<p><div><div><div><div data-show-faces="false" data-height="21" data-width="100" data-layout="button_count" data-send="true" data-href="https://www.r-bloggers.com/sjrdata-all-scimago-journal-country-rank-data-ready-for-r/"></div></div><div></div></div><div></div></div>
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(This article was first published on <strong><a href="https://ikashnitsky.github.io/2018/sjrdata-package/"> Ilya Kashnitsky</a></strong>, and kindly contributed to <a rel="nofollow" href="https://www.r-bloggers.com/">R-bloggers)</a> </div>
<p><a rel="nofollow" href="https://i1.wp.com/ikashnitsky.github.io/images/180923/sjrdata-logo.png?ssl=1" target="_blank"><img alt="" src="https://i1.wp.com/ikashnitsky.github.io/images/180923/sjrdata-logo.png?w=456&ssl=1" data-recalc-dims="1"></a></p>
<p>SCImago Journal &amp; Country Rank provides valuable estimates of academic journals’ prestige. The data is freely available at <a rel="nofollow" href="https://www.scimagojr.com/" target="_blank">the project website</a> and is distributed for deeper analysis in forms of .csv and .xlsx files. I downloaded all the files and pooled them together, ready to be used in R.</p>
<p>Basically, all the package gives you three easily accessible data frames: <code>sjr_journals</code> (Journal Rank), <code>sjr_countries</code> (Country Rank, year-by-year), and <code>sjr_countries_1996_2017</code> (Country Rank, all years together).</p>
<p>The whole process of data acquisition can be foun in the <a rel="nofollow" href="https://github.com/ikashnitsky/sjrdata" target="_blank">github repo</a> (<code>dev</code> directory) or this <a rel="nofollow" href="https://gist.github.com/ikashnitsky/3133422ef85ff3f3d65be9926d6bd990" target="_blank">gist</a>.</p>
<h1>How to use <code>sjrdata</code></h1>
<p>Install the package from github, load it and use the data.</p>
<p>The instalation will take a while since the main dataset <code>sjr_journals</code> is pretty heavy (15.7MB compressed).</p>
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<pre><code># install
devtools::install_github(&quot;ikashnitsky/sjrdata&quot;)

# load
library(sjrdata)

# use
View(sjr_countries)
</code></pre>
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<h1>A couple of examples</h1>
<p>Let’s compare <em>Nature</em> and <em>Science</em>.</p>
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<pre><code>library(tidyverse)
library(sjrdata)

sjr_journals %>%
    filter(title %in% c(&quot;Nature&quot;, &quot;Science&quot;)) %>%
    ggplot(aes(cites_doc_2years, sjr, color = title))+
    geom_path(size = 1, alpha = .5)+
    geom_label(aes(label = year %>% str_sub(3, 4)),
              size = 3, label.padding = unit(.15, &quot;line&quot;))
</code></pre>
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<p><a rel="nofollow" href="https://i2.wp.com/ikashnitsky.github.io/images/180923/nature-science.png?ssl=1" target="_blank"><img alt="" src="https://i2.wp.com/ikashnitsky.github.io/images/180923/nature-science.png?w=456&ssl=1" data-recalc-dims="1"></a></p>
<p>Several demographic journals.</p>
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<pre><code>sjr_journals %>%
    filter(title %in% c(
        &quot;Demography&quot;,
        &quot;Population and Development Review&quot;,
        &quot;European Journal of Population&quot;,
        &quot;Population Studies&quot;,
        &quot;Demographic Research&quot;,
        &quot;Genus&quot;
    )) %>%
    ggplot(aes(cites_doc_2years, sjr, color = title))+
    geom_point()+
    stat_ellipse()+
    scale_color_brewer(palette = &quot;Dark2&quot;)+
    coord_cartesian(expand = F)
</code></pre>
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<p><a rel="nofollow" href="https://i2.wp.com/ikashnitsky.github.io/images/180923/demographic-journals.png?ssl=1" target="_blank"><img alt="" src="https://i2.wp.com/ikashnitsky.github.io/images/180923/demographic-journals.png?w=456&ssl=1" data-recalc-dims="1"></a></p>
	<p>
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<div>To <strong>leave a comment</strong> for the author, please follow the link and comment on their blog: <strong><a href="https://ikashnitsky.github.io/2018/sjrdata-package/"> Ilya Kashnitsky</a></strong>.</div>
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