R
Built by statisticians, for statisticians — and anyone doing serious data analysis
What it is
About R
R was designed from the ground up for statistical analysis, and its package ecosystem (especially the tidyverse and ggplot2) makes it a favourite in academia, biostatistics, and any field where rigorous statistical modelling and hypothesis testing are core to the job. It's often compared directly with Python, but tends to have an edge in specialised statistical methods and publication-quality plotting.
What you can do with it
Capabilities
Run advanced statistical tests and models
Build publication-quality data visualisations with ggplot2
Design and analyse A/B tests and experiments
Clean and reshape data with the tidyverse packages
Produce reproducible statistical reports with R Markdown/Quarto
How to learn it
Learning Resources
Posit's (RStudio's) free official learning resources
'R for Data Science' (free online book by Hadley Wickham)
DataCamp's R programming tracks
Practise by analysing a public dataset end-to-end, from cleaning to a final report
Pro Tip
Learn the tidyverse (dplyr, ggplot2, tidyr) early rather than base R syntax first — it's what almost all current R teaching and real-world code actually uses.
Skills that use this tool
Roles that use this tool