Course code InfT5043

Credit points 3

Free Statistical Software R

Total Hours in Course81

Number of hours for lectures12

Number of hours for seminars and practical classes12

Independent study hours57

Date of course confirmation18.10.2022

Responsible UnitInstitute of Computer Systems and Data Science

Course developers

author prof.

Līga Paura

Dr. agr.

author prof.

Irina Arhipova

Dr. sc. ing.

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 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. The course topics are the following: descriptive statistic with R, hypothesis testing for two samples a mean with R, analyse of variance (ANOVA) with R, correlation and regression with R.

Learning outcomes and their assessment

is able to show the depth or extends knowledge and understanding about possibilities of data manipulating and analysis in R and critical understanding about apply data analyses methods in students research projects (the practical works are developed, assessment tests successfully are written);
• Skills:
is able to independly use R software for data analysis, able to choose statistical methods according to the research project (the practical works are developed, independent work is developed); •
is able to independently to realize data analysis in master work by R software; able to perform critical analysis and to evaluation of the results (assessment tests successfully are written, independent work is developed and presented in practical classes).

Course Content(Calendar)

1. Introduction to R. [L – 1h]
2. Data importing, data management in R. [P – 0.5h]
3. Basic computations in R. [P – 0.5h]
4. Computations of statistical parameters in R. [L – 1h, P – 1h]
5. Data graphical presentation in R. [L – 1h, P – 1h]
6. Test of differences - independent t-test. [L – 2h, P – 2h]
7. Test of differences - dependent t-test. [L – 2h, P – 2h]
1st test: Data graphical presentation, tests of differences. [P – 1h]
8. One-way Analysis of Variance (ANOVA) in R. [L – 1h, P – 0.5h]
9. Assumptions of ANOVA test. [L – 1h, P – 1h]
10. Two-way Analysis of Variance (ANOVA) in R. [L – 1h, P – 1h]
11. Assumptions of ANOVA test. [L – 1h, P – 1h]
12. Bivariate correlation analysis in R. [L – 1h, P – 0.5h]
13. Bivariate regression analysis in R. Assumptions of regression analysis. [L – 2h, P – 1h]
14. Working with databases in R. [L – 1h]
2nd test: ANOVA and regression analysis. [P – 1h]

Requirements for awarding credit points

Formal test with a grade (Ia). The test assignment consists of a test of a practical task on the course subjects and the theoretical subjects acquired during the study course. All practical works and two tests should be executed.

Description of the organization and tasks of students’ independent work

The organization of independent work during the semester is independently studying literature, using academic staff member consultations. Homework has been developed and defended. Homework: at least two different statistical methods for the real data analysis should be used.

Criteria for Evaluating Learning Outcomes

Evaluation (Ia) depends of the semester cumulative assessment: 1st test - 40 points, 2nd test 40 points and homework – 20 points. Students who have a cumulative assessment of the study course less than 4 or wish to improve it (at least 4) hold the complex test during the session. The test includes practical part (80%) and theory (20%).

Compulsory reading

1. An Introduction to R. [skatīts 7.decembrī 2018.]
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.

Further reading

1. Dalgaard P. Introductory statistics with R. New York [etc.]: Springer, 2002. 267 p.
2. Rahlf T. Datendesign mit R: 100 Visualisierungsbeispiele. Munchen: Open sourse press, 2014. 426 s. [ITF, VSK 1 eks.]
3. Ekstrøm C. T. The R primer. Boca raton [etc.]: CRP press of Taylor&Francis Group, 2012. 287 p. [ITF, VSK 1 eks.]
4. Zumel N., Mount J. Practical Data Science with R. Shelter Island: Manning publications Co., 2014. 416 p. [ITF, VSK 1 eks.]


Elective course for master study programme “Information Technologies”; Elective course for other faculties master study programmes.