Module: Data analysis and visualization using R (2)

* depending on the student population

Learning outcomes (leerdoelen)

After taking this course, you should be able to

• apply standard statistical tests and interpret them
• combine flow control elements into single-purpose functions
• combine functions into a readable script
• Use the packages from the tidyverse appropriately when relevant
• work with date and time data
• design and implement a multistep data analysis, combining multiple data processing steps for a single goal
• perform an Exploratory Data Analysis (EDA) on a given dataset, and report this readable and reproducible using RMarkdown

Contents

This course is a continuation of the course Data analysis and visualization using R (1). The current course will build on more advanced techniques and packages and also focus on combinatorial skills.

Assessment This course will be assessed using a 2-hour practical computer test. The gitbook will be available as a resource.

Literature and other resources

Literature

• Recommended as background material: "R for Data Science" (Wickham & Grolemund, 2016)

Web

• Blackboard course of the minor
• https://michielnoback.github.io/bincourses/davur2.html

Competences

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Method

• Hoor/werkcolleges

Entry demands

• You need to be enrolled in a Bachelor programme in the Domain of Applied Science.

Entry demands for test

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Foreknowledge

• You need to have followed the course Data analysis and visualization using R (1).

Foreknowledge can be obtained through

• Statistics course(s)

Resources for self study

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Mandatory materials

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Recommended materials

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