Course type: elective
Lecturer: Lara Lusa, Ph.D., Associate Professor
 

For the timeline see Curriculum.

Prerequisites:

• Regular inscription.

Content (Syllabus outline):

Efficient and reproducible data managmenent in R.

Graphical representation using ggplot2.

Development and deployment of interactive reports and of web applications using R, Rmarkdown and the shiny package

Analysis and code documentation using  versioning control.

R code development and optimization

• Common errors  that make the code inefficient

Testing, debugging, profiling and performance measurement

Objectives and competences:
R is one of the most widely used statistical programming languages. Applied statisticians use it for data analysis and to implement their own functions, which can be grouped into packages and shared with the growing R community. The student improves his or her basic knowledge of R language; the focus is on data management, data visualization and prepration of reproducible reports . The student learns how to effectively manage and present data and results. The student learns to optimize and test his or her code. He or she will also learn how to share the code with others by developing packages and web applications. This knowledge is useful for the other subjects and for the applied work of the student.

Intended learning outcomes:

Improved knowledge and understanding of statistical development platform R.