How to track changes across columns

[Irakli]

I cannot share the data, but the principle of what I want to achieve is easy - across several columns, I want to filter out the rows that do not change (from row to row, but only when non of the columns change). Overall, I want to track changes in the rows, if this makes it more simple.

What would be the best way to filter the dataset that way?

Thank you in advance.

[FJCC]

Here is one possible solution, though it isn't very elegant.

library(dplyr)

DF <- data.frame(A=c(1,1,1,1,1),B=c(1,3,1,1,1),C=c(1,1,1,1,2))
#Row 4 should be removed
DF
#>   A B C
#> 1 1 1 1
#> 2 1 3 1
#> 3 1 1 1
#> 4 1 1 1
#> 5 1 1 2
lagged <- rbind(c(NA,NA,NA),DF[-nrow(DF),])
colnames(lagged) <- paste("L",colnames(lagged),sep="_")
lagged                
#>   L_A L_B L_C
#> 1  NA  NA  NA
#> 2   1   1   1
#> 3   1   3   1
#> 4   1   1   1
#> 5   1   1   1
Combinded <- cbind(DF,lagged)
Combinded
#>   A B C L_A L_B L_C
#> 1 1 1 1  NA  NA  NA
#> 2 1 3 1   1   1   1
#> 3 1 1 1   1   3   1
#> 4 1 1 1   1   1   1
#> 5 1 1 2   1   1   1
Combinded <- Combinded |> rowwise() |> 
  mutate(Flag = all(c_across(A:C) == c_across(L_A:L_C))) 

DF <- Combinded |> filter(is.na(Flag) | !Flag) |> 
  select(A:C)
DF
#> # A tibble: 4 x 3
#> # Rowwise: 
#>       A     B     C
#>   <dbl> <dbl> <dbl>
#> 1     1     1     1
#> 2     1     3     1
#> 3     1     1     1
#> 4     1     1     2

^{Created on 2022-08-06 by the reprex package (v2.0.1)}

[Irakli]

Thank you so much for taking the time to go through this! Do you think I can somehow replace lagging step with diff() function?

[FJCC]

library(dplyr)

DF <- data.frame(A=c(1,1,1,1,1),B=c(1,3,1,1,1),C=c(1,1,1,1,2))
DF
#>   A B C
#> 1 1 1 1
#> 2 1 3 1
#> 3 1 1 1
#> 4 1 1 1
#> 5 1 1 2
DF <- DF |> mutate(across(.cols = everything(),
                    .fns = function(V) c(NA,diff(V)),
                    .names = "L_{.col}"))                                        
DF
#>   A B C L_A L_B L_C
#> 1 1 1 1  NA  NA  NA
#> 2 1 3 1   0   2   0
#> 3 1 1 1   0  -2   0
#> 4 1 1 1   0   0   0
#> 5 1 1 2   0   0   1
DF <- DF|> rowwise() |> 
  mutate(Flag = all(c_across(L_A:L_C)==0)) 
DF
#> # A tibble: 5 x 7
#> # Rowwise: 
#>       A     B     C   L_A   L_B   L_C Flag 
#>   <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <lgl>
#> 1     1     1     1    NA    NA    NA NA   
#> 2     1     3     1     0     2     0 FALSE
#> 3     1     1     1     0    -2     0 FALSE
#> 4     1     1     1     0     0     0 TRUE 
#> 5     1     1     2     0     0     1 FALSE
DF <- DF |> filter(is.na(Flag) | !Flag) |> 
  select(A:C)
DF
#> # A tibble: 4 x 3
#> # Rowwise: 
#>       A     B     C
#>   <dbl> <dbl> <dbl>
#> 1     1     1     1
#> 2     1     3     1
#> 3     1     1     1
#> 4     1     1     2

^{Created on 2022-08-07 by the reprex package (v2.0.1)}