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.
- Explain why Base R is a unique programming language.
- Summarize how to install R for Macintosh, Windows, Linux, or Unix.
- Name the IDE that does not have support for R.
- Describe two ways to set up R Tools for Visual Studio.
- Identify the three ways R programs can be executed.
- List two functions that sqldf adds to Base R.
Skill Level Intermediate
- [Mark] Hi, I'm Mark Niemann-Ross and in this course, I'll help you get started with the R programming language. The R programming language is designed for the needs of the scientific research community. Unfortunately, it can be un-intuitive and getting started can be a daunting task. I'll show you some of the different versions of R, how to install them, and how to customize them. For each version, I'll discuss a comparison task and an optimal task. We'll spend a bit of time discussing how to program R interactively and as a command-line app.
I'll also discuss a few of the most popular packages used to customize R for specific purposes, SQLDF, RGL, data.table, and cluster. These are only a few of the thousands of available packages. So, let's get started with R for data science, setup and start.
Integrating Tableau and R for Data Sciencewith Ben Sullins1h 10m Intermediate
1. Set Up Base R
2. Set Up tidyverse R
How to install the tidyverse1m 24s
3. Set Up Microsoft R Open
4. Set Up Bioconductor R
5. First Look at R Programming
6. Most Common R Packages
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