114-0557/04 – Economic analysis with R (EAR)

Gurantor departmentDepartment of EconomicsCredits4
Subject guarantordoc. Antonio Rodríguez Andrés, Ph.D.Subject version guarantordoc. Antonio Rodríguez Andrés, Ph.D.
Study levelundergraduate or graduateRequirementChoice-compulsory type B
Study languageEnglish
Year of introduction2020/2021Year of cancellation2022/2023
Intended for the facultiesEKFIntended for study typesFollow-up Master
Instruction secured by
LoginNameTuitorTeacher giving lectures
AND0096 doc. Antonio Rodríguez Andrés, Ph.D.
Extent of instruction for forms of study
Form of studyWay of compl.Extent
Part-time Credit 0+12

Subject aims expressed by acquired skills and competences

Passing the course, student will be able to work with important basics of the descriptive analysis and statistics, data visualization, regression analysis, binary choice models, count and panel data models using statistical software R.

Teaching methods



Course contents includes the basics of R, pre-processing and data visualization techniques, study and application of linear, non-linear models, and basic panel data models. Students will apply and analyze the results using the R software. The student will prepare and present both in oral and written form a research project in which the concepts studied will be discussed and applied to a particular problem.

Compulsory literature:

LANDER, Jared P. [i]R for everyone: Advanced Analytics and Graphics.[/i] Crawfordsville: Adison Wesley, 2014. ISBN-13: 978-0-321-88803-7. VERZANI, John. [i]Using R for introductory statistics.[/i] [online]. Available from: https://cran.r-project.org/doc/contrib/Verzani-SimpleR.pdf. WOOLDRIDGE, Jeffrey M. [i]Introductory econometrics: a modern approach. [/i] Sixth edition. Boston: Cengage Learning, 2016. ISBN 978-1-305-27010-7.

Recommended literature:

DOUGHERTY, Christopher. [i]Introduction to econometrics. [/i] Fifth edition. Oxford: Oxford University Press, 2016. ISBN 978-0-19-967682-8. JAMES, G., D. WITTEN, T. HASTIE and R. TIBSHIRANI. [i]An Introduction to Statistical Learning Applications in R. [/i] Stanford: Springer, 2012. Available from: http://www-stat.stanford.edu/~tibs/. NEUSSER, Klaus. [i]Time series econometrics.[/i] Switzerland: Springer, 2016. Springer texts in business and economics. ISBN 978-3-319-32861-4.

Way of continuous check of knowledge in the course of semester


Other requirements



Subject has no prerequisities.


Subject has no co-requisities.

Subject syllabus:

1. Getting R started: Downloading R, R version, Installing 2. The R Environment: Command line, and R studio 3. R packages: installing packages, loading packages 4. Basics of R: basic math, variables, missing data, vectors 5. Types of data: data frames, matrices, lists, and arrays 6. Reading data into R: Reading CSV files, reading Excel data, reading other datasets (Stata, and SPSS) 7. Basic graphs in R: the use of plot function 8. Advanced graphs using ggplot 9. Basic statistics: Summary statistics, covariance and correlation 10. Linear Models: Simple and multiple regression models 11. Generalized linear models: logistic models 12. Count data models: negative binomial and poisson models 13. Introduction to panel data: fixed and random effects models 14. Creating reports using R: The knitr command with R markdown

Conditions for subject completion

Conditions for completion are defined only for particular subject version and form of study

Occurrence in study plans

Academic yearProgrammeField of studySpec.ZaměřeníFormStudy language Tut. centreYearWSType of duty
2023/2024 (N0311A050012) Applied economics (S02) Economic Development K Czech Ostrava Choice-compulsory type B study plan
2022/2023 (N0311A050012) Applied economics (S02) Economic Development K Czech Ostrava Choice-compulsory type B study plan
2021/2022 (N0311A050012) Applied economics (S02) Economic Development K Czech Ostrava Choice-compulsory type B study plan

Occurrence in special blocks

Block nameAcademic yearForm of studyStudy language YearWSType of blockBlock owner

Assessment of instruction

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