2 min read

The R language is one of the top choices of data scientists – in no small part to the great packages and projects created to support it. If you want to expand the power and functionality of your R code, consider one of these popular and amazing options.


ggplot2 is a data visualization package, and widely believed to be one of the best reasons to use R. ggplot2 produces some of the most fantastic graphics and data visualizations you can get. It’s so popular, there’s even a port to use it in Python – R’s great rival. ggplot2 takes care of many of the fiddly details of plotting, leaving you to focus on interpreting your data insight.


R’s always been just about the data – but Shiny lets you take it onto the web! Without knowing any HTML or CSS you can use Shiny to turn your R code into interactive web applications. From interactive visualizations to exceptional web interfaces, you’ll be amazed what you can build with Shiny without a single line of JavaScript.


Use knitr to output your R analyses as dynamic reports in LaTex, Markdown and more. Developed as part of the Literate Program for reproducible research, knitr is the perfect tool for ensuring the the documentation for your R analysis is clear and understandable to all.


Have you ever gotten frustrated with trying to figure out dependency management in R? Packrat is there to help you for all those annoying situations where installing one library makes another piece of code stop working. It’s great for ensuring that your R projects are more isolated, more reproducible, and more portable. How does it work? Packrat stores your package dependencies inside your project directory instead of your personal R library, and lets you snapshot the information packrat needs to recreate your set-up on another machine.


Strings aren’t big or fancy – but they are vital for many data cleaning and preparation tasks (and who doesn’t love clean data?) Strings can be hard to wrangle in R – stringr seeks to change that. It provides a simple modern interface to common string operations, to make them just as easy in R as in Python or Ruby.



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