R Programming

Garant předmětu:

Lecturer:

Tutor:

Supervisor:

Department of Mathematics

Synopsis:

The aim of the course is to introduce R programming as a tool for research and data science projects including getting, cleaning, and exploring data. Students will learn how to write code in R and how to use R for effective data analysis. They will master the basics of the R language, work with factors, lists, data frames, tibbles. and applying R-studio packages. Data manipulation using tidyr and dplyr, visualisation with ggplot2, and present project with markdown and Shiny.

Requirements:

Syllabus of lectures:

1.Introduction to R-studio and R language, reading data into R, accessing R packages, writing R functions, debugging.

2.Set up Gitlab with R-studio, use git to manage research projects and other useful tools.

3.Application of basic statistics and probability in R.

4.Use R to clean, analyze, and visualize data.

5.Application of basic statistical models in R.

6.Learn how to build own package in R.

7.Present results of research project with R markdown and build interactive web applications in Shiny.

Syllabus of tutorials:

Study Objective:

Study materials:

Key references:

[1] Hadley Wickham,Advanced R, Chapman & Hall, 2nd Edition, 2019

[2] Colin Gillespie, Robin Lovelace, Efficient R Programming, O'Reilly Media; 2017

Recommended references:

[3] Hadley Wickham, Garrett Grolemund , R for Data Science: Import, Tidy, Transform, Visualize, and Model Data O'Reilly Media; 2016

Media and tools: cran.r-project.org, rstudio.com, postreSQL, gitlab.fjfi.cvut.cz, r-pkgs.org, r4ds.had.co.nz, csgillespie.github.io/efficientR

Note:

Further information:

No time-table has been prepared for this course

The course is a part of the following study plans:

• Aplikované matematicko-stochastické metody (elective course)