R
Undergraduate · CS / Programming
Syllabus focus
Topics typically covered
Standard syllabus
R basics
- RStudio workflow; scripts, console, and projects
- Vectors, matrices, lists, and data frames
- Indexing, subsetting, and recycling rules
- Factors, dates, and missing data (NA) handling
- Reading/writing CSV, RDS, and common file formats
Analysis and visualization
- Summary statistics and exploratory data analysis
- Base and ggplot2 graphics: histograms, scatter, faceting
- Hypothesis tests and confidence intervals (intro)
- Linear regression with lm(); model summaries and diagnostics
- dplyr/tidyverse: filter, select, mutate, group_by, summarize
Reproducible R workflows
- R Markdown/Quarto reports
- Project-oriented workflows and renv (intro)
- Tidy data principles and reshaping
- Functions, scoping, and package structure (intro)
- Debugging and profiling R code
- Version control habits for analysis projects
STEM / applied
Reproducible research
- R Markdown and Quarto for reports
- Version control for analysis projects
- Functional programming with apply family and purrr (intro)
- Joining datasets with dplyr joins
- Publishing HTML/PDF reports for stakeholders
Domain applications
- Biostatistics workflows: survival and clinical tables (intro)
- Econometrics-style panel data (intro)
- Spatial data with sf/ggplot (survey)
- API packages and web scraping ethics (intro)
- Performance with data.table for large tables (intro)
Analysis delivery
- Building publication-quality ggplot2 figures
- Modeling workflows beyond lm() (GLM survey)
- Shiny dashboards at introductory level
- Connecting R to databases (DBI intro)
- Communicating uncertainty to non-technical audiences
- Capstone: reproducible analysis notebook with narrative
Notes
Offered in statistics, social science, and CS-adjacent programs. Package emphasis (tidyverse vs base R) varies.