R for reproducible scientific analysis

Introduction to R for non-programmers using gapminder data.

The goal of this lesson is to teach novice programmers to write modular code and best practices for using R for data analysis. R is commonly used in many scientific disciplines for statistical analysis and its array of third-party packages. We find that many scientists who come to Software Carpentry workshops use R and want to learn more. 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: breaking down analyses into modular units, task automation, and encapsulation.

Note that this workshop will focus on teaching the fundamentals of the programming language R, and will not teach statistical analysis.

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.


Have attended the Shell and Git sessions.


  1. Introduction to R and RStudio
  2. Project Management
  3. Seeking help
  4. Data types and structures
  5. Data structures: data frames
  6. Subsetting data
  7. Functions
  8. Plotting
  9. Vectorisation
  10. Control flow
  11. Writing data
  12. Split-apply-combine
  13. Wrapping up

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