R for reproducible scientific analysis

Introduction to R for novice programmers.

The goal of this lesson is to teach novice programmers to write modular code, independent of programming language.

R was chosen because it is an open source, cross platform software environment popular across different disciplines for statistical analysis and graphics. R comes with an extensive list of third-party packages and documentation, driven by a highly active developers community.

The emphasis of these materials is to give attendees a strong foundation in the fundamentals of R, and to teach best practices for scientific computing, such as breaking down analyses into modular units, task automation, and encapsulation.

Note that this workshop will not teach any statistical analysis.

Disclaimer: A variety of third party packages are used throughout this workshop. These are not necessarily the best, nor are they comprehensive, but they are packages we find useful, and have been chosen primarily for their usability.

Prerequisites

The participants are expected to have attended the shell and git sessions.

Topics

  1. Introduction to R and RStudio
  2. Data types and structures
  3. Seeking help
  4. Project Management
  5. Data Analysis: using data frames
  6. Functions
  7. Generating Multiple Plots
  8. Split-apply-combine
  9. Best practices in R

Other Resources