I am looking at how camera angle affects species % cover when using photoquadrats, and so have a dataframe where the first column is the sample, the second is angle (a factor) and the rest of the columns are different species abundances (numeric). It looks something like this:
df <- data.frame(ID = c(1, 2, 3, 4, 5,6,7,8,9,10,11,12),
angle = c('0','0','0','10','10','10','20','20','20','30','30','30'),
spec1 = c(0.3,0.2,0.5,0.5,0.8,0.6,0.1,0.5,0.1,0.2,0.5,0.3),
spec2 = c(0.2,0.4,0.3,0.5,0.1,0.3,0.3,0,0.7,0.8,0.2,0.6),
spec3 = c(0.5,0.3,0.2,0,0.1,0.1,0.6,0.5,0.2,0,0.3,0.1))
I want to eventually run a PERMANOVA on this using adonis (I have never used this function before so this is all new to me). I am not sure how to make my dataframe purely abundance data in order to run dist and get the dissimilarity matrix I need without getting rid of the factors which are what I want to tell the difference between in the first place.
I hope that makes sense - can anyone help?
Just make a new table, where the first two columns have been removed - this is your community data matrix. Then when you call adonis you specify any predictors that you want from the specified data. Just make sure that your community data matrix and your data have the same number of rows.
library(vegan)
community <- df[,3:5]
adonis2(community ~ angle, data=df)