R for Reproducible Scientific Analysis

An introduction to R for non-programmers using the Gapminder data. Please see https://swcarpentry.github.io/r-novice-gapminder for a rendered version of this material, the lesson template documentation for instructions on formatting, building, and submitting material, or run make in this directory for a list of helpful commands.

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 focuses on the fundamentals of the programming language R, and not on statistical analysis.

The lesson contains more material than can be taught in a day. The [instructor notes page]({{ page.root }}/guide) has some suggested lesson plans suitable for a one or half day workshop.

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.

Maintainers:

• David Mawdsley
• Jeff Oliver
• Tom Wright
• Naupaka Zimmerman