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Data Analysis with R

Course developer

Course abstract

The aim of the course is to provide basic knowledge of the R language and on the skills of writing R scripts for practical applications. The course will focus on the data visualization and data analysis on different topics and data manipulation with R. Students acquire to use R for reading data, writing functions, making informative graphs, and applying statistical methods. During the studies the real examples related to different subject are using. Students will use the acquired knowledge in the course projects and master's thesis.

Learning outcomes and their assessment

Knowledge:
• depth knowledge and critical understanding about data visualisation and data analysis methods,
• depth knowledge about R software, choose and apply methods according to research task - (completed practical works, completed tests with calculations, theory test);
Professional skills:
• independently choose appropriate data visualisation and data analysis methods according to data analysis theory, perform data analysis in research work using R (completed practical works, developed independent work);
Competences:
• to realize data visualisation and data analysis in master work by using R software, to interpret the results and formulate conclusions, to make decisions and to analyse results (independent work, tests with calculations).

Course Content(Calendar)

1. Introduction to the study course. Classification of data. Graphical representation of data. Classification of data processing methods. Data processing tools. Introduction to R. [Lectures - 1h, Practical works - 2h]
2. Data importing and export, data management in R. Basic computations in R. Computations of statistical parameters in R. [L – 1h, P – 2h]
3. Using the R packages. The RMarkdown package: combining R code, data analysis results, and written comments into a single formatted document. [L – 1h, P – 2h]
4. Data graphical presentation in R. Graphical parameters [L – 1h, P – 2h]
5. Hypothesis testing. Analysis of two paired and unpaired samples with R. [L – 2h, P – 2h]
6. Correlation analysis, one-factor regression analysis, testing of regression analysis assumptions. [L – 1h, P – 2h]
7. Multivariate regression analysis, assumptions of regression analysis. [L – 1h, P – 1h]
8. Data transformation and nonlinear regression models. [L – 1h, P – 1h]
1st test: graphs, two samples analysis, one factor and multiple regression analysis. [Practical works - 2h]
9. Contingency analysis. Hi2 test, Fisher test. [L – 1h, P – 2h]
10. Cluster analysis: hierarchical and non-hierarchical. Hierarchical cluster analysis: Calculation of distance matrix; clustering methods. [L – 1h, P – 2h]
11. Non-hierarchical cluster analysis: k-means method. [L – 1h, P – 2h]
12. Discriminant analysis [L – 1h, P – 2h]
13. Logarithmic regression: one and multiple factors. [L – 1h, P – 2h]
14. Work in the R environment with the large data array. Detection of outliers: graphical approach and statistical test approach. [L – 1h, P – 2h]
15. Work in the R environment with the large data array. Data cleaning. [L – 1h, P – 1h]
2nd test: Contingency analysis, cluster and discriminant analysis. [Practical works - 2h]

Requirements for awarding credit points

The course includes two test with calculations - to be taken during practice work in classroom. Independent work. Theory test. Final assessment of the study course – exam is given as an accumulative assessment of the study results.

Description of the organization and tasks of students’ independent work

Within the framework of the study course description for independent work is given 114 hours. Independent studies (work) are organized as follows: preparing for theory test (34 hours); preparing independent work with calculations (40 hours); learning and preparing for tests with calculations (40 hours).

Criteria for Evaluating Learning Outcomes

The final grade of exam in the study course includes:
20% Theory test: methods classification and application for data analysis;
20 % independent work: creating a report based on the selected data and write the interpretation of the obtained results;
60% Tests with calculations: contingency analysis, correlation and regression analysis, cluster analysis, discriminant analysis. The evaluation of the works depend on the degree of completion.
Students who have a cumulative assessment of the study course less than 4 hold the exam during the session. The exam includes practical part (70%) and theory (30%).

Compulsory reading

1. An Introduction to R. http://cran.r-project.org/doc/manuals/R-intro.html [skatīts 7.decembrī 2023.]
2. Robert I. Kabacoff (2015) R in action: data analysis and graphics with R. Shelter Island, NY: Manning, 579 p. 3.James Gareth, at al. (2017) An introduction to statistical learning: with applications in R. New York : Springer, 426 p.
3. Kirk Andy. Data visualisation: a handbook for data driven design. - Los Angeles: SAGE, 2019. -312 lpp.

Further reading

1. Data science & big data analytics: discovering, analyzing, visualizing and presenting data / EMC Education Services. - Indianapolis, IN: John Wiley and Sons, 2015. - xviii, 410 lpp.
2. Gujarati D. N. Basic econometrics. 3rd ed. New York [etc.]: McGraw-Hill, Inc., 1995. 838 p.

Periodicals and other sources

1. Journal of Data Analysis and Information Processing: ISSN Online: 2327-7203
www.scirp.org/journal/jdaip

Notes

Obligatory course for professional master’s study programme “Information Technologies”