R Essentials: Statistics as a Programming Language

A short learning map for R fundamentals, vectors, data frames, functions, visualization and reproducible analysis.

R was built around statistical computing and graphics, which makes it unusually expressive for exploratory analysis and statistical modeling.

TipKey ideas

vector data.frame factor NA function formula subset apply package plot ggplot2

Start with the vector

x <- c(8, 11, 7, 14, 10)

mean(x)
sd(x)
x[x > 10]

Much of R becomes easier once you think in vectors rather than row-by-row loops.

Learning resources

★ Official Introduction to R

Foundational reference for the language and statistical environment.

https://cran.r-project.org/doc/manuals/r-release/R-intro.html

★ R for Data Science

A modern, free book centered on real data-science workflows.

https://r4ds.hadley.nz/

★ Posit Cheatsheets

Excellent compact references after you have learned a concept once.

https://posit.co/resources/cheatsheets/

Learn in this order

vectors + types
 ↓
data frames
 ↓
indexing
 ↓
functions
 ↓
missing values
 ↓
visualization
 ↓
packages
 ↓
statistical models

NoteBase R and tidyverse are not enemies

Learn enough base R to read ordinary R code and understand vectors/functions. Learn tidyverse tools because they provide a coherent workflow for transformation, visualization and modern data analysis.

Next: R for Statistics & Data Science