Data Analytics in R 1

Data

Official data in SubjectManager for the following academic year: 2025-2026

Topic

In this course, students will have the opportunity to learn advanced data management methods in the R environment to supplement their analytical skills.

Primary focus will be on advanced level script writing, data management using the tidyverse package, writing basic custom functions, and data export and reporting. Students will primarily learn by examples from sociological, psychological, demographic and business application.

Topics – Data analysis

1.      Basic data management in R: objects, vectors, data frames.

2.      Transforming and importing data.

3.      Handling lists in R.

4.      Creating and working with matrices.

5.      Working with time-based data.

6.      Efficient script writing in base R.

7.      Introduction to writing functions in R.

8.      Introduction to the tidyverse: the basic concepts of “tidy” data. I.

9.      Introduction to the tidyverse: the basic concepts of “tidy” data. II.

10.   Advanced visualization using ggplot2.

Topics – Statistics

1.      Descriptive methods for categorical and continuous data.

2.      Hypotheses testing I.: t-tests, chi-square and correlation.

3.      Hypothesis testing II.: anova and non-parametric methods.

4.      Regression modeling: OLS linear regression.

5.      Regression modeling: categorical outcomes.

6.      Regression trees, forests and other machine learning methods.

Lectures

Practices

Seminars

• 1.

    Basic data management in R: objects, vectors, data frames.

  - Bóvári-Biri Judit
• 2.

  Transforming and importing data.

  - Bóvári-Biri Judit
• 3.

  Handling lists in R

  - Bóvári-Biri Judit
• 4.

  Creating and working with matrices

  - Bóvári-Biri Judit
• 5.

  Working with time-based data

  - Bóvári-Biri Judit
• 6.

  Efficient script writing in base R

  - Bóvári-Biri Judit
• 7.

  Introduction to writing functions in R

  - Bóvári-Biri Judit
• 8.

  Introduction to the tidyverse: the basic concepts of “tidy” data. I.

  - Bóvári-Biri Judit
• 9.

  Introduction to the tidyverse: the basic concepts of “tidy” data. II

  - Bóvári-Biri Judit
• 10.

  Advanced visualization using ggplot2

  - Bóvári-Biri Judit
• 11.

  Descriptive methods for categorical and continuous data

  - Bóvári-Biri Judit
• 12.

  Hypotheses testing: t-tests, chi-square and correlation

  - Bóvári-Biri Judit

Reading material

Obligatory literature

Literature developed by the Department

PPT slides

Notes

Recommended literature

·        Wickham et al. (2025). R for Data Science. Available: https://r4ds.hadley.nz/

·        Bonell, J – Ogihara, M. (2024). Exploring Data Science with R and the Tidyverse.

·        Wickham, H. (2025). ggplot2: Elegant Graphics for Data Analysis. Available: https://ggplot2-book.org/

·        Boehmke, B. C. (2014). Data Wrangling with R.

·        Békés, G. – Kézdi, G. (2021). Data Analysis for Business, Economics, and Policy. Relevant Chapters.

·        Freedman, D. – Pisani, R. – Purves, R. (2007). Statistics, Fourth Edition. Relevant Chapters.

·        Agresti, A. (2018). An Introduction to Categorical Data Analysis, Third Edition. Relevant Chapters.

Conditions for acceptance of the semester

Mandatory attendance, completion of homework, end course analysis.

Mid-term exams

Homeworks, end course analysis

Making up for missed classes

No option

Exam topics/questions

End course analysis in practice

Examiners

Instructor / tutor of practices and seminars

• Bóvári-Biri Judit