R
Welcome to R, a powerful and beginner-friendly programming language for data analysis and statistics. This guide will help you get started quickly and confidently.
What is R?
R is a high-level programming language and environment designed for statistical computing, data analysis, and visualization. It is widely used in:
- Data analysis and statistical modeling
- Data visualization
- Scientific research
- Geospatial analysis (GIS)
- Reporting and reproducible research
R is especially popular in academia, research, and data-focused fields due to its extensive collection of packages and strong support for statistical methods.
Why Learn R?
- Designed for Data Analysis – Built specifically for working with data and statistics.
- Powerful Visualization Tools – Create high-quality graphs and charts.
- Extensive Package Ecosystem – Thousands of packages for specialized tasks.
- Strong Research and Academic Use – Widely used in science, economics, and social sciences.
- Reproducible Workflows – Tools like R Markdown make it easy to document and share analyses.
Resources for Learning R
In addition to this guide, there are many free resources available for learning R.
Below is a curated list of popular resources, organized by experience level and learning style.
Video Courses / Structured Learning Paths
| Resource | Author / Provider | Best for (goal) | Level |
|---|---|---|---|
| Sage Campus: Introduction to R | Sage Campus | Absolute beginners, guided introduction | New to programming |
| Posit Cloud: R Recipes | Posit | Hands-on practice, applied workflows | Beginner–Intermediate |
| The Carpentries: Data Analysis and Visualization in R for Ecologists | Data Carpentry | Data analysis, scientific workflows | Intermediate |
| Sage Campus: Practical Data Management in R | Sage Campus | Data wrangling and management | Intermediate |
Books / eBooks
| Resource | Author / Provider | Best for (goal) | Level |
|---|---|---|---|
| Hands-On Programming with R | Garrett Grolemund | Learning programming fundamentals in R | Beginner |
| Introduction to R | Multiple authors | Foundational concepts and basics | Beginner |
| R for Data Science | Hadley Wickham & Garrett Grolemund | Data science, tidyverse workflows | Beginner–Intermediate |
Documentation / Reference / Practice
| Resource | Author / Provider | Best for (goal) | Level |
|---|---|---|---|
| R Statistics | Selva Prabhakaran | Practical examples and quick reference | Beginner–Intermediate |
| R Packages | Community-driven | Exploring available R packages | Intermediate |
Applied / Domain-Specific Resources
| Resource | Author / Provider | Best for (goal) | Level |
|---|---|---|---|
| R for Spatial Analysis (GitHub) | dcarver1 | Geospatial analysis (intro to intermediate) | Beginner–Intermediate |
| R as GIS for Economists | T. Mieno | GIS and spatial economics | Intermediate–Advanced |
| CUAHSI Hydroinformatics Portal | CUAHSI | Water data science, hydroinformatics | Advanced |