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:
cssarguments of your html document. It’s easier to work with CSS rules and variables to apply your style guidelines.
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:
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:
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.
We hope that you can apply these skills to your future projects. There is a lot more to enjoy!
In this series, we walk through lesser-known tips and tricks to help you work more effectively and efficiently in R Markdown. This third post focuses on features that save you time and trouble.
Many tools used routinely by software developers can also be useful to data scientists.