R Workshops

This semester, I am offering a series of workshops on R. They will be held Fridays at 3:30 in the DigiLab at the UGA Main Library.

Spring 2018 Series

Intro to R (Part 1) (January 19): In this workshop I cover the basics of R itself. I talk about the differences between R and RStudio and I helped folks get both installed and running on their computers. We created a simple “Hello, World!” script using R.

Intro to R (Part 2) (January 26): Now that R is ready to go, this workshop will cover the basics of the R language. It will also be a very simple introduction to some core computer coding concepts like declaring variables and variable types.

Building interactive webpages in R: Introduction to Shiny (February 2 and 9): Due to demand, the Shiny workshop day will be early in the semester. Shiny is an R package that allows you to make your own interactive web pages. An entire semester could be devoted to shiny, so these two days will cover just the essentials.

Visualization I: Introduction to ggplot2 (February 16): ggplot2 is a widely used package that allows for high-quality visualizations. In this workshop I cover the basic syntax and how to make some simple types of plots.

Visualization II: Customizing plots in ggplot2 (February 23): The basic plots in ggplot2 are good, but often you’ll want to customize them in some way. In this workshop we’ll cover how to mess with properties of the plots like the axes, colors, and legends to make the plot work better for you.

Visualization III: Advanced topics in ggplot2 (March 2): In this workshop we go beyond the simple customization techniques and move on to modifying many other aspects of the plot. Time permitting, I’ll show how to create your own themes so that they match your powerpoint themes to create a more appealing presentation.

Communicating to your audience with R Markdown (March 26): R Markdown is a way to create different types of documents using R (pdfs, word files, html files). In this one-day crash course, I’ll show how to make R Markdown files and the kinds of things they would be useful for.

Clean and tidy data: The Tidyverse (March 23 and 30): The Tidyverse is a suite of packages that helps you wrangle your data. In this two-part workshop, we’ll learn some of the common functions in the tidyverse and compare them to base R, showing that there are multiple ways to accomplish the same task in R.

Special topics: Regression and mixed-effects modeling (April 6): In more and more fields, quantitative analysis is the norm. I can’t begin to cover everything about fitting statistical models to your data, but I’ll cover some introductory concepts to hopefully guide you in the right direction for further study.

Special topics: Network analysis (April 13): Network analysis is a fascinating field on its own, and learning to create and analyze visualizations of network data can be helpful for some studies. This workshop will cover some basic visualizations and statistical analysis of network data.

Special topics: Working with text (April 20): Most topics in this series have covered numbers and how to work with them. In this final presentation, I introduce the stringr package (part of the Tidyverse suite), and how you can use it to your advantage when working with text in R.

Fall 2017 Series

In fall 2017, I offered three workshops that broadly covered some basic R skills. The slideshows at available here, as well as the sample data that was used for all three installments.

In Introduction to R (September 13, 2017): In this workshop I go over the basics of R including installation, basic functions, loading your data into R, filtering, visualizations, and where to find help.

ggplot2 (October 12, 2017): Here I discuss the popular ggplot2 package, a collection of functions that makes it easy to create stunning, professional-quality visualizations. Specifically, I cover basic plots, how to modify them, and some of my thoughts on data visualization.

tidyverse (November 10, 2017): This workshop discusses a collection of R packages known as the tidyverse. This includes packages like dplyr, tidyr, stringr, and ggplot2. They are designed with the same underlying mechanisms so they they work harmoneously together to make it easy to import, tidy, and reshape your data for future analysis.