Subset a dataframe for rows that are duplicated across a set of columns.

Usage

check_dups(mydf, checkcols = NULL, dupvec = NULL, dupTFmatchesInput = TRUE)

Arguments

mydf: A dataframe.
checkcols: A vector of column names that you want to check for duplicates. Will also work with column indices. Alternative to dupvec.
dupvec: A vector to use to check for duplicate values. Length(dupvec) = nrow(mydf). Alternative to column names.
dupTFmatchesInput: If TRUE, the TRUE/FALSE vector indicating duplicated values corresponds to the input dataframe. If FALSE, it corresponds to the subsetted dataframe containing duplicate values. values

Value

Returns a list containing the number of unique column combinations that have duplicate values (ndups), a subsetted dataframe containing all duplicated values, and a TRUE FALSE vector to facilitate removal of duplicate rows (created by duplicated). This vector can correspond to the input dataframe (mydf) or the output dataframe (dupdf), depending on the choice for dupTFmatchesInput.

Examples

testdf <- data.frame("col1" = c("spam","eggs","spam","spam"),
                     "col2" = c(1,2,1,1),
                     "col3" = c("cat","fish","dog","cat"),
                     "col4" = c(1,2,3,4))
check_dups(testdf, checkcols = c("col1","col2"))
#> $ndups
#> [1] 1
#> 
#> $dupdf
#>   col1 col2 col3 col4
#> 1 spam    1  cat    1
#> 3 spam    1  dog    3
#> 4 spam    1  cat    4
#> 
#> $dupTF
#> [1] FALSE FALSE  TRUE  TRUE
#> 
check_dups(testdf, checkcols = c(1,2,3))
#> $ndups
#> [1] 1
#> 
#> $dupdf
#>   col1 col2 col3 col4
#> 1 spam    1  cat    1
#> 4 spam    1  cat    4
#> 
#> $dupTF
#> [1] FALSE FALSE FALSE  TRUE
#>