Day 3. Data Analysis and Graphics with R

R is powerful data programming language and environment for statistical computing, data analysis and graphics. R is typically used to explore and understand data in an open-ended, highly interactive, iterative way. Learning R will give you the freedom to experiment and problem solve during data analysis — exactly what we need as bioinformaticians and data scientists.

Before getting our hands dirty working with real data in R, we need to learn the basics of the R language. Even if you’ve poked around in R and seen these concepts before, I would still recommend you follow along and complete the free online interactive learning tutorial “TryR” ( This will take you through a gentle introduction to R syntax and some of the major R data structures (called vectors, matrices data.frames and lists) that we will cover in more detail in class .

Schedule (Tentative):

Session Time Topics
I 9:00-10:15 AM R Language Basics and the RStudio IDE
  10:15-10:30AM Coffee Break
II 10:30-12:00 AM R Data Structures and Functions
  12:00-1:00PM Lunch
III 1:00-2:15 PM Data Exploration and Visualization in R
  2:15-2:30 PM Coffee Break
IV 2:30-4:00 PM Working with R packages from CRAN & Bioconductor


Barry Grant (BJG)
Jacob Kitzman (JOK) Ryan Mills (RM)

Topics (Tentative):

I) R Language Basics and the RStudio IDE [1:15 hr] (Slides) (Exercise-1) BG

  • What is R?
  • Motivation: Why use R?
  • Getting started with R and the RStudio IDE (integrated development environment).
  • Using R.
    • Getting help.
    • Major data structures (vectors, matrices and data.frames).
    • Using functions (arguments, vectorizion and re-cycling).
    • R scripts and reproducibility.
    • Optional extra: working with strings.

—- Coffee Break [15 mins] —

II) R Data Structures and Functions [1:30 hr] (Slides)

  • Major data structures (vectors, matrices, lists and data.frames).
    • Indexing and vectorizion.
  • Using and writing functions (arguments, more on vectorizion and re-cycling).

—- Lunch Break [1 hr] —

III) Data Exploration and Visualization in R (Slides) JOK

  • Getting your data into R.
    • Import data in various formats (both local and from online sources).
  • The exploratory data analysis mindset.
  • Data visualization best practices.
  • R base graphics and the grammar of graphics.
    • Simple base graphics (scatterplots, histograms, bar graphs and boxplots).
    • Building more complex charts with ggplot.

—- Coffee Break [15 mins] —

IV) Working with packages from CRAN & Bioconductor [1.30 hr] (Slides) BG

  • CRAN - the Comprehensive R Archive Network.
  • Bioconductor bioinformatics package system.

—- End/Wrap-Up —


JIF data

Reference material

RStudio cheatsheet: A well designed reference card for RStudio features.

Try R: An excellent interactive online R tutorial for beginners.

R for Data Science: A brand new O’Reilly book, available free online, that will teach you how to do data science with R.

Class notes on R language basics.

Class notes on useful R functions for working with strings.