R
A language and environment purpose-built for statistical computing.
SpecializedintermediateGuide only -- no course yet
Overview
R was designed specifically for statistics and data visualization, with a mature ecosystem (especially the tidyverse packages) that remains dominant in academia, biostatistics, and applied statistics -- an alternative to Python's data-science stack rather than a general-purpose language.
- What it is
- A language and environment purpose-built for statistical computing and graphics.
- Why it's used
- For its deep statistical package ecosystem and long-standing dominance in academic/research statistics.
- Where it fits
- An alternative to Python for data analysis, especially in academic, biostatistics, and research contexts.
Core concepts
- Vectors and data frames
- The tidyverse (dplyr, ggplot2)
- Statistical modeling functions
- The pipe operator
Example
R's vectors and built-in statistical functions (mean, sd, t.test) are first-class, reflecting its origin as a statistics-first language rather than a general-purpose one adapted for stats.
prices <- c(10, 20, 30)
mean(prices) # 20Common use cases
- Academic and biostatistics research
- Statistical modeling and reporting
Project ideas
- Compute summary statistics and a basic plot for a small dataset