General information
| Course type | AMUPIE |
| Module title | Basic R Programming For Scientists |
| Language | English |
| Module lecturer | dr hab. Łukasz Grewling, dr Paweł Bogawski, dr Bartosz Łabiszak |
| Lecturer's email | bogawski@amu.edu.pl |
| Lecturer position | Assistant Professor |
| Faculty | Faculty of Biology |
| Semester | 2024/2025 (winter) |
| Duration | 30 |
| ECTS | 4 |
| USOS code | 01-RPROGRAM-PIE |
Timetable
Lectures: 4 hours
Computer workshops: 26 hours
The schedule of classes will be given later
Module aim (aims)
- To use R software in analysing environmental data
- To acquire basic programming skills in R
- To perform simple statistical modelling in R
- To download data from servers via R
- To visualize scientific data in R
- To manipulate 3-dimensional laser scanning data and measure plant features using remote sensing in R
- To process different climatic, phenological, soil and other environmental data in R
- To model current, past and future species distribution in R
- To prepare reports, theses directly from R
Pre-requisites in terms of knowledge, skills and social competences (where relevant)
No programming knowledge required – this course aims to start from the basics
Syllabus
Week 1: Introduction to R environment (lecture, 2h)
Week 2: How R can enhance the research: examples in biology and geography (lecture, 2h)
Week 3: Basics of manipulating data in R environment (R syntax, importing, managing, cleaning data) (computer workshop, 2h)
Week 4: Basics of manipulating data in R environment (export from R, format conversions, data types, functions and additional packages) (computer workshops, 3h)
Week 5: Downloading data from servers directly from R (for example, climatic and biological station/point data) (computer workshop, 3h)
Week 6: Statistical testing of hypotheses, preparing simple statistical models and performing their assessment (computer workshop, 3h)
Week 7: Visualisation of the results, creating boxplots, barplots and scatterplots and others using base and ggplot2 R package, preparing R Markdown reports directly from R (computer workshop, 3h)
Week 8: R software for maps, satellite and aerial images (for example mapping in ggplot2) (computer workshop, 3h)
Week 9: Laser scanning data as a valuable source of environmental/biotic information (computer workshop, 3h)
Week 10: Environmental niche modelling (computer workshop, 3h)
Week 11: Machine learning, loops, animations, summary, reviewing the acquired knowledge (computer workshop, 3h)
Reading list
R software tutorials, e.g.
Lovelace R. et al. 2019. Geocomputation with R. https://geocompr.robinlovelace.net/
Paradis E. 2005. R for beginners: https://cran.r-project.org/doc/contrib/Paradis-rdebuts_en.pdf
Quick-R tutorial: https://www.statmethods.net/r-tutorial/index.html
Statistical tools for high-throughput data analysis: http://www.sthda.com/english/
Other R tutorials, Videos freely available in the Internet, YouTube
Wang K. et al 2010. Remote Sensing of Ecology, Biodiversity and Conservation: A Review from the Perspective of Remote Sensing Specialists. Sensors 10, 9647-9667; doi:10.3390/s101109647
United Nations 2000. Handbook on geographic information systems and digital mapping. New York