R workshop series, a tidyverse approach

Ryan Womack, rwomack@rutgers.edu

2020-09-15 version

Materials for the "tidyverse-centric" R workshop series

First offered at Rutgers (New Brunswick Libraries) in Fall 2019

Full Playlist of screencasts for the Fall 2020 series at

https://www.youtube.com/playlist?list=PLCj1LhGni3hNxsLsVNHX6V1LICyklHl2a

R for data analysis: a tidyverse approach

Use R_for_data_analysis.md

The session introduces the R statistical software environment and basic methods of data analysis, and also introduces the "tidyverse". While R is much more than the "tidyverse", the development of the "tidyverse" set of packages, led by RStudio, has provided a powerful and connected toolkit to get started with using R. Note that graphics and data manipulation are covered in subsequent sessions.

R graphics with ggplot2

Use R_graphics_with_ggplot2.R

The ggplot2 package from the tidyverse provides extensive and flexible graphical capabilities within a consistent framework. This session introduces the main features of ggplot2. Some prior familiarity with R is assumed (packages, structure, syntax), but the presentation can be followed without this background.

R data wrangling with dplyr, tidyr, readr and more

Use R_data_wrangling.R

Some of the most powerful features of the tidyverse relate to its abilities to import, filter, and otherwise manipulate data. This session reviews major packages within the tidyverse that relate to the essential data handling steps require before (and during) data analysis.

R for interactivity: an introduction to Shiny

Use R_for_interactivity_with_Shiny.md

Shiny is an R package that enables the creation of interactive websites for data visualization. This session provides a brief overview of the Shiny framework, and how to edit and publish Shiny sites in RStudio (with shinyapps.io). Familiarity with R/RStudio is assumed.

R for reproducible scientific documents: knitr, rmarkdown, and beyond

Use R_for_reproducibility.md

The RStudio environment enables the easy creation of documents in various formats (HTML, DOC, PDF) using Rmarkdown, while knitr allows the incorporation of executable R code to produce the tables and figures in those documents. This session introduces these concepts and other packages and practices supporting reproducibility with the R environment.