| Abhijit Dasgupta, PhD | Phone: (301) 385-3067 (Office) |
| Email: adasgupta@araastat.com | |
| Eugen Buehler, PhD | Phone: (301) 637-9774 (Office) |
| Email: eugencbuehler@gmail.com |
| Joe Jessee | Email: jjessee1@jhu.edu |
Contact Joe if you want access to the Slack Channel for the class.
Lectures: Wednesdays, 5-7 PM (Buehler) or 7-9 PM (Dasgupta), Bldg 10, Room B1C206 in the FAES Academic Center.
Course Description
The goal of this course is to introduce biomedical research scientists to R as an analysis platform rather than a programming language. Throughout the course, emphasis will be placed on example-driven learning. Topics to be covered include: installation of R and R packages; command line R; R data types; loading data in R; manipulating data; exploring data through visualization; statistical tests; correcting for multiple comparisons; building models; and. generating publication-quality graphics. No prior programming experience is required.
Learning Objectives
Run R GUI and make use of command line features, including command history and help pages
Find and make use of the extensive libraries (R add-ons) available for analyzing biological and other forms of data
Load, manipulate, and combine data to make it amenable to further analyses
Visualize data with extensive graphics capabilities of R (including ggplot)
Use appropriate statistical tests on data within R that will conform to standards expected in scientific journals
Required Books
There are no required books for this class. We will recommend online resources for the use of R in data analysis.
Course Schedule and Tentative Lecture Topics
| Date | Topic |
|---|---|
| Sep. 12 | Introduction to R and RStudio. Basic Data Types |
| Sep. 19 | Data Frames, Matrices and Lists |
| Sep. 26 | Introduction to Tidy Data and the Tidyverse: Data Munging |
| Oct. 03 | Exploring Data through Visualization. |
| Oct. 10 | Basic Statistical Analysis with R: Summary statistics, Hypothesis tests |
| Oct. 17 | Building Statistical Models with R: Linear Regression, Formula interface |
| Oct. 24 | R Packages. Importing & exporting data. “Apply” family of functions |
| Oct. 31 | Importing and exporting data. Data manipulation. “Apply” family of functions. Missing data |
| Nov. 07 | Tidyverse II: Data manipulation with purrr and *apply |
| Nov. 14 | RMarkdown and other reporting packages |
| Nov. 21 | Basic graphics with ggplot. |
| Nov. 28 | Advanced Graphics with ggplot (facets, etc). Generating publication quality graphics. |
| Dec. 05 | Bioconductor: Bioinformatics in R . Multiple Comparisons |
| Dec. 12 | Project Presentations |
Class Project
Grading will be based on your class project, to be completed by and presented at our final class. Each student will demonstrate the learning objectives of the class on their own set of data. Ideally, the data used will actually be the student’s own data, or at least data familiar to them. Within their presentation, students will describe the data and how it was loaded and analyzed within R/RStudio. Instructors will evaluate the projects based on whether they demonstrate the learning objectives, with one point for each of following demonstrated: importing data, data manipulation, statistical analysis, use of a package, and data visualization. The point total will then be translated to a letter grade: 0 or 1 =F, 2=D, 3=C, 4=B, 5=A.
POLICY ON ACADEMIC INTEGRITY
The FAES Graduate School at NIH prides itself on providing quality educational experiences and upholds the highest level of honesty, integrity, and mutual respect. It is our policy that cheating, fabrication or plagiarism by students is
not acceptable in any form. If a student is found to be in violation of any, or all of the below, his/her credits will be forfeited, and he/she will not be allowed to enroll in future courses or education programs administered by FAES.
• Cheating is defined as an attempt to give or obtain inappropriate/ unauthorized assistance during any academic exercise, such as during examination, homework assignment, class presentation.
• Fabrication is defined as the falsification of data, information or citations in any academic materials.
• Plagiarism is defined as using the ideas, methods, or written words of another, without proper acknowledgment and with the intention that they be taken as the work of the deceiver. These include, but are not limited to, the use of published articles, paraphrasing, copying someone else’s homework and turning it in as one’s own and failing to reference footnotes. Procuring information from online sources without proper attribution also constitutes plagiarism.