Intermediate R Programming Course in East Africa

Course outline and learning expectations

This is a tutor-led course. The outline shows what your tutor will cover; teaching materials and examinations are provided directly to enrolled students.

What you will learn

• Build reliable data-cleaning pipelines with dplyr and tidyr.
• Combine multiple datasets using joins, keys and validation checks.
• Work confidently with dates, text, categories and missing-data patterns.
• Write reusable functions and apply them efficiently across columns, groups and files.
• Create layered, publication-quality visualisations with ggplot2.
• Perform and interpret common statistical tests and regression models in R.
• Produce reproducible analytical reports with Quarto and well-organised R projects.
• Complete an end-to-end East African data analysis that is clear, auditable and decision-ready.

Modules

1. 1

   Professional R Projects and Data Diagnostics

   Designing a repeatable project structure for source data, scripts, functions and outputs.Using relative paths and the here package to reduce broken file references.Inspecting data dimensions, types, ranges, uniqueness and missingness.Recording assumptions and creating a simple data-quality log.
2. 2

   Advanced Data Wrangling with dplyr

   Complex filtering, grouped calculations and window functions.Using across, case_when, count, distinct and slice tools effectively.Reshaping analytical logic into readable pipelines.Validating row counts, group totals and derived indicators.
3. 3

   Tidy Data, Reshaping and Joins

   Diagnosing wide, long and nested data structures.Using pivot_longer and pivot_wider for analysis-ready data.Applying inner, left, full, semi and anti joins.Checking join keys, duplicates, unmatched records and many-to-many relationships.
4. 4

   Dates, Text, Factors and Missingness

   Parsing, rounding and calculating with dates using lubridate.Cleaning and extracting text with stringr and regular expressions.Reordering, collapsing and displaying categories with forcats.Profiling missing-data patterns and documenting justified treatment choices.
5. 5

   Reusable Functions and Functional Programming

   Designing functions with clear inputs, outputs and validation.Applying functions with map-family tools from purrr.Working across multiple columns, groups or files without copied code.Handling failures safely and returning consistent results.
6. 6

   Data Visualisation with ggplot2

   Layering data, aesthetics, geometries, scales, coordinates, facets and themes.Designing honest comparisons, distributions, trends and relationships.Adding annotations and reference lines that clarify the analytical message.Exporting accessible, publication-ready graphics at suitable dimensions.
7. 7

   Statistical Analysis and Model Interpretation

   Translating a practical question into variables, hypotheses and an analysis plan.Correlation, group comparisons, confidence intervals and common significance tests.Simple and multiple linear regression; introductory logistic regression.Checking assumptions, tidying model output with broom and communicating uncertainty.
8. 8

   Reproducible Reporting and Capstone

   Creating Quarto documents with narrative, code, tables and figures.Controlling code visibility, output, parameters and document structure.Separating data preparation, analysis and presentation scripts.Building an end-to-end report that another analyst can rerun and review.