1 of 7

What is R and Why You Should You Care?��2022 April 04��David J. Riese II, PhD�Professor�Dept of Drug Discovery and Development�Walker 3211g; x48358 �driese@auburn.edu

Senior Scientist

Cancer Biology & Immunology O’Neal Comprehensive

Cancer Center

University of Alabama-Birmingham

2 of 7

Disclosures

  • David Riese
    • I have been a consultant for ImClone, Bristol-Myers Squibb, Eli Lilly
    • This presentation will not discuss off-label drug use
    • No financial conflict of interest

Slide 2

3 of 7

What is R

Slide 3

R is a language and environment for statistical computing and graphics

  • Open source freeware
  • Operates in MacOS, Linux, UNIX, and Windows
  • Includes a wide variety of statistical tests and graphical analyses

​

But R is not just a free version of GraphPad Prism!

4 of 7

R Is Also a Programming Environment

Slide 4

R is a computing language that features commands that enable data manipulation, calculation, analysis, and graphical display

  • Data handling and storage
  • Operators for arrays and matricies
  • Statistical functions
  • Graphing functions
  • Can be linked to C, C++, and Fortran code
  • Some support for GPU computing and multi-core CPU computing
  • Robust user community with extensive sharing of validated packages and code

5 of 7

What Can You Do With R?

Slide 5

  • Integrate and analyze behavioral and electrophysiology data
  • Organize, parse, and analyze “omics” data
  • Mine, organize, parse, and analyze “big” data

6 of 7

To Learn More About R

Slide 6

Install R Studio and R packages

  • R Studio Education

Many online courses (some are free!)

Troubleshooting R Code

​

7 of 7

R Is Taught At Auburn�Chem 6450 Foundations of R�Dr. Jordan Harshman

Slide 7

“R is an open-source statistical software that allows nearly limitless data manipulation, statistical analysis, and advanced data visualizations for both the social and physical sciences required for quality research in discipline-based education research (DBER). This course will dedicate approximately one half to learning the basics of coding in R for many common tasks found in DBER (focusing on how to independently find and apply new functions as only a portion of functions can possibly be discussed) and the second half on applying these principles into real data from students’ thesis or dissertation (or data can be provided by instructor).”