I find the functions pivot_wider() and pivot_longer() in the package tidyr to be the most explicit for these operations. There is a simple introduction in r4ds, and more complex usecases in that article.
In your case, you want to do 2 things: bring pop from the names to a column, and bring SNPs from a column to the names. So you can apply these two transformations successively:
library(tidyr)
SNPs<-c("SNP_A", "SNP_B","SNP_C")
pop1_allele_count<-c(1,3,5)
pop1_allele_number<-c(100,100,100)
pop2_allele_count<-c(5, 7, 8)
pop2_allele_number<-c(100, 100, 100)
pop3_allele_count<-c(2, 3, 4)
pop3_allele_number<-c(100, 100, 100)
main<-data.frame(SNPs,pop1_allele_count,
pop1_allele_number,
pop2_allele_count,
pop2_allele_number,
pop3_allele_count,
pop3_allele_number)
main
#> SNPs pop1_allele_count pop1_allele_number pop2_allele_count
#> 1 SNP_A 1 100 5
#> 2 SNP_B 3 100 7
#> 3 SNP_C 5 100 8
#> pop2_allele_number pop3_allele_count pop3_allele_number
#> 1 100 2 100
#> 2 100 3 100
#> 3 100 4 100
pivot_longer(main,
-SNPs,
names_to = c("pop", "type"),
names_pattern = "(pop[\\d])_([a-z_]+)",
values_to = "vals") |>
pivot_wider(id_cols = pop,
names_from = c("type", "SNPs"),
names_sep = "_",
values_from = vals)
#> # A tibble: 3 x 7
#> pop allele_count_SNP_A allele_number_SNP_A allele_count_SN~ allele_number_S~
#> <chr> <dbl> <dbl> <dbl> <dbl>
#> 1 pop1 1 100 3 100
#> 2 pop2 5 100 7 100
#> 3 pop3 2 100 3 100
#> # ... with 2 more variables: allele_count_SNP_C <dbl>,
#> # allele_number_SNP_C <dbl>
Created on 2022-02-04 by the reprex package (v2.0.1)
