Further learning

Recommended beginner resources

R for Data Science, 2e

Best for the broader workflow of importing, transforming, visualizing, and organizing data. Use the Whole Game chapter after completing the first two modules here.

Data Carpentry: R for Social Scientists

Best for short, research-oriented lessons designed for learners with no programming experience.

ModernDive

Best for connecting data visualization, regression, confidence intervals, and statistical inference.

Intro2R

A clear beginner reference for R and RStudio, also recommended in the Luiss course materials.

Posit RStudio guide

Use the official guide for current information about the RStudio interface, projects, scripts, plots, and help.

Posit cheatsheets

Keep the RStudio IDE, data transformation, and ggplot2 sheets available as quick references during practice.

How to use external resources

Do not try to complete several full books at once. For each course topic:

1. complete the corresponding lesson in this repository;
2. use one external resource only for a second explanation;
3. modify the example code with a different variable;
4. write one sentence interpreting the result.

Methodological caution

R can calculate a result even when the research design does not justify the interpretation. Always ask:

• What is the research question?
• What type of variables are involved?
• What assumptions does the method make?
• Is the evidence descriptive, predictive, associational, or causal?
• What uncertainty and limitations should be reported?

See references/resource-benchmark.md for the design review behind this course.