R and Statistics Labs
The five labs form the quantitative spine of the course. They teach R programming and foundational statistics through practical survey problems, with the live Univision project providing the principal application.
No prior mastery of R is required. Most lab work will be completed together during class: the instructor will introduce the statistical idea, build the code with students, pause for interpretation and troubleshooting, and model how to check the results. Students will then complete a shorter On Your Own section individually.
Lab materials
Lab 1: Your First Reproducible Survey Analysis
Lab 2: Describing and Visualizing Survey Responses
Read Lab 2 · Download the Lab 2 student template
The website versions display the lab instructions without executing the R code. The downloadable student templates contain executable R cells. Save the template inside your local copy of the course repository before beginning.
Lab sequence
| Lab | R and statistical foundations | Survey application |
|---|---|---|
| 1 | RStudio projects, Quarto, objects, functions, packages, importing data, variable types, missing values, filtering, recoding, summarizing, and readable code | Importing a survey, reading its codebook, cleaning variables, and creating a documented analysis file |
| 2 | Distributions, proportions, means and medians, cross-tabulations, grouped summaries, scales and indexes, and ggplot2 | Describing respondents, evaluating measures, and visualizing survey responses accurately |
| 3 | Probability, random sampling, simulation, sampling distributions, standard errors, margins of error, confidence intervals, and basic hypothesis tests | Understanding why poll estimates vary and evaluating topline and subgroup differences |
| 4 | Survey weights, weighted estimates, effective sample size, design effects, and the R survey package |
Comparing weighted and unweighted results and producing defensible estimates from survey data |
| 5 | Linear and logistic regression, categorical predictors, interactions, predicted probabilities, tables, graphics, and Quarto reporting | Explaining survey outcomes and producing a reproducible section of the final report |
Lab format
Each lab has two connected parts.
Guided workshop
Most of the lab is completed together during class. We will:
- introduce the statistical logic through a survey question;
- build and run the R code step by step;
- interpret the output in plain language;
- diagnose common errors;
- check whether the result is plausible; and
- connect the technique to the Univision project.
On Your Own
Students will independently complete a shorter application using a new variable, question, or subset of the data. This section assesses whether each student can adapt the guided workflow, make appropriate choices, and explain the result.
The individual portion should normally be finishable in approximately 60–90 minutes after class when a student has participated in the guided workshop.
What students should learn
By the end of the lab sequence, students should be able to:
- create and navigate an RStudio project;
- import and inspect common survey-data formats;
- use a codebook and distinguish variable types;
- clean, label, recode, and document variables;
- identify and handle missing values appropriately;
- calculate and interpret descriptive statistics;
- create cross-tabulations and clear survey graphics;
- explain sampling variability, standard errors, margins of error, and confidence intervals;
- evaluate basic differences across groups;
- calculate and interpret weighted survey estimates;
- recognize the consequences of extreme weights and design effects;
- estimate and interpret introductory linear and logistic regression models;
- communicate interactions with predicted probabilities and graphics; and
- produce a reproducible Quarto analysis.
The objective is not memorizing formulas or R commands. Students should understand the logic of a method, recognize when it is appropriate, implement it with a documented workflow, check the output, and explain the result to a nontechnical audience.
Submission and evaluation
Students submit the completed Quarto source file and rendered output. Labs are evaluated on:
- completion of the independent section;
- correct statistical reasoning;
- functioning and readable code;
- appropriate checks and documentation;
- accurate interpretation; and
- reproducibility.
Because programming involves troubleshooting, a minor syntax error will matter less than evidence that the student understands the intended analysis, can diagnose the problem, and can explain what the code and output mean.
Relationship to existing materials
The repository already contains useful foundations in introductory R, data manipulation, probability, inference, regression, predicted probabilities, and a Latino National Survey case study. These materials will be adapted into the five-lab sequence. Outdated syntax will be updated as as needed.