Programming in R (4406)
  
Coordinating lecturer :Prof. dr. Ziv SHKEDY 
  
Co-lecturer :De heer Ewoud DE TROYER 
 Mevrouw Marijke VAN MOERBEKE 
 dr. Rudradev SENGUPTA 
  
Member of the teaching team :De heer Bernard OSANGIR 
 Mevrouw Rahma AZIZAH 
 Mevrouw Thi Huyen NGUYEN 

Language of instruction : English
Credits: 3,0
  
Period: semester 1 (3sp)
  
2nd Chance Exam1: Yes
  
Final grade2: Numerical
 
Exam contract: not possible
 
Sequentiality
 
   No sequentiality

Content

Basic concepts of R, R studio and R markdown. Visualizing data using the lattice and ggplot 2 R packages. Basic programming in R, the tidyverse package and data wrangling


Organisational and teaching methods
Organisational methods  
Lecture ✔  
Project ✔  
Self-study assignment ✔  


Evaluation

Semester 1 (3,00sp)

Evaluation method
Written exam66 %
Open-book ✔
Take-home assignment✔
Oral exam34 %
Open questions✔

 

Compulsory course material
 

R software will be used for this course.



Learning outcomes
Master of Statistics and Data Science
  •  EC 

    • The student can handle scientific quantitative research questions, independently, effectively, creatively, and correctly using state-of-the-art design and analysis methodology and software.

  •  EC 

    • The student can critically appraise methodology and challenge proposals for and reported results of data analysis.

  •  EC 

    • The student can put research and consulting aspects of one or more statistical fields into practice.

  •  EC 

    • The student can work in a multidisciplinary, intercultural, and international team.

  •  EC 

    • The student has the habit to assess data quality and integrity. 

  •  EC 

    • The student is able to efficiently acquire, store and process data.

  •  EC 

    • The student is an effective written and oral communicator, both within their own field as well as across disciplines.

 
Master of Transportation Sciences
  •  EC 

    • EC2: The holder of the degree has in-depth knowledge and understanding of the concepts, methods, and (research) techniques of transportation sciences. He/she is able to apply the concepts, methods and (research) techniques in the field of transportation sciences adequately and autonomously.

 
  EC = learning outcomes      DC = partial outcomes      BC = evaluation criteria  
Offered inTolerance3
1st year Master Bioinformatics J
1st year Master Bioinformatics - icp J
1st year Master Biostatistics J
1st year Master Biostatistics - icp J
1st year Master Data Science J
1st year Master Quantitative Epidemiology - icp J
1st year Quantitative Epidemiology J
2nd year Master of Transportation Sciences J
Exchange Programme Statistics J



1   Education, Examination and Legal Position Regulations art.12.2, section 2.
2   Education, Examination and Legal Position Regulations art.15.1, section 3.
3   Education, Examination and Legal Position Regulations art.16.9, section 2.