R for Statistics & Data Science

Move from R syntax to tidy data, visualization, statistical modeling and reproducible Data Science.

R becomes especially powerful when code, statistical reasoning and communication live in one workflow.

Import → Tidy → Transform → Visualize → Model → Communicate

TipKey ideas

tidy data dplyr tidyr ggplot2 readr lm glm tidymodels Quarto reproducibility

Core learning path

R for Data Science (2e):
https://r4ds.hadley.nz/

It teaches the practical cycle of importing, transforming, visualizing and communicating data.

ggplot2 book:
https://ggplot2-book.org/

Tidy Modeling with R:
https://www.tmwr.org/

tidymodels:
https://www.tidymodels.org/

Quarto:
https://quarto.org/docs/get-started/

One beautiful idea: formulas

model <- lm(
  price ~ size + bedrooms + age,
  data = homes
)

summary(model)

R’s formula interface connects programming directly to statistical model specification.

Practice projects

Do not practice R only through isolated syntax exercises. Pick a dataset and repeatedly perform:

question
 → clean
 → visualize
 → summarize
 → model
 → diagnose
 → communicate

The free R for Data Science book deliberately emphasizes this end-to-end workflow.

TipWhere R shines

R is particularly strong when statistical analysis, visualization and reproducible scientific communication are central to the work.