Deleting reversed duplicates with R

Question

I have a data frame in R that contains the gene ids of paralogous genes in Arabidopsis, looking something like this:

gene_x    gene_y
AT1            


        

Answer 1

mydf <- read.table(text="gene_x    gene_y
AT1       AT2
AT3       AT4
AT1       AT2
AT1       AT3
AT2       AT1", header=TRUE, stringsAsFactors=FALSE)

Here's one strategy using apply, sort, paste, and duplicated:

mydf[!duplicated(apply(mydf,1,function(x) paste(sort(x),collapse=''))),]
  gene_x gene_y
1    AT1    AT2
2    AT3    AT4
4    AT1    AT3

And here's a slightly different solution:

mydf[!duplicated(lapply(as.data.frame(t(mydf), stringsAsFactors=FALSE), sort)),]
  gene_x gene_y
1    AT1    AT2
2    AT3    AT4
4    AT1    AT3

Answer 2

A dplyr possibility could be:

mydf %>%
 group_by(grp = paste(pmax(gene_x, gene_y), pmin(gene_x, gene_y), sep = "_")) %>%
 slice(1) %>%
 ungroup() %>%
 select(-grp)

  gene_x gene_y
  <chr>  <chr> 
1 AT1    AT2   
2 AT1    AT3   
3 AT3    AT4  

Or:

mydf %>%
 group_by(grp = paste(pmax(gene_x, gene_y), pmin(gene_x, gene_y), sep = "_")) %>%
 filter(row_number() == 1) %>%
 ungroup() %>%
 select(-grp)

Or:

mydf %>%
 group_by(grp = paste(pmax(gene_x, gene_y), pmin(gene_x, gene_y), sep = "_")) %>%
 distinct(grp, .keep_all = TRUE) %>%
 ungroup() %>%
 select(-grp)

Or using dplyr and purrr:

mydf %>%
 group_by(grp = paste(invoke(pmax, .), invoke(pmin, .), sep = "_")) %>%
 slice(1) %>%
 ungroup() %>%
 select(-grp)

And as of purrr 0.3.0 invoke() is retired, exec() should be used instead:

mydf %>%
 group_by(grp = paste(exec(pmax, !!!.), exec(pmin, !!!.), sep = "_")) %>%
 slice(1) %>%
 ungroup() %>%
 select(-grp)

Or:

df %>%
 rowwise() %>%
 mutate(grp = paste(sort(c(gene_x, gene_y)), collapse = "_")) %>%
 group_by(grp) %>%
 slice(1) %>%
 ungroup() %>%
 select(-grp)

Answer 3

Another tidyverse-centric approach but using purrr:

library(tidyverse)

c_sort_collapse <- function(...){
  c(...) %>% 
    sort() %>% 
    str_c(collapse = ".")
}

mydf %>% 
  mutate(x_y = map2_chr(gene_x, gene_y, c_sort_collapse)) %>% 
  distinct(x_y, .keep_all = TRUE) %>% 
  select(-x_y)
#>   gene_x gene_y
#> 1    AT1    AT2
#> 2    AT3    AT4
#> 3    AT1    AT3