Course Syllabus

Course Description

This course is an introduction to R, a powerful and flexible statistical language and environment that also provides more flexible graphics capabilities than other popular statistical packages. The course will introduce students to the basics of using R for statistical programming, computation, graphics, and modeling.

We will start with a basic introduction to the R language, reading and writing data, and graphics. We then discuss writing functions in R and tips on programming in R. Finally, the latter part of the course will focus on using R to fit some important statistical models, including basic linear regression, generalized linear models and survival analysis.

The class will include a short introduction on how to produce professional looking reports (with pretty plots and tables) that meet the standard necessary for reproducible research and documentation. The first 4 lectures will focus on R essentials. I am happy to tailor the last lectures to students interests. I can provide an introduction to analysis of genomics data in Bioconductor should there be interest among students. 

The class goal is to get students up and running with R such that they can use R in their research and are in a good position to expand their knowledge of R on their own. Course notes are written such that they provide students with a useful and extensive reference manual on R (its over 200 pages!)

Learning Objectives

After taking the course, students will be able to

1. Use R for basic statistical programming, computation, graphics, and modeling,

2. Write functions and use R in an efficient way,

3. Perform basic statistical analysis in R and fit basic statistical models

4. Use R in their own research, and produce reports which meet the standards for reproducible research

5. Be able to expand their knowledge of R on their own.

Intended Audience and prerequisites

There are no formal prerequisites, but in order to appreciate the abilities of R and for the later classes that explore various statistical models, a basic knowledge of statistics is useful. The intended audience is students who need a flexible statistical environment for their research. We do not expect any prior experience with R, but experience with another programming or statistical language may be helpful to a limited extent. Beginning R users with basic knowledge may also find the course useful

Instructors and Teaching Staff:

Primary instructor:

Aedin Culhane

Department of Biostatistics, Dana-Farber Cancer Institute Biostatistics and Computational Biology

Office: Dana-Farber Cancer Institute, Smith 822C

(8th floor of the Smith building at the end of Shattuck St)

Phone: (617) 617-2468

e-mail: aedin@jimmy.harvard.edu

web: http://www.hsph.harvard.edu/aedin-culhane/ 

Teaching Assistant:

BJ Stubbs

Channing Laboratory

e-mail: rebjh@channing.harvard.edu

 Download Syllabus: syllabus.pdf