ESF:MPE_DAR2 Data analysis in R 2 - Course Information

MPE_DAR2 Data analysis in R for practical use 2

Extent and Intensity

0/2/0. 4 credit(s). Type of Completion: zk (examination).
Synchronous online teaching

Teacher(s)

Ing. Jakub Solnička (seminar tutor)

Guaranteed by

prof. Ing. Martin Kvizda, Ph.D.
Department of Economics – Faculty of Economics and Administration
Contact Person: Mgr. Jarmila Šveňhová
Supplier department: Department of Economics – Faculty of Economics and Administration

Timetable

Fri 8:00–9:50 Virtuální místnost, except Fri 20. 2., except Fri 10. 4.

Prerequisites

This course builds on the knowledge gained in MPE_DAAR Data Analysis in R for practical application. The necessary knowledge to enrol in the course can also be acquired through self-study (e.g. rlandio.cz) or in BPE_AVED Analysis and Visualization of Economic Data.

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 24 student(s).
Current registration and enrolment status: enrolled: 15/24, only registered: 0/24, only registered with preference (fields directly associated with the programme): 0/24

fields of study / plans the course is directly associated with

• Economic Policy (programme ESF, N-HOSP)

Abstract

The course aims to deepen the knowledge of the R programming language, especially when working with the dplyr, ggplot2, leaflet and flexdashboard packages. Students should be able to create advanced data analyses on a variety of not only economic topics and present them in the form of a visually attractive web page also created in R.

Learning outcomes

Upon successful completion of the course, students will be able to work independently in R and create advanced data analyses on various economic topics. Students will practice all knowledge and concepts on their own projects.

Key topics

1) Repetition: familiarization with the course content, repetition of the basics of working in R 2) Data manipulation 1: working with the dplyr package (filter, slice, rename, select, mutate, transmute, distinct, arrange, join) 3) Data manipulation 2: working with the dplyr package (pipe, group by and summarize) 4) Working with graphs 1: creating line graphs in ggplot2 5) Working with charts 2: creating bar charts in ggplot2 6) Working with charts 3: creating box plots and histograms in ggplot2 7) Working with charts 4: creating advanced charts using the dplyr package 8) Maps: creating interactive maps using the leaflet package 9) Statistical analysis 1: statistical tests and models in the R language environment 10) Statistical analysis 2: statistical tests and models in the R language environment 11) Web Presentation 1: creating custom CSS stylesheets, chart presentation issues and the problem of CSS and images in the flexdashboard environment 12) Web Presentation 2: creating interactive graphs (plots, dygraphs), presentation presentation of source data (data tables) 13) Repetition

Study resources and literature

required literature
• WICKHAM, Hadley; Mine ÇETINKAYA-RUNDEL and Garrett GROLEMUND. R for data science : import, tidy, transform, visualize, and model data. 2nd edition. Tokyo: O'Reilly, 2023, xxiii, 548. ISBN 9781492097402. info

recommended literature
• SOLNIČKA, Jakub. Cesta kolem světa za datovou analýzou. 2020. Dostupné z: https://www.rlandio.cz.

Approaches, practices, and methods used in teaching

Classes are held every week and are dedicated to practical demonstrations in the RStudio environment.

Method of verifying learning outcomes and course completion requirements

The course is assessed based on an oral defence of a data analysis project of the student's choice. The project takes the form of a web presentation.

Language of instruction

Czech

Further Comments

Study Materials
The course is taught annually.

• Enrolment Statistics (recent)
  
• Permalink: https://is.muni.cz/course/econ/spring2026/MPE_DAR2