R language, a powerful tool for environmental data analysis | Christian Mong

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R language, a powerful tool for environmental data analysis

It is important to categorize landscapes in a meaningful way. What can we build and how should we build? Which buffer zones must we include? The most valuable landscapes may be vulnerable to various forms of influence, and what must be emphasized is dependent on the local context. Landscapes have different value for agriculture: quality of soil, site quality in forests, and grazing conditions in outlying areas. Forest soil and peat lands can also be assessed by how much carbon is stored in organic materials.

Biologists often use a section of natural landscape and separate it into vegetation types based on prevailing plant species. The Norwegian Directorate for Nature Management has taken these vegetation type sections and picked out those which are vulnerable. These are called habitats.

The purpose of this course is to teach how the R statistical environment language can be applied to biological data analysis. After this course, the students will be able to use R for analyzing diverse data types from very different biological experiments. The theoretical aspects of the methodologies will be introduced, and after that, assignments and activities will provide opportunities to explore practical ways of performing the analyses. You will learn how to use R for performing statistical analysis relevant for molecular biologists. You will learn how to perform simple sequence analysis with R. You will get an essential overview of biological network analysis and the highly popular enrichment analysis of gene lists.



Christian Mong

Christian E. Mong is an ecological advisor for urban development, from overarchingurban and landscape planning right down to planning and design of green spaceand gardens. I can also perform landscape analysis, mapping biological values and assist as approved ecologist in environmental certification (BREEAM NOR).