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Lecturer(s)
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Šipoš Jan, Mgr.
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Kuras Tomáš, RNDr. Ph.D.
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Course content
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1-2. Introduction to the web pages, description of console, introduction to the syntax. 3-4. Work with libraries and packages. 5-6. Formulation of simple commands, objects in R program. 7-8 Formulation of the functions in the R. 9-10. Application of descriptive statistic on concrete data. 11-12. Introduction to the creation of plots. 13-14. Possibilities of using basic regression methods in R program.
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Learning activities and teaching methods
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Monologic Lecture(Interpretation, Training), Dialogic Lecture (Discussion, Dialog, Brainstorming), Demonstration
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Learning outcomes
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The aims of the subject is introduction new statistical program to students and teach them to use this program independently. R software is open source program that means it is freely available. Students will gain knowledge about web pages of R program, they will be able to use help pages and other documents which can help to use this program. Less familiar program interface (it means using console for writing commands) will require knowledge of programme language syntax. R program contains a lot of possibilities how to analyze the data, number of new methods of analysis, libraries and packages are still added to the program (e.g. Vegan for ecology, Bioconductor for bioinformatics, Rgeo for geografy, ade4 for multidimensional data analysis, ape for taxonomy). Therefore one of the objectives will be teaching the studens how to use the different packages . R program is suitable for modern statistical analysis, therefore the main attention will be devoted to apply this method on the concrete data.
- orientation and knowledge of syntax in R program - ability to use statistical analysis in R program - using of modern statistical method in R program - ability to individual study of new satistical methods
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Prerequisites
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- knowledge of basic statistical methods - ability to work with tables in program Microsoft Excel - awareness of general statistical principes - willingness to study new programming language
BOT/BSTSB
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Assessment methods and criteria
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Written exam
- active participation on the seminars - work out individual assignment
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Recommended literature
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Crawley M.J., Hoboken N.J. (2007). The R book. Wiley..
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Drozd P. (2007). Cvičení z biostatistiky. Základy práce ze softwarem R. Universitas Ostraviensis. ISBN 978-80-7368-433-4.
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Faraway J.J. (2004). Linear Models with R. Chapman & Hall, New York..
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Faraway J.J. (2005). Extending the Linear Model with R: Generalized Linear, Mixed Effects and Nonparametric Regression Models (Texts in Statistical Science). Chapman & Hall, New York..
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Pekár S., Brabec M. (2009). Moderní analýza biologických dat. Scientia, Praha. ISBN 978-80-86960-44-9.
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Wood S. (2006). Generalized Additive Models: An Introduction with R (Texts in Statistical Scence). Chapman & Hall, New York..
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