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Lecturer(s)
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Dostál Daniel, PhDr. Ph.D.
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Course content
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Basic operators Types of variables Functions Importing data from different sources Loops Packages Graphical outputs
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Learning activities and teaching methods
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Demonstration, Projection (static, dynamic)
- Attendace
- 25 hours per semester
- Homework for Teaching
- 50 hours per semester
- Homework for Teaching
- 20 hours per semester
- Attendace
- 5 hours per semester
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Learning outcomes
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The course introduces students to the statistical program R. Students completing the course are able to import data of various formats into the R environment, perform basic and advanced statistical procedures and are able to program scripts in the R language for processing large and complicated datasets. The R programming language is a standard in statistics and contemporary psychometry. The course is strongly recommended for students who want to enroll in a doctoral program or have research ambitions outside the academic world. R i R Studio are free to download.
Students will learn to write scripts in the R language for obtaining data from various sources, their cleaning and preparation, various statistical calculations up to the creation of graphical, tabular or other outputs.
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Prerequisites
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Knowledge of descriptive and inferential statistics. Statistical linear models.
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Assessment methods and criteria
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Student performance
Students receive credit for writing a script in the R language, which processes data of a known format into a predetermined form (eg automatically generates graphs or tables of statistical procedure results). Credit assignment students compiled independently at home.
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Recommended literature
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Robert I. Kabacoff. (2015). R in Action. New York, Manning Publications.
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