td>\n    </tr>\n    <tr>\n      <th>75%</th>\n      <td>    0.101000</td>\n      <td>    0.408000</td>\n    </tr>\n    <tr>\n      <th>max</th>\n      <td>    6.916000</td>\n      <td>    7.581000</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
       "output_type": "pyout",
       "prompt_number": 136,
       "text": "         1999 SJR2    2011 SJR2\ncount  2201.000000  2201.000000\nmean      0.077542     0.378843\nstd       0.300900     0.542297\nmin       0.000000     0.000000\n25%       0.000000     0.128000\n50%       0.000000     0.216000\n75%       0.101000     0.408000\nmax       6.916000     7.581000"
      }
     ],
     "prompt_number": 136
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": "# Part 2"
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "country_list = ['United States', 'United Kingdom', 'Germany','Netherlands']\nnonlst = pd.DataFrame(impact.Country.value_counts()[4:])\nnonlst = list(nonlst.index)\n\noa_big4 = matches[matches.Country_x.isin(country_list)]\noa_nonbig4 = matches[matches.Country_x.isin(nonlst)]",
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 137
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": "Separate OA and closed by era, then by SJR and SNIP"
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "oa_snip, oa_era1, oa_era2, oa_era3 = select_snip_by_era(oa_big4)\nnon_snip, non_era1, non_era2, non_era3 = select_snip_by_era(oa_nonbig4)\n\noasjr, oasjr1, oasjr2, oasjr3 = select_sjr_by_era(oa_big4)\nnonsjr, nonsjr1, nonsjr2, nonsjr3 = select_sjr_by_era(oa_nonbig4)",
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 138
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": "Find mean SJR of all journals, by year, and by whether they are one of the big 4 countries."
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "median_sjr = pd.DataFrame([oasjr1['2011 SJR2'].median(), oasjr2['2011 SJR2'].median(), oasjr3['2011 SJR2'].median()])\nmedian_sjr_col = median_sjr\ntmp = median_sjr_col.rename(columns={0: 'Big4 Median SJR'}).T\nmedian_sjr_big4 = tmp.rename(columns={0:'1996', 1: '1996-2001', 2:'2002-2011'})",
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 139
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "OA_sjr_big4 = median_sjr_big4.T\nOA_sjr_big4['Non-Big4 mean SJR'] = ([nonsjr1['2011 SJR2'].median(), nonsjr2['2011 SJR2'].median(), nonsjr3['2011 SJR2'].median()])\nOA_sjr_big4.T",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>1996</th>\n      <th>1996-2001</th>\n      <th>2002-2011</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>Big4 Median SJR</th>\n      <td> 0.489</td>\n      <td> 0.5005</td>\n      <td> 0.418</td>\n    </tr>\n    <tr>\n      <th>Non-Big4 mean SJR</th>\n      <td> 0.223</td>\n      <td> 0.1940</td>\n      <td> 0.171</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
       "output_type": "pyout",
       "prompt_number": 140,
       "text": "                    1996  1996-2001  2002-2011\nBig4 Median SJR    0.489     0.5005      0.418\nNon-Big4 mean SJR  0.223     0.1940      0.171"
      }
     ],
     "prompt_number": 140
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": "Newer journals appear to be less impactful in 2011 than those started before 1996"
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "OA_sjr_big4.T.plot(kind='bar', rot=0,title='2011 median SJR of OA journals from Big 4, by start year\\n Big 4 = USA, UK, Netherlands & Germany');",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "display_data",
       "png": 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a2tqQl5dHREQEt27jxo3IysrCiBEjuCn0anKuqw4Fq6uryw20VjHcbOXhjVu3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\n"
      }
     ],
     "prompt_number": 141
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "median_snip = pd.DataFrame([oa_era1['2011 SNIP2'].median(), oa_era2['2011 SNIP2'].median(), oa_era3['2011 SNIP2'].median()])\nmedian_snip_col = median_snip\ntmp = median_snip_col.rename(columns={0: 'Big4 Mean SNIP'}).T\nmedian_snip_big4 = tmp.rename(columns={0:'1996', 1: '1996-2001', 2:'2002-2011'})",
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 142
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "OA_big4 = median_snip_big4.T\nOA_big4['Non-Big4 median SNIP'] = ([non_era1['2011 SNIP2'].median(), non_era2['2011 SNIP2'].median(), non_era3['2011 SNIP2'].median()])\nOA_big4.T",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>1996</th>\n      <th>1996-2001</th>\n      <th>2002-2011</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>Big4 Mean SNIP</th>\n      <td> 0.872</td>\n      <td> 0.834</td>\n      <td> 0.812</td>\n    </tr>\n    <tr>\n      <th>Non-Big4 median SNIP</th>\n      <td> 0.554</td>\n      <td> 0.403</td>\n      <td> 0.340</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
       "output_type": "pyout",
       "prompt_number": 143,
       "text": "                       1996  1996-2001  2002-2011\nBig4 Mean SNIP        0.872      0.834      0.812\nNon-Big4 median SNIP  0.554      0.403      0.340"
      }
     ],
     "prompt_number": 143
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "OA_big4.T.plot(kind='bar', rot=0,title='Median SNIP of OA journals from Big 4, by start year\\n Big 4 = USA, UK, Netherlands & Germany');",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "display_data",
       "png": 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0kyr75aloKuLK91KTly9fYsqUKZgwYQJu3ryJ7OxstGzZUqn129jYQENDQ2Lf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\n"
      }
     ],
     "prompt_number": 144
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "big4_closed = closed[closed.Country.isin(country_list)]\nnon_closed = closed[closed.Country.isin(nonlst)]",
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 145
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "big4_closed_snip = find_snip(big4_closed)\nnon_closed_snip = find_snip(non_closed)",
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 146
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "big4_closed_sjr = find_sjr(big4_closed)\nnon_closed_sjr = find_sjr(non_closed)",
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 147
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": "nevermind about start year, these analysis are all about access, country status, and SJR year.\n\nGood news, Big4 OA journals have been shooting up in terms of impcat"
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "plt.figure()\nsjr_combo = pd.DataFrame([oasjr.median(), nonsjr.median(), big4_closed_sjr.median(), non_closed_sjr.median()]).T\nsjr_combo = sjr_combo.rename(columns={0: 'Big4 OA', 1: 'Non-Big4 OA', 2: 'Big4 closed', 3: 'Non-Big4 closed'})\nsjr_combo.plot(rot=30);\nplt.show()",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "display_data",
       "png": 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0acaPH09KSoppEyM7Ozu6d+9O+/bteeKJJ+jZsyfbtm175PXc3d0pWbIkDRo0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\ngSBg/gHxaayhCvtVBAAAAABJRU5ErkJggg==\n"
      }
     ],
     "prompt_number": 148
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "sjr_combo['OA diff'] = sjr_combo['Big4 closed'] - sjr_combo['Big4 OA']\nsjr_combo['closed diff'] = sjr_combo['Non-Big4 closed'] - sjr_combo['Non-Big4 OA']\nsjr_combo[['OA diff', 'closed diff']].plot(rot=30, title='SJR difference, OA and closed');",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "display_data",
       "png": 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PkpcXUXi42BGxiho3juiHH8SOQrEo8neP/UcRE0ulqsJWrVoFHx8f5OXlwc/P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\nSpGeAAAAAElFTkSuQmCC\n"
      }
     ],
     "prompt_number": 149
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "sjr_combo.describe()",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Big4 OA</th>\n      <th>Non-Big4 OA</th>\n      <th>Big4 closed</th>\n      <th>Non-Big4 closed</th>\n      <th>OA diff</th>\n      <th>closed diff</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>count</th>\n      <td> 13.000000</td>\n      <td> 13.000000</td>\n      <td> 13.000000</td>\n      <td> 13.000000</td>\n      <td> 13.000000</td>\n      <td> 13.000000</td>\n    </tr>\n    <tr>\n      <th>mean</th>\n      <td>  0.143077</td>\n      <td>  0.051462</td>\n      <td>  0.293769</td>\n      <td>  0.101000</td>\n      <td>  0.150692</td>\n      <td>  0.049538</td>\n    </tr>\n    <tr>\n      <th>std</th>\n      <td>  0.166178</td>\n      <td>  0.070902</td>\n      <td>  0.099030</td>\n      <td>  0.049156</td>\n      <td>  0.071100</td>\n      <td>  0.053258</td>\n    </tr>\n    <tr>\n      <th>min</th>\n      <td>  0.000000</td>\n      <td>  0.000000</td>\n      <td>  0.173000</td>\n      <td>  0.000000</td>\n      <td>  0.015000</td>\n      <td> -0.024000</td>\n    </tr>\n    <tr>\n      <th>25%</th>\n      <td>  0.000000</td>\n      <td>  0.000000</td>\n      <td>  0.210000</td>\n      <td>  0.101000</td>\n      <td>  0.114000</td>\n      <td>  0.000000</td>\n    </tr>\n    <tr>\n      <th>50%</th>\n      <td>  0.106000</td>\n      <td>  0.000000</td>\n      <td>  0.264000</td>\n      <td>  0.106000</td>\n      <td>  0.166000</td>\n      <td>  0.020000</td>\n    </tr>\n    <tr>\n      <th>75%</th>\n      <td>  0.260000</td>\n      <td>  0.104000</td>\n      <td>  0.374000</td>\n      <td>  0.124000</td>\n      <td>  0.205000</td>\n      <td>  0.101000</td>\n    </tr>\n    <tr>\n      <th>max</th>\n      <td>  0.445000</td>\n      <td>  0.186000</td>\n      <td>  0.460000</td>\n      <td>  0.162000</td>\n      <td>  0.245000</td>\n      <td>  0.111000</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
       "output_type": "pyout",
       "prompt_number": 150,
       "text": "         Big4 OA  Non-Big4 OA  Big4 closed  Non-Big4 closed    OA diff  closed diff\ncount  13.000000    13.000000    13.000000        13.000000  13.000000    13.000000\nmean    0.143077     0.051462     0.293769         0.101000   0.150692     0.049538\nstd     0.166178     0.070902     0.099030         0.049156   0.071100     0.053258\nmin     0.000000     0.000000     0.173000         0.000000   0.015000    -0.024000\n25%     0.000000     0.000000     0.210000         0.101000   0.114000     0.000000\n50%     0.106000     0.000000     0.264000         0.106000   0.166000     0.020000\n75%     0.260000     0.104000     0.374000         0.124000   0.205000     0.101000\nmax     0.445000     0.186000     0.460000         0.162000   0.245000     0.111000"
      }
     ],
     "prompt_number": 150
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "kind_combo = pd.DataFrame([oa_snip.median(), non_snip.median(), big4_closed_snip.median(), non_closed_snip.median()]).T\nkind_combo = kind_combo.rename(columns={0: 'Big4 OA', 1: 'non-Big4 OA', 2: 'Big4 closed', 3: 'Non-Big4 closed'})",
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 151
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "kind_combo.describe()",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Big4 OA</th>\n      <th>non-Big4 OA</th>\n      <th>Big4 closed</th>\n      <th>Non-Big4 closed</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>count</th>\n      <td> 13.000000</td>\n      <td> 13.000000</td>\n      <td> 13.000000</td>\n      <td> 13.000000</td>\n    </tr>\n    <tr>\n      <th>mean</th>\n      <td>  0.634885</td>\n      <td>  0.302077</td>\n      <td>  0.783808</td>\n      <td>  0.247654</td>\n    </tr>\n    <tr>\n      <th>std</th>\n      <td>  0.144327</td>\n      <td>  0.041310</td>\n      <td>  0.035933</td>\n      <td>  0.042857</td>\n    </tr>\n    <tr>\n      <th>min</th>\n      <td>  0.420000</td>\n      <td>  0.239000</td>\n      <td>  0.723000</td>\n      <td>  0.201000</td>\n    </tr>\n    <tr>\n      <th>25%</th>\n      <td>  0.522500</td>\n      <td>  0.268000</td>\n      <td>  0.770000</td>\n      <td>  0.216500</td>\n    </tr>\n    <tr>\n      <th>50%</th>\n      <td>  0.631000</td>\n      <td>  0.312000</td>\n      <td>  0.781000</td>\n      <td>  0.236500</td>\n    </tr>\n    <tr>\n      <th>75%</th>\n      <td>  0.759500</td>\n      <td>  0.326500</td>\n      <td>  0.809000</td>\n      <td>  0.262000</td>\n    </tr>\n    <tr>\n      <th>max</th>\n      <td>  0.823000</td>\n      <td>  0.386000</td>\n      <td>  0.847000</td>\n      <td>  0.324000</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
       "output_type": "pyout",
       "prompt_number": 152,
       "text": "         Big4 OA  non-Big4 OA  Big4 closed  Non-Big4 closed\ncount  13.000000    13.000000    13.000000        13.000000\nmean    0.634885     0.302077     0.783808         0.247654\nstd     0.144327     0.041310     0.035933         0.042857\nmin     0.420000     0.239000     0.723000         0.201000\n25%     0.522500     0.268000     0.770000         0.216500\n50%     0.631000     0.312000     0.781000         0.236500\n75%     0.759500     0.326500     0.809000         0.262000\nmax     0.823000     0.386000     0.847000         0.324000"
      }
     ],
     "prompt_number": 152
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "kind_combo.to_csv(\"snip_origin_year.csv\")",
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 153
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "kind_combo.plot(rot = 30, title='Median SNIP by year, access status and country of origin');",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "display_data",
       "png": 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yn9jzUlDawuq9OHW5cOFCdOrUSaYN8fHx0NTUhKGhIV68eIH58+dLnR80aBCWL1+OoKAg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\nMBgMRrtgRsJgMBiMdsGMhMFgMBjt4v8A5hAWffJBPtkAAAAASUVORK5CYII=\n"
      }
     ],
     "prompt_number": 154
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "kind_combo['OA diff'] = kind_combo['Big4 closed'] - kind_combo['Big4 OA']\nkind_combo['closed diff'] = kind_combo['Non-Big4 closed'] - kind_combo['non-Big4 OA']\nkind_combo[['OA diff', 'closed diff']].plot(rot = 30, title='SNIP difference, OA and closed');",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "display_data",
       "png": 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aO9dNSQFWrQK2b+c+ellr2lS4f3zJDsOdOgkTH/LzZR0ZYwyQg0QCACoqwv5W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\n"
      }
     ],
     "prompt_number": 155
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": "### Is the journal's impact related to what country it comes from?"
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "def country_impact(df):\n\n    impacts = df.groupby(by='Country')\n    pop_countries = pd.DataFrame(impact.Country.value_counts(), columns=['Num_journals_total'])\n    country_median = pd.DataFrame(impacts['2011 SNIP2'].median(), columns=['Median snip'])\n    country_median['Num_journals_total'] = pop_countries.Num_journals_total\n    country_median['Median SJR'] = impacts['2011 SJR2'].median()\n    \n    plt.figure()\n    plt.scatter(x=log(country_median['Num_journals_total']), y=country_median['Median snip']);\n    plt.scatter(x=log(country_median['Num_journals_total']), y=country_median['Median SJR'], c='y', marker='+');\n    plt.title('Median impact by log of number of journals published in that country\\nBlue is SNIP, yellow is SJR')\n    \n    clean = country_median.dropna(how='any')\n    slope, intercept, r_value, p_value, std_err = stats.linregress(clean['Median snip'], clean['Num_journals_total'])\n    print 'SNIP. r = {0}, p = {1}'.format(r_value, p_value)\n    \n    slope1, intercept1, r_value1, p_value1, std_err1 = stats.linregress(clean['Median SJR'], clean['Num_journals_total'])\n    print 'SJR. r = {0}, p = {1}'.format(r_value1, p_value1)\n    \n    return country_median",
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 156
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": "For both open and closed access journals, the more journals that country has, the higher the average journal's impact rating in that country. In other words, for countries that have a lot of journals, each journal in that country is more impactful"
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "open_access = country_impact(matches)",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": "SNIP. r = 0.30092808042, p = 0.00704211226706\nSJR. r = 0.388444556851, p = 0.000403882110151\n"
      },
      {
       "output_type": "display_data",
       "png": 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zMDc3N5iamsLe3j7fcjdv3oSGhkap/FiUsS9x9+7dfJNXUYeKKC/u3LmDe/fu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\nGfT6jVWTnq3WQrp1W4ybW1zVfmVlR7DZivH1HYDVWkho6Iw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      }
     ],
     "prompt_number": 157
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "closed_access = country_impact(closed)",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": "SNIP. r = 0.466675569107, p = 3.1207456288e-06\nSJR. r = 0.60358379053, p = 2.40787599202e-10\n"
      },
      {
       "output_type": "display_data",
       "png": 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Pnrh9+7ZourCwEPHx8WIxfqiWLVsiOztbtO+ysrLw4sWLj1rn+/X6/PPPoampib59+4LH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\nTkSuQmCC\n"
      }
     ],
     "prompt_number": 158
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "combo = pd.merge(open_access, closed_access, suffixes=('_open', '_closed'), left_on=open_access.index, right_on=closed_access.index)\ncombo = combo.set_index('key_0')\ncombo['snip_diff'] = combo['Median snip_open'] - combo['Median snip_closed']\ncombo['sjr_diff'] = combo['Median SJR_open'] - combo['Median SJR_closed']\nfor_plot = combo[combo['Num_journals_total_open'] >= 150]",
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 159
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": "On the whole, open access journals have greater impact than non-OA. "
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "for_plot.snip_diff.plot(kind='bar', title='Median snip open - median snip closed\\n(Negative means closed has higher snip)');",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "display_data",
       "png": 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dF62LBzseYvx/Sx3606UvPux4iFHXdjzEKKXMq0LqEqpPQhAEUUil0tx1qdsS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\nncFgMOog/wsIeEidx2u/XAAAAABJRU5ErkJggg==\n"
      }
     ],
     "prompt_number": 160
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "for_plot.sjr_diff.plot(kind='bar', title='SJR');",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "display_data",
       "png": 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WHaF/qlV7RXSMFhb1adSoUeXSn6+8UtPg/ici0X35yis1De5/ZV8a0/+G9Gdl7n/l91Me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\nrkJggg==\n"
      }
     ],
     "prompt_number": 161
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "combo.snip_diff.hist(bins=30);",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "display_data",
       "png": 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      }
     ],
     "prompt_number": 162
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "combo.snip_diff.describe()",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 163,
       "text": "count    73.000000\nmean      0.119822\nstd       0.253111\nmin      -0.900000\n25%       0.035500\n50%       0.139500\n75%       0.272000\nmax       0.776000"
      }
     ],
     "prompt_number": 163
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "combo.sjr_diff.hist(bins=30);",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "display_data",
       "png": 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      }
     ],
     "prompt_number": 164
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "combo.sjr_diff.describe()",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 165,
       "text": "count    74.000000\nmean      0.033054\nstd       0.110953\nmin      -0.479000\n25%      -0.004000\n50%       0.028750\n75%       0.075625\nmax       0.455000"
      }
     ],
     "prompt_number": 165
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "combo.head()",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Median snip_open</th>\n      <th>Num_journals_total_open</th>\n      <th>Median SJR_open</th>\n      <th>Median snip_closed</th>\n      <th>Num_journals_total_closed</th>\n      <th>Median SJR_closed</th>\n      <th>snip_diff</th>\n      <th>sjr_diff</th>\n    </tr>\n    <tr>\n      <th>key_0</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>Argentina</th>\n      <td> 0.281</td>\n      <td>  60</td>\n      <td> 0.142</td>\n      <td> 0.213</td>\n      <td>  60</td>\n      <td> 0.1235</td>\n      <td> 0.068</td>\n      <td> 0.0185</td>\n    </tr>\n    <tr>\n      <th>Australia</th>\n      <td> 0.267</td>\n      <td> 390</td>\n      <td> 0.202</td>\n      <td> 0.445</td>\n      <td> 390</td>\n      <td> 0.2260</td>\n      <td>-0.178</td>\n      <td>-0.0240</td>\n    </tr>\n    <tr>\n      <th>Austria</th>\n      <td> 0.104</td>\n      <td>  79</td>\n      <td> 0.116</td>\n      <td> 0.225</td>\n      <td>  79</td>\n      <td> 0.1240</td>\n      <td>-0.121</td>\n      <td>-0.0080</td>\n    </tr>\n    <tr>\n      <th>Bangladesh</th>\n      <td> 0.734</td>\n      <td>  22</td>\n      <td> 0.261</td>\n      <td> 0.227</td>\n      <td>  22</td>\n      <td> 0.1280</td>\n      <td> 0.507</td>\n      <td> 0.1330</td>\n    </tr>\n    <tr>\n      <th>Belgium</th>\n      <td> 0.519</td>\n      <td> 181</td>\n      <td> 0.183</td>\n      <td> 0.199</td>\n      <td> 181</td>\n      <td> 0.1245</td>\n      <td> 0.320</td>\n      <td> 0.0585</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
       "output_type": "pyout",
       "prompt_number": 166,
       "text": "            Median snip_open  Num_journals_total_open  Median SJR_open  \\\nkey_0                                                                    \nArgentina              0.281                       60            0.142   \nAustralia              0.267                      390            0.202   \nAustria                0.104                       79            0.116   \nBangladesh             0.734                       22            0.261   \nBelgium                0.519                      181            0.183   \n\n            Median snip_closed  Num_journals_total_closed  Median SJR_closed  \\\nkey_0                                                                          \nArgentina                0.213                         60             0.1235   \nAustralia                0.445                        390             0.2260   \nAustria                  0.225                         79             0.1240   \nBangladesh               0.227                         22             0.1280   \nBelgium                  0.199                        181             0.1245   \n\n            snip_diff  sjr_diff  \nkey_0                            \nArgentina       0.068    0.0185  \nAustralia      -0.178   -0.0240  \nAustria        -0.121   -0.0080  \nBangladesh      0.507    0.1330  \nBelgium         0.320    0.0585  "
      }
     ],
     "prompt_number": 166
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "clean = combo.dropna(how='any')\nslope1, intercept1, r_value1, p_value1, std_err1 = stats.linregress(clean['snip_diff'], clean['Num_journals_total_open'])\nprint r_value1, p_value1",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": "-0.158138988222 0.181469148274\n"
      }
     ],
     "prompt_number": 167
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "plt.figure()\nplt.scatter(log(clean['Num_journals_total_open']), clean['snip_diff']);",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "display_data",
       "png": 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n0UuvXhXThg0biw4e/x8BLiSR2NE770zR6Y6E7747o+hK0xUkkw0lL69WpT7m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\npqZi/PjxAAovn+7duzeAx7d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      }
     ],
     "prompt_number": 168
    }
   ],
   "metadata": {}
  }
 ]
}