Courses Bachelor Display 2022-2023
|Course Description||To PDF|
|Course title||R Functions and Libraries|
|Assessment||Pass / Fail|
For more information: email@example.com
|Language of instruction||English|
* Students understand the R programming language and the "tidyverse" library and functions as well as RMarkdown.
* Students apply the "tidyverse" functions that read, reshape, visualize and aggregate data and deliver a presentation in Rmarkdown.
* Students motivate their use of the "tidyverse" functions for specific data situations.
* Students evaluate and compare different uses of the "tidyverse" functions that lead to the same result and reflect upon best practices.
* Students understand the importance and impact of the R programming language to different business industries and institutions.
* Students understand the ethical principles of objectivity, carefulness and respect for data privacy regulations.
* Students make publication quality tables and graphs summarizing their results with the "tidyverse" in R and deliver a presentation created within R Markdown.
* Students know how to search for tool extensions or additional functions in R and use the help functions.
* Students collaborate and brainstorm in intercultural teams.
PLEASE NOTE THAT THE INFORMATION ABOUT THE TEACHING AND ASSESSMENT METHOD(S) USED IN THIS COURSE IS WITH RESERVATION. A RE-EMERGENCE OF THE CORONAVIRUS AND NEW COUNTERMEASURES BY THE DUTCH GOVERNMENT MIGHT FORCE COORDINATORS TO CHANGE THE TEACHING AND ASSESSMENT METHODS USED. THE MOST UP-TO-DATE INFORMATION ABOUT THE TEACHING/ASSESSMENT METHOD(S) WILL BE AVAILABLE IN THE COURSE SYLLABUS.
This skills training is a follow-up of the Statistics and Knowledge Discovery and Data Visualization courses and provides skills for efficient and more advanced statistical analysis of extensive business datasets. The training introduces students to the "tidyverse" package of the statistical software environment R. The tidyverse package is a collection of packages of functions, data and documentation, designed to tackle data science problems. All packages work in harmony because they share an underlying design philosophy, grammar and data structures. The training covers the core packages that provide functionality to model, transform and visualize data as well as programming tools to automate common tasks and solve new problems with greater ease.
Formative assessment: Feedback by tutors and peers during tutorial meetings
Summative assessment: Final project and short intermediate assignments
Instructional approach: Lecture and tutorials
|Teaching methods (indicative; course manual is definitive)||Assignment / Groupwork / Skills / Coaching|
|Assessment methods (indicative; course manual is definitive)||Final Paper / Assignment / Presentation|
|Evaluation in previous academic year||For the complete evaluation of this course please click "here"|
|This course belongs to the following programmes / specialisations||