Subject description
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
The subject is taught in programs
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.
Teaching and learning methods
Lectures, Lab work, home work, seminar
Part of the pedagogical process will be carried out with the help of ICT technologies and the opportunities they offer.
Expected study results
Improved knowledge and understanding of statistical development platform R.
Basic sources and literature
- R Development Core Team (2009). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. ISBN 3-900051-07-0, URL http://www.R-project.org.
- Wickham, H. (2009). ggplot2. Spinger.
- Wickham, H. (2014). Advanced R. Chapman & Hall/CRC The R Series.
- Burns, P. (2012). The R Inferno. Engels.
- Razpoložljiva literatura se letno spreminja in posodablja. Primerni viri so zbrani na spletni strani www.r-project.org, zato se bodo aktualni viri letno spreminjali.
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