R for Lunch: schedule

Fall semester, 2023


John Little

In this series we’ll learn to use R for reproducible computational thinking. Each one-hour session builds upon the last. Sessions will be in-person; recordings released at a later date (TBD).


Lunch will NOT be provided but you are welcome to bring your own!

Attendees will use their personal laptops. Preparation: The R application, RStudio, the Tidyverse, and Quarto will be installed, in advance, by attendees. (Instructions will be available.)


Getting started: import data, data wrangling

Thursday - 8/31/23 ; 12:30pm. Register for location.

Learn how to import data following a brief tour of the RStudio IDE and an introduction to coding notebooks. Five essential {dplyr} data wrangling verbs are introduced and a sample visualization is presented.

Data wrangling with dplyr

Friday - 9/1/23 ; 12:30. Register for location.

We explore the five essential {dplyr} data wrangling verbs. We demonstrate and apply data pipes inside code-chunks within coding notebooks, which were discussed in the previous session.

Visualization with ggplot2

Friday - 9/8/23 ; 12:30. Register for location.

We visualize data by leveraging previously discussed reproducible coding techniques, including the {dplyr} verbs. We apply the grammar of graphics to our coding workflow.

Coding with ChatGPT

Friday - 9/15/23 ; 12:30. Register for location.

Ai-assisted coding can improve efficiency. Learn a few basics about Large Language Models (LLMs); LLMs can help and sometimes bedevil us. Discover which LLMs work best with R. Techniques and add-ins are shared to save time and learn more.

Tidy data, pivot, join, and iteration (part 1)

Friday - 9/22/23 ; 12:30. Register for location.

Building on the last session and our goal of efficiency, we create strategies to avoid LLM barriers. Begin to engage the power of functional programming as applied in the tidy-data context.

Functions & {purrr}; iteration part2

Friday - 9/22/23 ; 12:30. Register for location.

FOR loops? Maybe FOR loops are a bit dated. Let’s surf past the next level and apply custom functions to larger quantities of data while using fewer coding steps.

Regression and tidymodels

Friday - 10/6/23 ; 1:30. Register for location.

R is a great tool for academic computational workflow and R is borne from the statistics discipline. Wading only ankle deep, we learn computation techniques for modeling. Please note: this is not a refresher workshop on picking or interpreting models. This is a workshop on efficient syntax to apply models.