114-0557/03 – Economic analysis with R (EAR)
| Gurantor department | Department of Economics | Credits | 4 |
| Subject guarantor | doc. Antonio Rodríguez Andrés, Ph.D. | Subject version guarantor | doc. Antonio Rodríguez Andrés, Ph.D. |
| Study level | undergraduate or graduate | Requirement | Choice-compulsory type B |
| Year | 2 | Semester | winter |
| | Study language | English |
| Year of introduction | 2020/2021 | Year of cancellation | 2021/2022 |
| Intended for the faculties | EKF | Intended for study types | Follow-up Master |
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
Lectures
Tutorials
Summary
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:
Recommended literature:
Way of continuous check of knowledge in the course of semester
The lectures contain slides and we will go trough some in class labs which try to make it easier to digest the materials for students. On this way, they would get hands on experience with applied data analysis issues and R coding.
No final exam. Students need to elaborate their own research project using data, and R code. The course assessment will be based on this research project.
E-learning
Other requirements
No other requirements.
Prerequisities
Subject has no prerequisities.
Co-requisities
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
Occurrence in study plans
Occurrence in special blocks
Assessment of instruction
Předmět neobsahuje žádné hodnocení.