I have a dataset containing several variables and I wish to statistically test the variances (Kruskal-test) for each variable seperately.
My data (df) looks like that: (carbon and nitrogen content for diffrent agricultural managements (see name)). I have 16 groups (to simplify it, I´d say, I have got 8 groups):
extract of the data
1. List item
name    N_cont  C_cont  agriculture
C_ero   1,064   8,380   1
C_ero   0,961   8,086   1
C_ero   0,977   8,331   1
Ds_ero  1,767   17,443  2
Ds_ero  1,802   18,264  2
Ds_ero  2,083   20,112  2
Ms_ero  1,547   14,380  3
Ms_ero  1,566   15,313  3
Ms_ero  1,505   14,760  3
Md_ero  1,512   14,303  4
Md_ero  1,656   15,331  4
Md_ero  1,500   13,788  4
C_upsl  1,121   10,581  5
C_upsl  1,159   10,460  5
C_upsl  1,223   10,171  5
Ds_upsl 1,962   20,656  6
Ds_upsl 1,784   16,780  6
Ds_upsl 1,720   17,482  6
Ms_upsl 1,578   16,228  7
Ms_upsl 1,634   15,331  7
Ms_upsl 1,394   13,419  7
Md_upsl 1,286   11,824  8
Md_upsl 1,241   11,452  8
Md_upsl 1,317   11,932  8
I already put a factor for the agriculture
df$agriculture<-factor(df$agriculture)
I can do statistical tests compairing all of the 16 groups.
e.g. kruskal.test(df$C,df$agriculture)
But now I would like to do statistic tests just for specific groups out of the 8 groups, e.g. those which contain e.g. an C (Conventional) or rather DS (Direct seeding) in the name column
or e.g. ero (eroding site) or upsl (upper slope) 
It did try grep or split, but it did not work, because the dimension of x and y should be the same.
Do you have any clue?
 
     
    