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Lecturer(s)
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Slámová Tereza, Mgr. Ph.D.
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Course content
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1. Introduction to R software, basic functionalities 2. Data types and objects, data structures (vector, matrix, array) 3. Data structures (data table, list) 4. Mathematical functions and constants 5. Graphics (high- a low-level functions) 6. Graphics (high- a low-level functions) 7. Creating functions and scripts, conditional commands 8. Cycle commands 9. Descriptive statistics in R (measures of location and dispersion, graphical representation of data) 10. Descriptive statistics in R (measures of association between two variables) 11. Inferential statistics in R (parameter estimation) 12. Inferential statistics in R (hypothesis testing) 13. Exercises 14. Exercises
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Learning activities and teaching methods
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Laboratory work
- Class attendance
- 28 hours per semester
- Preparation for credit
- 28 hours per semester
- Home preparation for classes
- 4 hours per semester
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Learning outcomes
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The course will include the important features of R.
Basic functionality of R and how to use it for a statistical task solving.
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Prerequisites
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Basic probability and statistics and computer skills.
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Assessment methods and criteria
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Student's performance analysis
Credit: Active participation on seminars, semestral work.
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Recommended literature
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C. Heumann, M. Schomaker, Shalabh. Introduction to Statistics and Data Analysis with Exercises, Solutions and Applications in R. 978-3-319-46160-1, 2016.
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W.K. Venables, D.M. Smith, the R Development Core Team. An Introduction to R.
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