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Introduction in R


Admission requirements

Participants must be able to use a laptop/computer capable of running recent RStudio.
Goals R is an open-source, free environment/language for statistical computing and graphics. It provides a large repository of statistical analysis methods.


The course teaches how to do data analysis in the open source statistical program R. It covers calculations, reading data, manipulating data, making plots and reports, basic statistical methods, and basic programming skills. Focus is on R as a language, so the course is not tailored to any specific application.

Course objectives

The goal of the course is to teach students how the R language, extended by tidyverse package, can be used to build a report with a simple statistical analysis of data provided in a table. NOTE: This is not a statistics course! The course assumes no prior programming knowledge. Elementary statistics knowledge is necessary to understand examples.

After the course you will be able to:

  • Use R language expressions.

  • Use R to create simple data objects (vectors, lists) and change their content.

  • Apply R descriptive statistics functions to data in vectors.

  • Use R to read tables (tibbles), filter their rows, select/rename/add columns (with tidyverse/dplyr and tidyverse/tidyr).

  • Apply R descriptive statistics functions to summarize data from grouped table rows.

  • Use R to create scatter plots (with tidyverse/ggplot2).

  • Locate other methods for data manipulation or plotting.

  • Locate and install packages extending base R functionality (e.g. advanced statistical methods).

  • Explain R factors.

  • Use R Markdown to create a reproducible data analysis leading to a report (HTML, PDF, DOC).

  • Recognize and explain simple tidyverse-based R code written by others.


All course and group schedules are published on MyTimeTable.

The exam dates have been determined by the Education Board and are published in MyTimeTable.
It will be announced in MyTimeTable and/or Brightspace when and how the post-exam feedback will be organized.

Mode of instruction

Interactive lectures

Assessment method

Written exam (a computer based exam), you will prepare an R Markdown document reporting an analysis on a dataset.

Reading list

All material will be made available on a special website for the course.


To participate in interactive lectures and the exam students must register via MyStudyMap. Registration in MyStudyMap gives you automatic access to the course in Brightspace. For more information, please visit the Leiden University website for students.


Szymon M. Kiełbasa, (