<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/word-associations-from-the-small-world-of-words/"></div></div><div></div></div><div></div></div>
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(This article was first published on <strong><a href="https://juliasilge.com/blog/word-associations/"> Rstats on Julia Silge</a></strong>, and kindly contributed to <a rel="nofollow" href="https://www.r-bloggers.com/">R-bloggers)</a> </div>
<p>Do you subscribe to the <a rel="nofollow" href="https://tinyletter.com/data-is-plural" target="_blank">Data is Plural</a> newsletter from <a rel="nofollow" href="https://www.jsvine.com/" target="_blank">Jeremy Singer-Vine</a>? You probably should, because it is a treasure trove of interesting datasets arriving in your email inbox. In the November 28 edition, Jeremy linked to the <a rel="nofollow" href="https://smallworldofwords.org/en" target="_blank">Small World of Words</a> project, and I was entranced. I love stuff like that, all about words and how people think of them. I have been mulling around a blog post ever since, and today I finally have my post done, so let’s see what’s up!</p>
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<h2>It’s a Small World</h2>
<p>The Small World of Words project focuses on word associations. You can <a rel="nofollow" href="https://smallworldofwords.org/en" target="_blank">try it out for yourself</a> to see how it works, but the general idea is that the participant is presented with a word (from “telephone” to “journalist” to “yoga”) and is then asked to give their immediate association with that word. The project has collected more than 15 million responses to date, and is still collecting data. You can <a rel="nofollow" href="https://smallworldofwords.org/en/project/visualize" target="_blank">check out some pre-built visualizations</a> the researchers have put together to explore the dataset, or you can <a rel="nofollow" href="https://smallworldofwords.org/en/project/research" target="_blank">download the data for yourself</a>.</p>
<pre><code>library(tidyverse)

swow_raw &lt;- read_csv(&quot;SWOW-EN.R100.csv&quot;) %>%
    select(-education, -X1) %>%
    mutate(gender = case_when(gender == &quot;Fe&quot; ~ &quot;Women&quot;,
                              gender == &quot;Ma&quot; ~ &quot;Men&quot;,
                              gender == &quot;X&quot; ~ &quot;Non-binary&quot;),
           gender = fct_infreq(gender))

swow_raw</code></pre>
<pre><code>## # A tibble: 1,228,200 x 11
##       id participantID   age gender nativeLanguage country   created_at          cue      R1           R2     
##    </code></pre></div>