2026-03-05-R-basic

February 26, 2026 · View on GitHub

ELIXIR-EE training course on R and RStudio basic usage. Targeted to learners who have no or very little prior experience with R and RStudio.

Repository: https://github.com/ELIXIREstonia/2026-03-05-R-basic

The main instructional materials are the R Markdown documents in this repository:

  • R-basic-warmup.Rmd — warm-up material for complete beginners
  • R-basic.Rmd — learner-facing workshop material
  • R-basic-instructor.Rmd — instructor notes and solutions

R Markdown allows code (examples) and explanation/documentation to be in the same document in a nicely formatted, well structured manner. You can render the learner document locally with RStudio or from R with:

# render learner material
rmarkdown::render("R-basic.Rmd")

Data files used in the examples are located in the data/ folder. Current files included:

  • data/Islander_data.csv
  • data/Islander_data.tsv
  • data/Islander_data.xlsx

Learning outcomes for the training:

  1. Understanding Basic R Data Structures:

    • Learners will be able to distinguish between primary R data structures, namely vectors, matrices, data frames, and lists.

    • They will be proficient in initializing and inspecting these structures using basic R functions.

  2. Data Transformation with Tidyverse:

    • Participants will gain proficiency in using the %>% pipe operator to chain together functions for data manipulation.

    • They will understand and be able to apply essential tidyverse functions like select, rename, filter, mutate, group_by, summarize, and arrange to transform and analyze datasets.

    • They will also learn common data manipulations like pivot_wider and pivot_longer

  3. Data Categorization and Conditional Operations:

    • Trainees will be equipped to generate new variables in a dataset based on conditional logic, such as categorizing numerical data into distinct groups.

  4. Application of Data Manipulation Techniques:

    • By the end of the session, participants will have hands-on experience in applying the introduced concepts to real-world datasets, such as the "Memory Test on Drugged Islanders" dataset. They'll be able to group data, compute summary statistics, and create new columns based on specific criteria.

Course materials (at a glance)

  • Authors / date: Priit Adler & Nurlan Kerimov (2026-03-05).
  • How to read data: readr::read_csv(), readr::read_tsv(), and readxl::read_excel() (examples shown in R-basic.Rmd).
  • Tidyverse overview and common verbs taught: %>% (pipe), filter(), mutate(), select(), rename(), arrange(), group_by(), summarize(), left_join() (joins) and related helpers.
  • Conditional operations and new columns: case_when() examples for creating categories (age groups, etc.).
  • Reshaping data: tidyr::pivot_wider() and tidyr::pivot_longer() with examples (fish_encounters, relig_income).
  • Practical dataset used in exercises: the "Memory Test on Drugged Islanders" dataset (Kaggle). Exercises include grouping, summarizing, dosage and age-group analyses, and comparing groups (happy/sad priming).

Next step

Take a look at our follow-up course on how to create compelling visualisations using R and ggplot2: https://github.com/ELIXIREstonia/2024-10-09-R-visualisation

License: see LICENSE.

Feedback form is here: https://forms.gle/omANJ76dXUJt6XSy9