Topic: Programming

Please join us for a discussion followed by question and answer session on the topic of “R or Python, Which Should I Learn?”.

We will be joined by Andrew Weisman of the Frederick National Laboratory for Cancer Research, Strategic Data Science Initiative (FNLCR, SDSI) and new BTEP Trainer Joe Wu to discuss the utility of R and Python for bioinformatics and data science analyses. You will gain insight into which of these 2 programming languages might be more appropriate for the types of analyses you want to perform. For example:

• Are there bioinformatics or genomics specialized tools in R? (Bioconductor). In Python? (BioPython)
• What kinds of data visualization can I do in R and how do I do them?
• Should I use Python for Machine Learning?
• Can I run Python and R on my local machine?
• Should I use Python or R on NIH High Performance Unix Cluster Biowulf?
• What are Integrated Data Environments (IDE) for R and Python? Why should I use them and how do I access them?
• How do I upload my data into Python? Into R?
• Where should I go next to learn more about R and Python?

This is the second class in the NIH Library Introduction to R Series. A basic understanding of R and RStudio is expected. This class provides a basic overview of R data types, data frames, and factors. Additionally, this class will cover indexing and subsetting data frames, and dealing with missing data. R is a programming language and open source environment for statistical computing and graphics. The R class series is a comprehensive collection of training sessions offered by the NIH Library Data Services program that is designed to teach non-programmers how to write modular code and to introduce best practices for using R for data analysis and data visualization. Each class uses both evidence-based best practices for programming and practical hands-on lessons.

By the end of this class, participants should be able to: define R data frames; characterize how to inspect data frames; list the major methods for describing the content and structure of data frames; illustrate how to index and subset a data frame; describe how to use comparison operators on a data frame; discuss R factors; describe how to convert R factors; describe how to rename factors; discuss options for dealing with missing data in R; and describe how to save data in R.

Participants are encouraged to install R(link is external) and RStudio(link is external) before the class so that they can follow along with the instructor. Attendees will need to download the class data before the class.