course is intended for doctoral students only course can be enrolled in outside the study plan you can enroll for the course in winter and in summer semester
The course aims to teach participants the basics of working with the statistical program R in the graphical
environment of RStudio. The goal is to acquire fundamental experience and practical skills for the rapid and
efficient processing of clinical data using the tidyverse package collection. The course will cover an introduction to
statistical data processing in R (descriptive statistics, advanced data visualization, basic statistical tests, and an
introduction to multivariate analysis). No prior experience with the R programming language is required from
participants. The instruction will be hands-on, directly in RStudio, which participants will install on their own
computers. Teaching scripts and data sets will be provided. Participants must have their own laptop.
The topics covered include, for example:
• Introduction to R
Overview of data science, software for data science, study literature, DataCamp, cheatsheets, introduction to R
and RStudio, installation of R and RStudio, first commands in R
• How to load and organize your data: Introduction to tidyverse
Why Tidyverse, data import into R (csv, xlsx) – reader, glimpse, introduction to data wrangling – pipeline, dplyr
(select, filter, mutate, arrange, rename, group_by, summarise)
• Images for articles and posters: Introduction to visualization
ggplot (scatter plot, bar plot, box plot, histogram, facets, geom_smooth)
• Publishing directly from RStudio: Introduction to markdown
Why markdown, reproducible research, pandoc, html, ms word, pdf
• Working with factors, strings, and dates/times
factor, forcats, stringr, lubridate
• Introduction to functions
• Relational data
Wide + long data formats, what relational data are, how data are stored in databases, left_join, right_join, full_join,
anti_join, wide
• Introduction to statistical data processing
Descriptive statistics (gtsummary), t-test (paired, unpaired), non-parametric tests, categorical data, chi-square
test, Fisher's test, linear regression, logistic regression, time-to-event analysis, multivariate linear, logistic, and
Cox regression…
Last update: Machová Marie, Bc., DiS. (15.08.2024)
Literature - Czech
Povinná:
Wickham, Hadley Grolemund, Garrett. R for data science : import, tidy, transform, visualize and model data. Beijing ; Boston ; Farnham ; Sebastopol ; Tokyo: O'Reilly Media, 2016, 492 s. ISBN 978-1-491-91039-9.
