Created By: Joel Singley, Jacob Bueno de Mesquita, Rebecca Distefano, Jimoh Fatoki, Victoria Heimer-McGinn, Robert Holmberg, Allison Marn, & Charles Nicholson
This library provides a collection of short, single-session activities that integrate computer coding (mostly in R) into disciplinary courses. These activities are designed to help students engage with data in meaningful ways that support course-specific topical learning outcomes while building familiarity and confidence in coding. The materials include hands-on exercises, datasets, and guided instruction to facilitate learning across various disciplines.
The provided activities are intended for use in undergraduate courses across disciplines where data analysis and visualizations can enhance learning. They are designed to:
- Support students with minimal or no prior coding experience
- Provide scaffolded exercises to build confidence and fluency in the chosen programming language.
- Reinforce data literacy within disciplinary contexts
- Offer flexbility in implementation, whether used as in-class exercises, homework, or lab activities.
In general, these activities are provided as exmaples than can be used to build your own similar lessons to meet the needs of your specific course. We encourage users to treat them as a set of templates, even if the exact topics or datasets don't align with what you teach.
The following table summarizes the activities created by PLC members. The materials for each are available in the lessons folder of this repository.
| Discipline | Creator | Description | Prior Coding Experience | Key Coding Skills |
|---|---|---|---|---|
| Environmental Science/Ecology | Joel Singley | The activity first introduces a few uses of R and associated vocabulary. Then focuses predominantly on visualizing penguin morphology data to answer questions about inter- and intra-species trait variation. | None required | Fundamentals of R coding and data structures; visualizing bi-variate relationships; utilizing categorical variables for visual grouping |
| Business and Management | Jimoh Fatoki | The activity explores “Auto” data available in the “ISLR” library in R. Students learn data manipulation, visualization, and interpretation to answer interesting questions. | Prior experience in coding is not required. | Understanding data types and structures; installing packages and loading data; using functions to explore data; manipulating data using dplyr; plotting with ggplot(); reusing/modifying code to achieve similar objectives |
| Biology/Marine Biology | Rob Holmberg | The activity summarizes and visualizes population densities of marine organisms counted using quadrats in the intertidal zone. | None required | Importing data from spreadsheets; managing dataframes; calculating averages; creating bar charts |
| Psychology | Rebecca Distefano | This activity focuses on word learning in young children. It introduces basic functions in R, details data cleaning, and introduces data visualization and basic statistical analysis. | None required | Importing data from CSV; handling missing data; renaming variables; scatterplots and boxplots; t-tests |
| Public Health/Epidemiology | Jacob Bueno de Mesquita | occ_health_epi_investigation_oilspill — Students investigate an occupational health dataset (modeled on Deepwater Horizon cleanup crew data) to identify hypotheses about differences in exposure and health outcomes between worker groups. | No experience required, but assumes exposure to R skills built throughout the course. A guide and sample code are provided. | Full analysis of occupational health data: summarizing and visualizing data; developing hypotheses; regression analyses with confounding/effect modification; using provided code to produce public health recommendations for worker protection |
| Public Health/Epidemiology | Jacob Bueno de Mesquita | plot1_skillsdemo — Get familiar with the R environment, make a plot, and understand relationships between code and output. Experiment with visualization options (colors, line/point size, labels). | None | Basic plotting; understanding code–plot relationships; modifying aesthetic parameters |
| Public Health/Epidemiology | Jacob Bueno de Mesquita | exploring_births_data_skillsdemo — Using a births cohort dataset to practice exploring data and understanding variables; includes histogram generation and sensitivity/specificity exploration. | None | Reading data from an R package; identifying variable names; exploring a dataset; generating histograms; computing sensitivity and specificity |
| Public Health/Epidemiology | Jacob Bueno de Mesquita | ecological_casecontrol_studies_activity — Using real Rhode Island Lyme disease study data to explore early analytical epidemiology. | None | Generating a dataset from a script; understanding variables; scatterplots; boxplots; generating and interpreting odds ratios to support public health action |
| Public Health/Epidemiology | Jacob Bueno de Mesquita | regression_skillsdemo — Uses the births cohort dataset to practice regression skills, building on previous work. | None | Reading data from a package; boxplots; working with categorical and continuous variables; linear and logistic regression; interpreting regression output to support public health conclusions |
These resources were originally created through a professional learning community for faculty at Roger Williams University during the 2024–2025 academic year. For more information or questions, please contact the PI of the PLC, Joel Singley (jsingley@rwu.edu). This work was supported by funding from Academic Affairs at RWU and subaward #00002565 from the NASA Rhode Island Space Grant Program administered by Brown University. Any opinions, findings, and conclusions or recommendations expressed are solely those of the authors.