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Data Analysis in R - JEB157

Title:	Data Analysis in R
Czech title:	Data Analysis in R
Guaranteed by:	Institute of Economic Studies (23-IES)
Faculty:	Faculty of Social Sciences
Actual:	from 2025
Semester:	winter
E-Credits:	6
Examination process:	winter s.:combined
Hours per week, examination:	winter s.:2/2, Ex [HT]
Capacity:	162 / unknown (unknown)
Min. number of students:	unlimited
4EU+:	no
Virtual mobility / capacity:	no
State of the course:	taught
Language:	English
Teaching methods:	full-time
Note:	course can be enrolled in outside the study plan enabled for web enrollment priority enrollment if the course is part of the study plan

Guarantor:	prof. PhDr. Ladislav Krištoufek, Ph.D.
Teacher(s):	Anna Kábrtová prof. PhDr. Ladislav Krištoufek, Ph.D.
Class:	Courses for incoming students
Pre-requisite :	{Group of pre-requisites for JEB157 (JEB034 or JEB142)}

WS schedule Noticeboard

Annotation -

An introductory course to data analysis in R. The course covers the basics of practical programming in the R environment, including data structures, data manipulation, graphs, graphical outputs, basic statistics, variance analysis, test powers, bootstrapping, and component and factor analysis.

Last update: Krištoufek Ladislav, prof. PhDr., Ph.D. (19.09.2022)

Aim of the course -

The aim of Data Analysis in R is to lay foundations for analytical work with data complememtary to the other compulsory courses, namely Introductory Statistics, Statistics, and Econometrics I + II.

Last update: Krištoufek Ladislav, prof. PhDr., Ph.D. (19.09.2022)

Literature -

Kabacoff, Robert I. (2015): R in Action (2E), Manning Publications, Shelter Island, NY

Last update: Krištoufek Ladislav, prof. PhDr., Ph.D. (19.09.2022)

Teaching methods -

Traditional classes. Recordings of all classes are available online as well. Links to the videos are provided in the Syllabus section. The recordings are 1:1 with the standard classes.

Reschedulings:

14. 10. 2025 - NO CLASS
21. 11. 2025 - EXTRA CLASS (9:30AM, room 314) for 14. 10. 2025
5. 12. 2025 - EXTRA CLASS (9:30AM, room 314) for 28. 10. 2025

Software: R a RStudio (available on all computers in room 016), available here a here (freeware).

Last update: Krištoufek Ladislav, prof. PhDr., Ph.D. (06.10.2025)

Requirements to the exam -

There are 3 components to the final score and grade:

2 tracks in DataCamp (40 points)
3 assessments in DataCamp (30 points)
1 research report (30 points) (you can use this shared table to set up teams)

Use this LINK to register to DataCamp, fill in the profile (properly, use your name, it will be used to track fulfillment of assignments), and complete your assignments there. If you do not have a @fsv.cuni.cz/@cuni.cz/@o365.cuni.cz email, let me know, I will send you an invite.

Tracks (upload certificates of completion to the Study Roster, separately for the completed tracks):

Track 1: Skill Track "R Programming Fundamentals" (15 points) - by 23 November 2025 CEST
Track 2: Career Track "Data Analyst with R" (25 points) - by 25 January 2026 CET

Assessments (upload a printscreen of your finished assessments to the Study Roster, make sure you name is visible in the printscreen):

Assessment 1: Data Manipulation with R (10 points) - by 23 November 2025 CEST
Assessment 2: Statistics Fundamental in R (10 points) - by 7 December 2025 CET
Assessment 3: R Programming (10 points) - by 28 December 2025 CET
Scoring:

To get the score, use the DataCamp score x and fit it to (x-60)/80*100%
At least 50%, i.e. at least 5 points, from each assessment is a necessary (not a sufficient) condition for passing the Data Analysis in R course.
You can re-take the assessments twice a week during the whole semester. Remember that the last one counts (not necessarily the best one).

Research report (upload a zip file including the report, R code, and dataset, to the Study Roster):

Teams of up to 4 students.
Up to 8 pages (including everything but the code and data which will form separate attachments).
Submit by 1 February 2026 CET.

Grading scale follows the faculty regulations:

A: 90+
B: 80-90
C: 70-80
D: 60-70
E: 50-60
F: below 50

Last update: Krištoufek Ladislav, prof. PhDr., Ph.D. (15.11.2025)

Syllabus -

BLOCK I - GETTING STARTED:

Introduction to R and RStudio (Course info, R basics)
Creating a dataset (Intro, Data structures, Data input)
Basic data management (Basic data management)
Advanced data management (Math & Stats functions, Data-management challenge, Control-flow and others)
Getting started with graphs (ggplot basics, ggplot details)

BLOCK II - BASIC METHODS:

Basic graphs (Bar charts & tree maps, Histograms, kernels, box plots)
Basic statistics (Descriptive statistics, Contingency tables, Correlations, Parametric and non-parametric tests)

BLOCK III - INTERMEDIATE METHODS:

Regression basics (Types of regression, Picking the "best" model, Cross-validation)
Analysis of variance (Terminology, One-way ANOVA, Other ANOVA types)
Intermediate graphs (Scatter plots, Line charts, correlograms, mosaic plots)

Last update: Krištoufek Ladislav, prof. PhDr., Ph.D. (03.10.2024)