Introduction to R

Study Board of Market and Management Anthropology, Economics, Mathematics-Economics, Environmental and Resource Management

Teaching language: English
EKA: B540046102
Censorship: Second examiner: None
Grading: 7-point grading scale
Offered in: Odense
Offered in: Summer school (spring)
Level: Bachelor

Course ID: B540046101
ECTS value: 5

Date of Approval: 07-12-2021

Duration: 1 semester

Course ID


Course Title

Introduction to R

Teaching language


ECTS value


Responsible study board

Study Board of Market and Management Anthropology, Economics, Mathematics-Economics, Environmental and Resource Management

Date of Approval


Course Responsible

Name Email Department
Marie-Pier Bergeron Boucher CPOP (00)

Offered in




Offered in

Summer school (spring)


1 semester

Recommended prerequisites

The course is open to both bachelor and master students motivated to learn R. The course is designed so that students from different backgrounds and levels with no prior knowledge of R or statistics can learn the program and how to manage and analyze data.

Aim and purpose

The purpose of this course is to introduce the students to the programming language R. R is one of the most popular programming languages in data science, statistics, and other quantitative sciences. In the course, students will get to analyse data from a broad range of disciplines: health, population, economy, etc. R is a free, powerful, versatile, and easy to use tool for data analytics and visualisation. The students will learn how to master R, from installation to basic statistics. By the end of the course, the students will be able to use general programming features, analyse and visualise data. 


  • Installation of R and RStudio
  • Variables
  • Data types
  • Basic arithmetic
  • Data structures
  • Working with different data structures:
    • Vectors
    • Matrices
    • Data frames
    • Others
  • Basic R vs tidyverse
  • Functions, Conditions and Loops
  • R packages
  • Getting information from data:

- Summary statistics: Mean, Median, Variance, etc.

- T-tests and normal distribution

- Linear regressions

  • Visualising data

- Plots and graphs

- Map

Description of outcome - Knowledge

  • Describe and explain the different data types and structures in R
  • Understand basic arithmetic and statistical functions in R
  • How to create one’s own function
  • How to create graphics in R

Description of outcome - Skills

  • Open datasets in R
  • Manipulate and analyse data in R
  • Create plots in R

Description of outcome - Competences

  • Choose the proper function, tool or features to analyse a given dataset
  • Choose the proper graph types to visualise results and data


  • Venables, W.N., Smith, D.M. and the R Core Team (2021). An Introduction to R. url: 
  • Davies, T.M. (2016). The Book of R. No Starch Press (San Francisco): 835p. url:

The course may use additional literature if necessary.

Teaching Method

The course will be a combination of lectures and exercises. 


Scheduled classes:

3 hours of lectures per day for 2 consecutive weeks in August.
Each three-hour session will be divided about equally between lectures and exercises.

A 5 ECTS course entails a total workload of 135 hours. These are divided between the different learning activities and below follows an estimation for the average student:
Face-to-face lectures: 30h
Preparation for lectures: 65
Preparation for exam: 38
Exam: 2h
Total: 135 hours

Examination regulations





Exam: August
Reexam: September





Form of examination

Written examination on premises


Second examiner: None


7-point grading scale


Student Identification Card - Exam number




2 hours

Examination aids

It is allowed to use lecture notes and textbooks. Access to the internet is not allowed. 

Assignment handover

The assignment is handed over in Digital Exam.

Assignment handin

Electronic hand-in via Digital Exam.

ECTS value


Additional information



Form of examination

Oral examination


Student Identification Card - Date of birth


The dataset is handed out one day before the exam. The questions will not be handed out before the oral exam, and there is no preperation time.


30 minutes

Assignment handover

Digital exam or Itslearning. 

Assignment handin

No handin.

Additional information

For the Reexam, students will have a new dataset and will have one task to perform in R. They will have to discuss how to perform the task with the teacher (10 minutes) and do it in R (20 minutes).



External comment

This is a new course for the Summerschool.

Courses offered

Offer period Offer type Profile Education Semester
Spring 2022 Exchange students

URL for Skemaplan