Introduction to R—IND218

Reproducible data analysis for business decisions

Turn raw data into defensible decisions

Learn R by solving realistic analytics problems: importing untidy data, checking its quality, transforming it, and communicating results in a reproducible workflow.

Start with Module 1 View the course schedule

What you will learn

1. Work confidently in R

Use RStudio projects, scripts, objects, vectors, functions, and control flow to make analyses repeatable.

2. Prepare real data

Import files, diagnose data types, handle missing values, and use dplyr and tidyr to create analysis-ready tables.

3. Answer business questions

Translate questions into measurable indicators, validate joins and aggregations, and distinguish evidence from interpretation.

How to use this site

Each lesson combines a business question, an executable example, an explanation of what each transformation changes, and practice tasks. Run the code yourself: change one input, predict the result, and then inspect the output. That cycle is more valuable than copying a finished pipeline.

TipA reliable analysis habit

Before writing code, state the unit of observation (for example, one row per order) and the decision metric (for example, net revenue by region). After every join or pivot, check row counts, key uniqueness, and missing values. These checks prevent technically valid code from producing misleading conclusions.

Learning route

1. Begin with R setup and projects.
2. Build core programming skills in R fundamentals.
3. Learn tidyverse workflows in Module 3 and data types in Module 4.
4. Apply dplyr to business data in Module 5.
5. Reshape and validate tabular data in Module 6.

Need help? Read the support guide and include a minimal reproducible example when asking a coding question.