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Learn R Programming

Analyze and visualize data with the language built by statisticians.

Free to start. Built from a single prompt. No credit card.

R is a language and environment designed for statistics and data analysis. It is widely used in academic research, biostatistics, social science and data teams for cleaning data, running statistical tests, fitting models and making publication-quality charts. Its core unit is the vector, so operations apply to whole columns at once, and data frames hold tables. The tidyverse packages, such as dplyr for data manipulation and ggplot2 for charts, give modern R a consistent style. Learnr builds a personalized R course from one prompt and teaches it through short, interactive lessons using realistic datasets.

How to learn R Programming, step by step

Install R and RStudio, then use projects. Install R from CRAN and the RStudio IDE from Posit. Create an RStudio project for each analysis so file paths stay relative and your work is easy to reopen and share.
Understand vectors and vectorized operations. Learn that c(1, 2, 3) * 2 doubles every element without a loop, that indexing starts at 1, and how NA represents missing values. Practice subsetting with [ ], $ and logical conditions.
Learn the core dplyr verbs. Install the tidyverse and practice filter, select, mutate, arrange, group_by and summarise chained with the pipe operator. These verbs cover most everyday data wrangling questions.
Build charts with ggplot2. Learn the grammar of graphics: map variables to aesthetics with aes, then add geoms like geom_point, geom_histogram and geom_boxplot, plus facets and labels. Recreate a chart from a news article as practice.
Run real statistical analyses. Use summary, t.test, cor and lm on built-in datasets like mtcars or penguins from the palmerpenguins package. Interpret coefficients and p-values in words, not just numbers.
Write a reproducible report. Combine code, output and prose in an R Markdown or Quarto document and render it to HTML. Daily Learnr lessons help the function names and syntax stick between analyses.

What you'll learn

Work with vectors, lists, factors and data frames in R
Import, clean and reshape data with dplyr and tidyr
Build clear, layered charts with ggplot2
Run descriptive statistics, t-tests and linear regression
Write reproducible reports with R Markdown or Quarto

What's inside your R Programming course

A sample path Learnr builds for R Programming. Your real course adapts to your goal and level.

1. R, RStudio and how the console works
2. Vectors, data types and data frames
3. Importing and cleaning data with the tidyverse
4. Visualizing data with ggplot2
5. Statistics and modeling in R
6. Functions, reproducible reports and a full analysis

How long does it take to learn R Programming?

Basic data wrangling and plotting take about 4 to 8 weeks at 4 to 6 hours a week. Confident analysis with modeling and reporting usually takes 3 to 6 months. A statistics background speeds up the modeling side a great deal.

What you need before you start

No programming is required. Basic statistics such as means, distributions and correlation helps you make sense of the output. Spreadsheet experience makes data frames feel familiar.

Common mistakes to avoid

Using setwd() with absolute paths that break on any other computer. Use RStudio projects and relative paths, or the here package.
Writing for loops over rows when a vectorized function or dplyr verb would do it more clearly and faster.
Running statistical tests without first plotting the data. Visualize distributions and relationships to check assumptions before trusting any p-value.

How Learnr builds your course

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R Programming questions, answered

Should I learn R or Python for data analysis?

Both work well. R is especially strong for statistics, academic research and visualization with ggplot2. Python is more general-purpose and common in machine learning and software engineering teams.

What is the tidyverse?

A collection of R packages, including dplyr, tidyr, ggplot2 and readr, that share a consistent design for importing, cleaning, transforming and visualizing data.

Is R hard to learn without a programming background?

R has some quirks, but many people learn it as their first language through statistics courses. Starting with the tidyverse gives a consistent, readable style that is beginner friendly.