An introduction to R for non-programmers using the Gapminder data. Please see https://datacarpentry.org/r-intro-geospatial 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 revise best practices for using R in data analysis. This lesson is a modification of the Software Carpentry: Programming with R, and is part of the Data Carpentry Geospatial Curriculum. It introduces the R skills needed in the Introduction to Raster and Vector Geospatial Data lesson.

R is commonly used in many scientific disciplines for statistical analysis and its array of third-party packages. These materials are designed to provide attendees with a concise introduction in the fundamentals of R, and to introdue best practices for scientific computing: breaking down analyses into modular units, task automation, and encapsulation, before getting started with working with geospatial data.

Note that this workshop focuses on the fundamentals of the programming language R, and not on statistical analysis.

The lesson contains material than can be taught in about 4 hours. The instructor notes page has some suggested lesson plans suitable for a one or half day workshop.

• Johanna Bayer
• Mike Mahoney
• Alber Sánchez

• Leah Wasser
• Jemma Stachelek
• Tyson Swetnam
• Lauren O'Brien
• Janani Selvaraj
• Lachlan Deer
• Chris Prener
• Juan Fung