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: 19-09-2023
Duration: Intensive course
Course ID
Course Title
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
Offered in
Level
Offered in
Duration
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.
Content
Day 1: Fundamentals - Create an object, data type, vector, basic arithmetic, open and save a dataset and basic plot
Day 2: Data structure - How to work with data frame, matrix, array and list
Day 3: Function, condition and loop
Day 4:Packages and plots in base R
Day 5: Summary statistics - Frequency, and measures of location (e.g. mean), spread (e.g. variance) and dependence (e.g. covariance)
Day 6: Linear regression - Theory, application and interpretation
Day 7: Tidyverse and ggplot2 - Data transformation, summarising, grouping, reshaping and visualising data
Day 8: Maps
Day 9: Workshop
Day 10: Exam
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
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
Literature
Examples:
- Venables, W.N., Smith, D.M. and the R Core Team (2021). An Introduction to R. url: https://cran.r-project.org/doc/manuals/r-release/R-intro.pdf
- Davies, T.M. (2016). The Book of R. No Starch Press (San Francisco): 835p.
The course may use additional literature if necessary.
Teaching Method
Workload
Scheduled classes:
3 hours of lectures per day for 2 consecutive weeks in August.
Each three-hour session will include both lectures and exercises.
Workload:
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
Name
Exam
Timing
Exam: August
Reexam: September
Tests
Exam
Name
Exam
Form of examination
Written examination on premises
Censorship
Second examiner: None
Grading
7-point grading scale
Identification
Student Identification Card - Exam number
Language
English
Duration
2 hours
Examination aids
It is allowed to use class notes in physical and digital form (as PDF, Word or R scripts)
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
5
Additional information
The assignment is handed in as both an R script and the exam answers as a PDF/Word document.
Re-examination
Form of examination
Oral examination
Identification
Student Identification Card - Date of birth
Preparation
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.
Duration
30 minutes
Examination aids
It is allowed to use class notes in physical and digital form (as PDF, Word or R scripts)
Assignment handover
Digital exam or Itslearning.
Assignment handin
No handin.
Additional information
The reexam will be conducted online.
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).
EKA
B540046102
External comment
Used examination attempts in the former identical course will be transferred.
Courses that are identical with former courses that are passed according to applied rules cannot be retaken.