How to do redundancy Analysis with R language

Today, I will talk to you about how to use R language to do redundancy analysis, many people may not know much about it. In order to make you understand better, the editor has summarized the following contents for you. I hope you can get something according to this article.

Redundancy analysis (redundancy analysis, RDA) has heard of it before. It seems that it is often used in ecological research, mainly to explore the relationship between the environment and some sample indicators. Recently, I have been looking at some content related to population genetics, and found that RDA can also be used in population genetics. For example, this reference link https://popgen.nescent.org/2018-03-27_RDA_GEA.html introduces this analysis, but the main research content has not been understood by myself: generally speaking, it seems that the chip technology was used to measure some wolf genotypes and collect the environmental data of the wolf's living place. Genotypic data and environmental data were analyzed by RDA. There is still some ambiguity in this view, and we still need to look at it carefully. The two papers corresponding to this link

Https://onlinelibrary.wiley.com/doi/abs/10.1111/mec.13364

Https://onlinelibrary.wiley.com/doi/full/10.1111/mec.14584

When looking for materials, I also found another paper.

Https://onlinelibrary.wiley.com/doi/abs/10.1111/1755-0998.12906

The corresponding data and code of the paper

Https://github.com/Capblancq/RDA-genome-scan

Today's tweet repeats the code for redundancy analysis in this paper.

The first is to read in the data.

The 1:14 column of the sim1.csv dataset is environmental data, followed by genotype data.

Geno