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Lecturer(s)
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Lisner Aleš, RNDr. Ph.D.
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Kozel Petr, RNDr. Ph.D.
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Course content
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Content of lectures: 1 Introduction, data import, basic orientation in MS excel software, searching, cell replacement, data collection errors 2 Conditional formatting, list management, basic descriptive statistics. 3 Functions and their use in creating extensive datasets 4 Contingency tables 5 Basic data visualization 6 Resolving typical errors and issues in large datasets 7 Basic R program control, data import/export, online data sources 8 Data manipulation, merging, splitting, sorting, and formats 9 Conditions and loops 10 Basic descriptive statistics and data visualization in R 11 Phylogenetic data, their sources, basic applications in ecology 12 Data sources, databases, data formats, and conversions between them 13 Review and practice test Content of tutorials/seminar: Tutorials complement the lectures and are not temporally separated from them
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Learning activities and teaching methods
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unspecified
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Learning outcomes
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The aim of the course is to familiarize students with basic and advanced methods of preparing ecological data for further analysis. The course focuses on using basic functions for data manipulation, creating contingency tables, and partial data files in MS Excel and R programs. Significant attention is given to identifying and solving errors and issues that are typical for biological datasets. There is a strong emphasis on working with datasets from various research areas (plant ecology, entomology, parasitology, etc.), different sources (personal, published, database data), and varying data quality. Upon completing the course, students should be able to clean and prepare data from ecological experiments, observational studies, or online databases for statistical analysis. The first 6 teaching hours are dedicated to data manipulation in MS Excel, and the next 6 teaching hours are dedicated to data manipulation in the R program.
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Prerequisites
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unspecified
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Assessment methods and criteria
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unspecified
Students are required to actively participate in the course, complete assigned homework (approximately 5 assignments). At the end of the semester, students will clean and prepare a provided dataset for statistical analysis.
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Recommended literature
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Alexander, M. & Kusleika, D. (2022). Microsoft Excel 365 Bible. John Wiley & Sons Inc.
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Locke, S. (2017). Data Manipulation in R: Colour edition (R Fundamentals). Locke Data Ltd..
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Wickham, H. & Grolemund, G. (2017). R for Data Science: Import, Tidy, Transform, Visualize, and Model Data. O'Reilly Media.
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