Welcome to the Summer 2026 Pre-Assignment. These assignments build R fluency before Math Camp by using one cleaned country-year development dataset across all five parts.
This repo includes a simple static website in docs/index.html that
walks students through the summer assignment process. The website is
separate from Posit Cloud: Posit Cloud remains the environment where
students edit and submit the R Markdown assignments.
main: current stable landing branch for the repository. This should reflect the current year's approved guide and project overview.2026-refresh: website branch. GitHub Pages publishes fromdocs/on this branch.posit-cloud-2026: student Posit Cloud branch. This branch keeps the project focused on the files students need in Posit Cloud: assignments, data, the project file, README, and the refresh script.archive/2025: frozen 2025 branch.
v2025.final: final 2025 snapshot.v2026.1-website: reviewed Summer 2026 website release.v2026.1-posit: reviewed Summer 2026 Posit Cloud assignment release.
- Google Account: You will need a Google account to manage your projects and access the Posit Cloud assignments.
- Access Assignments: You can access the summer assignments using the Posit Cloud project link provided by the teaching team.
- Save a permanent copy: Save the project to your own Posit Cloud workspace before editing any files.
The five assignments use the same data backbone so students can focus on R skills instead of learning a new dataset each time.
- Part 1: R Basics for Development Data
Objects, data types, logical values, functions, vectors, comments, and one controlled debugging exercise. - Part 2: Filtering and Inspecting Country-Year Data
read_csv(),head(),glimpse(),distinct(), coherentfilter()use,arrange(), lightmutate(), data dictionaries, and missingness. - Part 3: Visualizing Development Patterns
ggplot(), line plots, scatterplots, bar charts, color, facets, labels, and descriptive interpretation. - Part 4: Summarizing Countries, Regions, and Time Periods
group_by(),summarise(), means, medians, denominators, and missing-data caveats. - Part 5: Mini Development Diagnostic Memo
A structured country-peer diagnostic memo using filters, plots, summaries, and a short non-causal interpretation.
In your Posit Cloud workspace, you will find the following structure:
/summer-assignments
assignments/
R Summer Assignment 1.Rmd
R Summer Assignment 2.Rmd
R Summer Assignment 3.Rmd
R Summer Assignment 4.Rmd
R Summer Assignment 5.Rmd
data/
development_indicators_2026.csv
development_indicators_dictionary_2026.csv
figs/
scripts/
refresh_development_indicators.R
The assignment files live in assignments/. The shared CSV snapshot and
data dictionary live in data/. When an assignment reads
../data/development_indicators_2026.csv, the .. means “go up one
folder” from assignments/ and then enter the data/ folder.
The assignments use one cleaned country-year dataset created for this course. Most indicators come from the World Bank World Development Indicators. Governance indicators come from the Worldwide Governance Indicators. The data are stored as a local CSV snapshot so everyone works with the same values and the assignments knit reliably.
The data dictionary records each variable’s label, source, source
indicator code, unit, and interpretation notes. The instructor refresh
script in scripts/refresh_development_indicators.R documents how the
snapshot was generated.
Knitting runs an R Markdown document from top to bottom in a fresh R session. This checks whether the work is reproducible: the code, output, and written answers can be rebuilt in order. Running chunks manually is useful while learning, but a successful knit is the best check that an assignment is complete and self-contained.
We use Posit Cloud as the primary platform for the summer assignments. Posit Cloud lets students write, execute, save, and knit R Markdown files in a browser.
R is the programming language used for data analysis. RStudio is the interface for writing and running R code. Posit Cloud provides RStudio online.
R Markdown combines instructions, code, output, and written answers.
Students submit the completed .Rmd files to Canvas.
AI tools can be useful for debugging error messages and clarifying what a specific R command does. They are not a substitute for running the code, checking the output, and explaining the analysis. Each assignment asks for a brief AI-use note; the capstone asks for a fuller reflection.
Complete the assignments in Posit Cloud. For each part, knit successfully
and submit the completed .Rmd file to Canvas unless the teaching team
gives different instructions.
If you get stuck, check the guide website and FAQ first. If you still need help, use Slack or the course support channel and include the assignment number, the code or error message, and what you already tried.