R
A language built for statistics and data analysis.
R is taught here in 3 lessons, running from Vectors, factors and data frames through to Base graphics and ggplot2. Each lesson takes one topic, shows the working code, and links onward to the next, so the course can be read straight through in order.
| Track | Python & Data Science |
| Lessons | 3 |
| Level | Beginner |
| Reading time | about 39 minutes |
| Prerequisites | Helpful, but not required: Python 2.x |
Lessons
- Vectors, factors and data framesThe four things every R user does with a vector, and the data frame rules that decide whether your analysis is correct.
- Data manipulation with base RSelect, filter, arrange, mutate, group and join without leaving base R — and the exact dplyr verb each base expression replaces.
- Base graphics and ggplot2The painter's model behind base plots, the grammar behind ggplot2, and how to write a chart to a file without losing it.
More in Python & Data Science
Python 3 NumPy pandas Matplotlib Jupyter Notebook Flask FastAPI Django SciPy Pillow Python 2.x Julia
FAQ
How long does the R course take?
It has 3 lessons, about 39 minutes of reading. Expect roughly twice that if you type out and run every example.
Do I need prior experience for R?
Not strictly. It helps to have read Python 2.x first, because some lessons build on it, but every lesson explains its own assumptions.
What should I read after R?
Continue with Julia (3 lessons), the next course in Python & Data Science.
Last refreshed 2026-09-18.