Statistics Globe<p>When working with missing data, understanding the patterns and relationships of missing values across variables is essential for accurate and effective data analysis.</p><p>The attached mosaic plot, created using the mosaicMiss function, provides a clear depiction of missing values within a data set.</p><p>For more information on the VIM package, visit the official vignette here: <a href="https://cran.r-project.org/web/packages/VIM/vignettes/VIM.html" rel="nofollow noopener" translate="no" target="_blank"><span class="invisible">https://</span><span class="ellipsis">cran.r-project.org/web/package</span><span class="invisible">s/VIM/vignettes/VIM.html</span></a></p><p>Further details: <a href="https://statisticsglobe.com/online-workshop-missing-data-imputation-r" rel="nofollow noopener" translate="no" target="_blank"><span class="invisible">https://</span><span class="ellipsis">statisticsglobe.com/online-wor</span><span class="invisible">kshop-missing-data-imputation-r</span></a></p><p><a href="https://mastodon.social/tags/bigdata" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>bigdata</span></a> <a href="https://mastodon.social/tags/pythonprogrammer" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>pythonprogrammer</span></a> <a href="https://mastodon.social/tags/datavisualization" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>datavisualization</span></a> <a href="https://mastodon.social/tags/datascience" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>datascience</span></a></p>