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(This article was first published on <strong><a href="http://www.datacamp.com/community/blog/the-5-most-downloaded-r-packages"> DataCamp Blog</a></strong>, and kindly contributed to <a rel="nofollow" href="https://www.r-bloggers.com/">R-bloggers)</a> </div>
<p>Curious which R packages your colleagues and the rest of the R community are using? Thanks to <a target="_blank" rel="nofollow" href="https://www.rdocumentation.org/">Rdocumentation.org</a> you can now see for yourself! Rdocumentation.org aggregates R documentation and download information from popular repositories like CRAN, BioConductor and GitHub. In this post, we’ll take a look at the top 5 R packages with the most direct downloads!*</p>
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<p><strong>1. </strong><a target="_blank" rel="nofollow" href="https://www.rdocumentation.org/packages/dplyr/versions/0.5.0?"><strong>dplyr</strong></a><strong>, a grammar of data manipulation </strong></p>
<p>The R package with the highest number of direct downloads was dplyr, with <strong>98,417 monthly direct downloads</strong>. The dplyr package, written by Hadley Wickham, is a fantastic R package for all of your data manipulation tasks! Known as the grammar of data manipulation, dplyr is built around 5 main verbs: select, mutate, filter, arrange and summarize. Beyond the easy syntax, dplyr uses efficient data storage backends, which speeds up your data manipulation tasks. Pretty handy, right? The dplyr package is easy to learn – you can start with <a target="_blank" rel="nofollow" href="https://www.datacamp.com/courses/dplyr-data-manipulation-r-tutorial">this course</a> on <a target="_blank" rel="nofollow" href="http://www.datacamp.com/">DataCamp</a>. </p>
<p><strong>2. <a target="_blank" rel="nofollow" href="https://www.rdocumentation.org/packages/devtools/versions/1.12.0?">devtools</a>, a collection of package development tools</strong></p>
<p>The second most popular R package by direct downloads, with <strong>82,962 direct downloads</strong>, is devtools. The devtools package, also developed by Hadley Wickham, is a package developer’s best friend. As Hadley explains, “the devtools package makes code sharing easy and it reduces the time and effort required to complete your project by following a template that removes the need to have you think about how to organize things and paves the way for the creation of standardized tools that can further accelerate your progress.” Download devtools and get started making your own packages today! </p>
<p><strong>3. <a target="_blank" rel="nofollow" href="https://www.rdocumentation.org/packages/foreign/versions/0.8-67?">foreign</a>, read data stored by Minitab, S, SAS, SPSS, Stata, and more</strong></p>
<p>Data comes in all shapes, sizes, and formats. The foreign package, which earned <strong>80,191 direct downloads</strong> this month, helps you handle this! This package, developed by the folks at<a target="_blank" rel="nofollow" href="http://www.r-project.org/"> www.r-project.org</a>, includes functions for reading data from Minitab, S, SAS, SPSS, Stata, Systat, Weka, dBase and more. The foreign package is a must download for R users who have colleagues that work with SAS, SPSS, STATA and other software. Don’t let different file formats discourage you, download foreign today! </p>
<p><strong>4. <a target="_blank" rel="nofollow" href="https://www.rdocumentation.org/packages/cluster/versions/2.0.5?">cluster</a>, methods for cluster analysis</strong></p>
<p>The cluster package, with <strong>75,358 direct downloads</strong>, came in fourth this month. The cluster package, maintained by Dr. Martin Mächler, includes a variety of methods for cluster analysis including functions for hierarchical clustering, creating AGNES objects, clustering large applications and more. Check out the cluster package’s page on Rdocumentation.org to learn more and use it for your own R projects!<strong></strong></p>
<p><strong>5. <a target="_blank" rel="nofollow" href="https://www.rdocumentation.org/packages/ggplot2/versions/2.1.0?">ggplot2</a>, An implementation of the grammar of graphics in R</strong></p>
<p>One of the most popular R packages for data visualization is ggplot2. This useful data visualization package had <strong>72,550 monthly direct downloads</strong>. Authored by Hadley Wickham, the ggplot2 package offers an easy, consistent interface for producing plots and charts in R. As mentioned in its documentation, “it combines the advantages of both base and lattice graphics: conditioning and shared axes are handled automatically, and you can still build up a plot step by step from multiple data sources. It also implements a sophisticated multidimensional conditioning system and a consistent interface to map data to aesthetic attributes.” Want to learn for yourself? DataCamp offers 3 full-length courses devoted to ggplot2: </p>
<ul>
<li><a target="_blank" rel="nofollow" href="https://www.datacamp.com/courses/data-visualization-with-ggplot2-1">Data Visualization with ggplot2 (Part 1)</a></li>
<li><a target="_blank" rel="nofollow" href="https://www.datacamp.com/courses/data-visualization-with-ggplot2-2">Data Visualization with ggplot2 (Part 2)</a></li>
<li><a target="_blank" rel="nofollow" href="https://www.datacamp.com/courses/data-visualization-with-ggplot2-part-3">Data Visualization with ggplot2 (Part 3)</a></li>
</ul>
<p><em>*Rankings are based on direct downloads, as opposed to indirect downloads related to dependencies. Direct downloads were measured as of 11/04/2016. Please note these numbers can fluctuate daily. </em></p>
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