880256 :Research Skills: Programming with R


Voertaal Engels
Werkvorm: Guided practical sessions (Collegerooster)
Tentamenvorm: 3 Papers (Geen informatie over tentamendata bekend)
Studielast:3 ECTS credits
Inschrijving:Enrollment via Blackboard before start of lectures
Blackboard informatieLink to Blackboard (Als u de melding 'Guest are not allowed in this course' krijgt, dient u nog bij Blackboard in te loggen)


No photo available
dr. E.E. van der Vaart (coördinator)

Doel van de cursus (alleen in het Engels beschikbaar)

At the end of the course, students are able to:

  • Read, transform and merge data using R

  • Visualize data using R

  • Carry out analyses using R

  • Find, install and use R packages

Inhoud van de cursus (alleen in het Engels beschikbaar)

The programming language R is a powerful tool for working with data – for manipulating it, plotting it, and modeling it. It’s used by academics and industry alike, and it’s free and open source. Thanks to a rich universe of add-ons, known as packages, it makes many advanced techniques easily accessible. This course offers an introduction to R. It teaches a specific set of skills - mostly related to data manipulation and visualisation - but more importantly, it teaches R self-help: How to use fundamental R knowledge, built-in functions, the ? command, and Google, to get R to do what you want.

The course is taught using RStudio, which is a free programme that makes it easier to work with R, and RMarkdown, which is a special format for intermingling text and code. Each week, a worksheet containing instruction and exercises is published on Blackboard. Each worksheet introduces a specific R topic using illustrative examples and data sets. The following week, answers to all exercises are posted, and difficult questions are discussed in class. Working through these worksheets provides all the necessary insights and skills to pass the course.

There are 6 worksheets in all, covering the following topics:

W1: Introduction to R
W2: Transforming Data
W3: Visualising Data
W4: Programming
W5: Cleaning & Tidying Data
W6: Modeling Data

Specifically, the topics covered include: R data structures, such as vectors, lists and data frames; the overall setup of an R project using RStudio, relative paths, and the workspace; customizing the behavior of R functions using arguments; subsetting data, re-naming variables and creating new ones; summarizing and merging data using dplyr; creating and customizing plots using ggplot2; finding and fixing input errors; re-shaping data using tidyr; writing new functions and calling them repeatedly using apply(); installing and loading R packages; fitting models and evaluating their performance.

Bijzonderheden (alleen in het Engels beschikbaar)

The course is assessed through two individual take-home programming assignments and a final group project. These make up 30%, 30% and 40% of your final grade, respectively. To pass the course, the average of the two take-home programming assignments must also be pass. The worksheets themselves are not graded, but they are inspected at random, and uploading them on Blackboard is compulsory; two may be missed at maximum.

Verplichte literatuur

  1. All necessary materials will be provided in class

Aanbevolen literatuur

  1. Lander, J.P. (2014) R for Everyone. Advanced Analytics and Graphics. Pearson.

Vereiste voorkennis


Verplicht voor

  • Data Science: Business and Governance ( 2015, 2016 )

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