Three Videos to Supercharge Your R Skills

This is a companion discussion topic for the original entry at https://www.rstudio.com/blog/three-videos-to-supercharge-your-r-skills


Image by Rachael Dempsey

We have many great videos on the RStudio YouTube channel. You can watch folks discuss their data science stories and processes, learn about packages and products, and hear inspiring examples from others in the community.

With hundreds of videos, you have hours of content to watch! To get you started, we want to highlight three videos about R tools that will take your R skills to another level, whether for work, fun, or general learning. Happy watching!

1. Business Reports with R Markdown by Christophe Dervieux

Want to learn how to style your R Markdown reports to tailor them for your organization? Christophe Dervieux shows us various options to customize our HTML output:

  • Using R Markdown’s built-in support for Sass: R Markdown now has built-in support for Sass through the sass R package. Sass is a CSS extension language that helps create CSS rules in more flexible ways than with plain CSS. You can directly supply .scss files to css arguments of your html document. It’s easier to work with CSS rules and variables to apply your style guidelines.
  • Going further with bslib: The bslib package provides tools for customizing Bootstrap themes directly from R. R Markdown’s built-in support allows you to customize your documents without CSS or Sass. You can start with pre-packaged themes or create a custom look.

Christophe also discusses how to develop templates for Office outputs, create PDFs from HTML using the pagedown R package, and more. Watch Christophe’s full talk here:

2. Exploratory Data Analysis by Priyanka Gagneja

Exploratory data analysis, or EDA, is a crucial step of every data science project. However, it can be repetitive and time-consuming.

Priyanka Gagneja shares packages that have helped her automate her EDA process. She walks through:

  • Starting your EDA, feature engineering, and data reporting using DataExplorer.
  • Checking data quality with DataReporter.
  • Calculating summary statistics using skimr.

Once you have completed your EDA, then it’s time to start looking at patterns and relationships. Priyanka demonstrates:

Learn about these packages and more in her talk:

3. Scaling Spreadsheets with R by Nathan Stephens

We use Excel spreadsheets for the same reasons we use R: to wrangle, transform, analyze, visualize, and communicate our data. However, it can be difficult to work in Excel if your data is large or your analysis is complicated. R, however, is an attractive alternative:

  • Increasing file size: While you start hitting the limits of Excel when you move into gigabytes of data, R can handle those files very easily.
  • Handling complexity: Things quickly become complicated in Excel with Visual Basic scripts, various pivot tables, multiple spreadsheets, and so on. These can often be replaced by a simple R script.

When would you start to think about using R rather than Excel? Nathan Stephens shows us that boundary where you might agree that R is the right tool for the job.

Learn More

We hope that you can apply these skills to your future projects. There is a lot more to enjoy!

  • Interested in watching these webinars live? Join the RStudio Enterprise Community Meetup to learn more from industry leaders on data science best practices and the capabilities of open-source software.
  • Want to keep watching? Check out RStudio’s YouTube page for more content from RStudio staff and others in the data science community.