R is a widely used programming language that works well with data. It’s a great option for statistical analysis, and has an active development community that’s constantly releasing new packages, making R code even easier to use. It’s built around a central data science concept: The DataFrame, so if you’re interested in data science, analysis, and visualization, you’ll want to learn how to use R.

This course is Harvard University's introduction to programming using a language called R, a popular language for statistical computing and graphics in data science and other domains. By course’s end, learn to package, test, and share R code for others to use.

Learn R programming for effective data analysis with Johns Hopkins University experts. This intermediate course covers installing and configuring R, programming concepts, data import/export, debugging, profiling, and simulations. Through hands‑on assignments, you’ll gain practical skills in statistical computing, data manipulation, and performance tuning, earning a shareable certificate.

Master the essentials of R programming with Johns Hopkins University. This course introduces R’s environment, installation, and configuration, while covering data handling, debugging, profiling, and simulation. Through practical assignments, learners gain skills in statistical computing and efficient data analysis, preparing them for advanced applications in data science and research.

The goal of these videos is to provide students with tools and concepts for working with R, a free software environment for statistical computing and graphics. The students will learn the basics of R, how to navigate the R interface and deal with different data formats, how to run and interpret linear models with R, and how to use Geographic Information Systems (GIS) in R. These practical sessions were developed as part of the course 1.845 Terrestrial Carbon Cycle and Ecosystem Ecology but will be useful for anyone looking to learn about R and GIS.