The goal of this introductory workshop 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. 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.
This workshop is based on the Software Carpentry lesson "R for reproducible scientific analysis" and covers:
- Introduction to R and RStudio
- Project Management With RStudio
- Data Structures
- Exploring and sub-setting your data
- Writing functions
- Introduction to the tidyverse (dplyr, tidyr, ggplot2)
- Dataframe manipulation with dplyr and tidyr
- Creating Publication-Quality Graphics with ggplot2
- Producing reports
This lesson assumes you have R and RStudio installed on your computer.
R can be downloaded here.
RStudio is an environment for developing using R. It can be downloaded here. You will need the Desktop version for your computer.