R Programming in Data Science: Setup and Start

Learn how to choose and install a version of R-base R, tidyverse R, R Open, or Bioconductor R-and install useful R packages.

R is powerful, but not intuitive. There is a strong and diverse R ecosystem, and data scientists are expected to mix and match from the different versions and packages. Before you can even begin programming, you have to choose, install, and set up R to work for you.

In this course, Mark Niemann-Ross provides a direct and efficient introduction to the many flavors of the R programming language, including base R, tidyverse R, R Open from Microsoft, and Bioconductor R. He also provides a peek at programming with R interactively and via the command line, and introduces some helpful packages for working with SQL, 3D graphics, data, and clusters in R. At the end of this short course, you will have installed a version of R along with a few core libraries and an optimized IDE.

Introduction

• Welcome
• Why learn R?
• Base R, tidyverse, Microsoft R, and others
• What you should know before watching this course
• Using the exercise files

1. Set Up Base R

• Why is base R unique?
• Check to see if R is installed
• How to install base R
• Development environments for base R
• A comparison task in base R
• An optimal task for base R

2. Set Up tidyverse R

• Why is the tidyverse unique?
• How to install the tidyverse
• A comparison task using the tidyverse
• An optimal task for the tidyverse

3. Set Up Microsoft R Open

• Why is Microsoft R Open unique?
• How to install Microsoft R Open
• Development environments for Microsoft R Open
• A comparison task using Microsoft R Open
• An optimal task for Microsoft R Open

4. Set Up Bioconductor R

• Why is Bioconductor R unique?
• How to install Bioconductor R
• Development environments for Bioconductor R
• A comparison task using Bioconductor R
• An optimal task for Bioconductor R

5. First Look at R Programming

• Use R interactively
• Use R with command-line arguments

6. Most Common R Packages

• sqldf
• rgl
• data.table
• cluster

Conclusion

• Next steps