DTS 2112 units200 LevelFirst Semester

Introduction to R Programming

B.Sc. Data Science, University of Uyo

R from the ground up: the language, its data structures, and the tidyverse workflow for importing, tidying, transforming, visualising and modelling data. R is the statistician's language, and it is the fastest route from a dataset to a publication-quality figure.

On Areté, DTS 211 comes with

  • 2 recommended textbooks
  • an AI tutor that has read this course’s outline and notes
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Course outline

  1. 01The R environment and RStudio; scripts, projects and the console
  2. 02Atomic vectors, coercion and vectorised operations
  3. 03Data structures: vectors, matrices, lists, factors and data frames
  4. 04Control flow, functions and the apply family
  5. 05Reading and writing data: CSV, Excel, delimited files and databases
  6. 06The tidyverse: dplyr verbs (filter, select, mutate, arrange, summarise, group_by)
  7. 07Tidy data principles and reshaping with tidyr
  8. 08Visualisation with ggplot2: the grammar of graphics, aesthetics, geoms, facets and themes
  9. 09Reproducible reporting with R Markdown / Quarto

Recommended textbooks

  • R for Data Science — Hadley Wickham, Mine Çetinkaya-Rundel & Garrett Grolemund

    2nd ed. — free at r4ds.hadley.nz; the definitive text for this course

  • The Art of R Programming — Norman Matloff

    Stronger on the language itself and on programming style

How to pass DTS 211

  • Install R and RStudio in week one and never read R code without running it
  • Learn the six dplyr verbs cold — filter, select, mutate, arrange, group_by, summarise. Together they solve most real data-wrangling tasks
  • ggplot2 feels strange until the grammar of graphics clicks: data → aesthetic mapping → geometry. Once it clicks it is faster than anything else
  • Do everything in an R Markdown / Quarto document from the start so your work is reproducible — you will need exactly this for DTS 417

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