R

R is built for statistical computing and data analysis. It is especially useful when the work already lives in data frames, formulas, statistical models, or publication-quality plots.

Start here

1. R basics covers vectors, data frames, control flow, and functions.
2. Data import covers delimited text, spreadsheets, JSON, RDS, and RData.
3. Use the built-in help before searching the web:

?mean
help("lm")
vignette(package = "dplyr")

?mean and help("lm") work with a standard R installation. The vignette example requires dplyr to be installed; vignette() lists vignettes from installed packages. For a specific call, read its arguments and return value, then run the smallest example before applying it to your data.

A small working stack

Need	Start with
language and standard library	base R
tabular transformation	dplyr or data.table
plotting	ggplot2
delimited files	readr or data.table::fread()
spreadsheets	readxl
reproducible reports	Quarto
package development	devtools, usethis, and testthat

This is a starting set, not a claim that one package is the permanent default. Existing projects, data size, team conventions, and deployment constraints should decide the final stack.

Primary references

• The R Project for the language and manuals.
• CRAN manuals for authoritative language documentation.
• Posit documentation for RStudio, Quarto, and related tools.
• R Packages for package development.
• R for Data Science for a maintained data-analysis workflow.

Community posts are useful for examples, but package documentation and reproducible code should settle questions about current behavior.

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