The R Statistical Language

From the base-R console to tidyverse & ggplot2

Author
Affiliation

A. Stamm

Department of Mathematics Jean Leray, UMR CNRS 6629, Nantes University

This introductory / comprehensive course on R is taught from the base-R console in Positron, progressively moving into the tidyverse and ggplot2. It runs over three 3-hour sessions, each pairing slide decks with hands-on labs and live-coding.

Synopsis

Professor Aymeric STAMM (CNRS & ENSAI)
ECTS Credits 0 (preliminary course)
Lectures and Tutorials 9 hrs (ENSAI)
Estimated personal workload (beyond lecture and tutorial time) ~1–2 hrs per session: working through the un-solved lab scripts and the cheeky “Your turn” tasks scattered through the decks.
Teaching language English
Software & Packages R, Positron (edition/render), base R + the tidyverse (dplyr, tidyr, stringr) and ggplot2.
Teaching materials Per-session slide decks and matching lab-*.R scripts, downloadable below; the shared penguins.csv / penguins_messy.txt data ships alongside each lab.

Prerequisites

Nothing beyond R itself and the Positron IDE. Beginners and experts alike can start at Session 1; advanced students can skim the foundations and pick up at the tidyverse in Session 2.

Subjects Covered

Slide deck Description Lab material
1 · Foundations: vectors to tables What is R and why use it; setting up your environment; the console, scripts and R as a calculator; vectors and subsetting; lists; data frames / tibbles; reading and writing data. lab 1 · lab 2 · data: penguins_messy.txt
2 · Explore your data: the tidyverse The |> pipe; dplyr transforms (filter, select, mutate, arrange, group_by, summarise); tidyr (pivot longer/wider); stringr; base-R plotting; ggplot2 (layers, facets, hists/scatters/boxplots, fct_reorder). lab 1 · lab 2 · data: penguins.csv
3 · Repeat & package: loops, matrices, functions for/while/if/else (preallocation, vectorization); the *apply() family; base-R grouped summaries; matrices (arithmetic, %*%, solve(), apply(), sweep()); custom functions. lab 1 · lab 2 · lab 3 · data: penguins.csv

Learning Outcomes

By the end of this course you will be able to:

  • Work comfortably from the base-R console in Positron: use R as a calculator, know the core data types and special values, and read/write data.
  • Build and subset vectors, lists and data frames / tibbles, and choose the right structure for a task.
  • Transform data with the tidyverse, the |> pipe, filter()/select()/mutate()/arrange()/group_by()/summarise(), and tidying with pivot_longer()/pivot_wider().
  • Repeat work with loops, the *apply() family, and your own functions, using base-R grouped summaries (tapply(), aggregate()).
  • Explore data with base-R plotting and produce polished figures with ggplot2 (layers, facets, histograms, scatter plots, boxplots).

Bibliography