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R language and R computational environment are dedicated to statistical computations and their ongoing graphical visualization (The R Project for Statistical Computing, https://www.r-project.org/). Besides statisticians, the
R language is commonly used for purposes of data and hypotheses analyses by biologists and physicians at a vast majority of not only foreign universities. The R environment is open-source, free-of-charge (both free-as-in- beer and free-as-in-speech) and relatively user-friendly and becomes a dominant analytical tool in many fields, which makes it an equivalent alternative to clickable software. Furthermore, in biomedical fields such as analyses of molecular data scoping on genes (Bioconductor platform) or interpretations of cross-over designs of pharmacological trials, there are even no alternatives to R yet.
The subject is recommended for all undergraduate students considering doing science in their future career, i. e. the ones thinking about a Ph.D. study, as well as for all interested graduate students (Ph.D. candidates). The subject is designed as a brief introduction into the R language, therefore there are no demands on previous R or other programming knowledge. A student is going to be introduced with installation and running of the R environment, with import and export of data to/from the R environment and eventually with data manipulation.
Going through fingers-on examples based on (bio)medical data, a student is going to become familiar with R libraries, basic statistical analyses and data visualization by which she could realize any possibilities of R application in her future intended research. In addition, a student is going to be introduced into R programming and user-defining of her own R functions. During all the course and particularly in the final course project, a student will face a need to keep her data analyses and project results in a reproducible and transparent way.
R used in statistical analysis of a publication is claimed to be linked to statistically significantly higher chance the publication will be cited more often, according to some evidence.