R Programming Course for Beginners in Uganda

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

• Explain what the R programming language is and how it supports data analysis, statistics and visualisation.
• Install and use R and RStudio, organise an R project and run scripts confidently.
• Create, inspect and modify objects using variables, operators and core R syntax.
• Work with vectors, matrices, lists, factors and data frames.
• Handle missing values, data types and common coding errors.
• Use conditions, loops and beginner-friendly functions to solve repeatable tasks.
• Import, clean, summarise and export practical datasets in common formats.
• Create clear charts and communicate findings from a small Uganda- or Africa-focused data project.

Modules

1. 1

   Getting Started with R and RStudio

   What R is, where it is used and why it matters for data work in Uganda and Africa.Installing R and RStudio; understanding the console, script editor, environment, files, plots and help panes.Creating an R project, setting a working structure and writing the first script.Using comments, help pages and simple package commands.
2. 2

   R Syntax, Objects and Operators

   Assignment, object names, expressions and readable coding conventions.Numeric, character, logical and complex values.Arithmetic, comparison and logical operators.Inspecting objects with class, typeof, length and structure tools.
3. 3

   Vectors and Missing Data

   Creating, combining, naming and indexing vectors.Vectorised calculations, recycling and type coercion.Missing values, NaN and infinity; detecting and handling incomplete observations.Useful summary functions and safe comparisons.
4. 4

   Matrices, Lists, Factors and Data Frames

   Choosing the right R data structure for a task.Creating and subsetting matrices and lists.Representing categories with factors and ordered factors.Creating, inspecting and selecting rows and columns in data frames and tibbles.
5. 5

   Decisions, Loops and Functions

   Writing if, else and vectorised conditional logic.Using for and while loops appropriately.Creating functions with arguments, defaults and return values.Understanding local variables and simple function scope.Practical application: Write reusable functions that classify records and calculate indicators across several
6. 6

   Importing, Cleaning and Exporting Data

   Importing CSV, text and Excel files.Checking column names, data types, duplicates and missing values.Selecting, filtering, arranging and creating columns with beginner-friendly tidyverse verbs.Exporting clean datasets and keeping source data separate from outputs.
7. 7

   Summaries and Data Visualisation

   Calculating totals, averages, medians, percentages and grouped summaries.Choosing an appropriate chart for categories, distributions, comparisons and trends.Creating bar charts, line charts, histograms and scatter plots with ggplot2.Writing clear titles, labels, captions and accessible colour choices.
8. 8

   Debugging, Good Practice and Capstone

   Reading error messages, warnings and tracebacks.Testing small sections of code and checking intermediate results.Writing clear scripts with consistent names, comments and sections.Combining import, cleaning, summaries and visualisation in a complete analysis.