E7527 Data Analysis in R

Faculty of Science
Autumn 2026
Extent and Intensity
2/1/0. 2 credit(s) (plus extra credits for completion). Recommended Type of Completion: k (colloquium). Other types of completion: zk (examination).
In-person direct teaching
Teacher(s)
Mgr. Soňa Smetanová, Ph.D. (lecturer)
Mgr. Jan Böhm (lecturer)
doc. Mgr. Eva Budinská, Ph.D. (lecturer)
Guaranteed by
doc. Mgr. Eva Budinská, Ph.D.
RECETOX – Faculty of Science
Contact Person: Mgr. Soňa Smetanová, Ph.D.
Supplier department: RECETOX – Faculty of Science
Timetable
Thu 12:00–14:50 F01B1/709
Course Enrolment Limitations
The course is also offered to the students of the fields other than those the course is directly associated with.
The capacity limit for the course is 30 student(s).
Current registration and enrolment status: enrolled: 30/30, only registered: 1/30, only registered with preference (fields directly associated with the programme): 0/30
fields of study / plans the course is directly associated with
Abstract
The aim of the course is to teach the researchers to use advanced R - statistical software for data analysis. We will in detail explain the syntax of the R language and introduce a number of functions for data pre-processing, statistical data analysis and graph plotting. This is a basic course that assumes no previous experience of working in R.
Learning outcomes
PAfter attending this course, the student:
Understands the syntax of language R
Knows data structures in R
Knows the difference between a script and a function
Can create functions
Creates scripts for R batch commands and uses them
Knows the syntax of basic cycles and conditions (for, repeat, if...)
Can install packages of R functions
Automatically creates objects with names defined by a variable
Makes automatic scripts
Optimizes computational burden of algorithms by using less time-consuming functions(e.g. apply instead of for cycle)
Knows the options of connecting R with other programming languages (C, Python, Perl)
Loads and saves data files
Transforms matrices and other data tables
Can merge tables of different types
Effectively recodes variables
Performs hypothesis testing
Applies different functions for data clustering
Knows all possibilities of graph saving
Knows and works with basic graphical interface in R
Creates graphs in lattice and grid
Can create and save graphs in automatic script
Creates complex colour graphs
Knows how to set up graph resolution and creates graphs of publication quality
Saves graphs in different formats
Can create analysis plan and find and select the best functions
Can create a simple-to-follow script and additional functions for complex data analysis of example data
Will optimize this script from the computational burden point of view
Key topics
  • 1st lecture - Introduction to R (history of R, what is R, advantages, and disadvantages of R; downloading and installing R; basic work with  R - setting the working directory, basic commands, operators, libraries; help; what is an object and its basic characteristics)
  • 2.-5. lecture – Objects in R (vectors and basic work with vectors; matrices and basic work with matrices; data frames; lists; and other objects)
  • 6.-7. lecture - Programming in R (for loop, if condition, while, repeat, commands from the apply family; functions; how to write a script effectively)
  • 8.-9. lecture – Loading and saving files, basic data editing
  • 10.-11. lecture - Graphs in  R (traditional graphics; Lattice (Trellis); Grid; ggplot2; saving graphs)
  • 12. lecture - Multidimensional analysis, analysis of a real example
  • 13. - 14. lecture - Introduction to the specialities (Quarto/Rshiny), projects discussion, using of AI in scripting
Study resources and literature
    recommended literature
  • MATLOFF, Norman S. The art of R programming : a tour of statistical software design. Eleventh printing. San Francisco: No Starch Press, 2011, xxiii, 373. ISBN 1593273843. info
  • TORGO, Luís. Data mining with R : learning with case studies. Boca Raton: Chapman and Hall/CRC, 2011, xv, 289. ISBN 9781439810187. info
  • GENTLEMAN, Robert. R programming for bioinformatics. Boca Raton: CRC Press, 2009, xii, 314. ISBN 9781420063677. info
  • MURRELL, Paul. R graphics. Boca Raton: Chapman & Hall/CRC, 2006, xix, 301. ISBN 158488486X. info
  • Bioinformatics and computational biology solutions using R and bioconductor. Edited by Robert Gentleman. New York: Springer, 2005, xix, 473. ISBN 0387251464. info
Approaches, practices, and methods used in teaching
The course is held weekly and consists of a two-hour lecture followed by a one-hour practical session. During the lecture, theoretical concepts are explained using a presentation and are immediately applied in the R user interface on computers in a dedicated computer lab after each key section. In the subsequent practical session, students reinforce their understanding by working on assigned exercises related to the material covered. The number of students is set so that each has access to their own computer. Students are encouraged to be proactive and to propose their own algorithmic solutions to the given problems.
Method of verifying learning outcomes and course completion requirements

1) Mandatory attendance in practical classes


2) Written tests


During the semester, students complete two mandatory offline written tests during practical classes. These tests assess students’ knowledge of the material covered up to that point. Students must obtain at least 50% in each test. One retake is allowed for each test. In case of a relevant excused absence, the student will take a make-up test by prior arrangement.


The first written test covers Lessons 1–5, and the second written test covers Lessons 6–11.


3) Project + Colloquium


During the semester, students prepare a projects demonstrating their ability to use R for basic data analysis and graphical outputs. They may use either their own data or publicly available data. The project will be assessed based on the functionality and clarity of the script in relation to the stated aims of the project.


The project is then defended during the colloquium, which consists of a discussion and the offline completion of tasks verifying that the student understands the submitted project and is able to correct or modify it without using online support tools.




Optional examination (if a student wants to obtain one additional credit)


To pass the examination, students must first meet the requirements for the colloquium and then obtain at least 17.5 points in a practical offline test in R. The test consists of a set of tasks; students submit both the solutions and the corresponding code.


The maximum score for the test is 30 points. Students are allowed to use study materials and R help, but they are not allowed to use the internet.


Grading:


<17.5 F, ≤20 E, ≤22.5 D, ≤25 C, ≤27.5 B, ≤30 A.

Language of instruction
Czech
Teacher's information
Eva Budinská, RECETOX, budinska@recetox.muni.cz, +420 775 07 30 30, 

Soňa Smetanová, RECETOX, smetanova@recetox.muni.cz


web: btr.iba.muni.cz


Additional sources of information
• http://www.r-project.org
• http://www.bioconductor.org
• http://www.pubmedcentral.nih.gov/picrender.fcgi?artid=2653488&blobtype=pdf
• http://www.stat.auckland.ac.nz/~paul/RGraphics/rgraphics.html

Further comments (probably available only in Czech)
Study Materials
The course is taught annually.
Information on course enrolment limitations: Doporučení absolvovat Bi8600, DSMBz01, Bi3060
Listed among pre-requisites of other courses
The course is also listed under the following terms Autumn 2022, Autumn 2023, Autumn 2024, Autumn 2025.
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