Make values not adjacent to each other NA - r

Question

The values >=10 in the data frame below (values 31,89,12,69) does sometimes come in order like 89 and 12. By that I mean de order 123456789, they are adjacent to eachother. I would like to make the values which are not adjacent to each other(31,69, in 31 nr 2 is missing in between to be in order, for 69, nr 7 and8 are missing to be in order) NA. How to code this? Imagine a big dataset! :)
id <- factor(rep(letters[1:2], each=5))
A <- c(1,2,NA,67,8,9,0,6,7,9)
B <- c(5,6,31,9,8,1,NA,9,7,4)
C <- c(2,3,5,NA,NA,2,7,6,4,6)
D <- c(6,5,89,3,2,9,NA,12,69,8)
df <- data.frame(id, A, B,C,D)
df
id A B C D
1 a 1 5 2 6
2 a 2 6 3 5
3 a NA 31 5 89
4 a 67 9 NA 3
5 a 8 8 NA 2
6 b 9 1 2 9
7 b 0 NA 7 NA
8 b 6 9 6 12
9 b 7 7 4 69
10 b 9 4 6 8
It should look like:
id A B C D
1 a 1 5 2 6
2 a 2 6 3 5
3 a NA NA 5 89
4 a 67 9 NA 3
5 a 8 8 NA 2
6 b 9 1 2 9
7 b 0 NA 7 NA
8 b 6 9 6 12
9 b 7 7 4 NA
10 b 9 4 6 8

Answer 1

Another solution defining a vector of the values to keep beforehand (only up to two-digit numbers, but could be extended):
numerals <- 1:9
vector <- 0:9
for (i in numerals) {
j <- numerals[i+1]
if (!is.na(j)) {
number <- as.numeric(paste(c(i, j), collapse = ""))
number_reverse <- as.numeric(paste(c(j, i), collapse = ""))
vector <- c(vector, number, number_reverse)
}
}
vector
[1] 0 1 2 3 4 5 6 7 8 9 12 21 23 32 34 43 45 54 56 65 67 76 78 87 89 98
Function to replace number if not in vector:
replace <- function(x) {
x <- ifelse(!x %in% vector, NA, x)
return(x)
}
Result:
df %>% mutate_at(c("A", "B", "C", "D"), replace)
id A B C D
1 a 1 5 2 6
2 a 2 6 3 5
3 a NA NA 5 89
4 a 67 9 NA 3
5 a 8 8 NA 2
6 b 9 1 2 9
7 b 0 NA 7 NA
8 b 6 9 6 12
9 b 7 7 4 NA
10 b 9 4 6 8

Answer 2

Here is a function that tests individual numbers
MyFunction <- function(A){
NumbersToCheck <- lapply(strsplit(as.character(A),""),as.integer)
check <- lapply(2:length(unlist(NumbersToCheck)), function(X) ifelse(NumbersToCheck[[1]][X]-NumbersToCheck[[1]][X-1]==1,TRUE,FALSE))
return(ifelse(FALSE %in% check,NA,A))
}
Which can then be applied to your entire df as follows
df[,2:ncol(df)] <- lapply(2:ncol(df), function(X) unlist(lapply(df[,X],MyFunction)))
to get the following result
> df
id A B C D
1 a 1 5 2 6
2 a 2 6 3 5
3 a NA NA 5 89
4 a 67 9 NA 3
5 a 8 8 NA 2
6 b 9 1 2 9
7 b 0 NA 7 NA
8 b 6 9 6 12
9 b 7 7 4 NA
10 b 9 4 6 8

Answer 3

df[] <- lapply(df, function(col) {
# Split each value character by character
NAs <- sapply(strsplit(as.character(col), split = ""), function(chars) {
# Convert them back to integer to compare with `diff`
# and verify the increment is always 1 or -1
diff <- diff(as.integer(chars))
!all(diff == 1) && !all(diff == -1)
})
# If not, replace those values with NA
col[NAs] <- NA
col
})
#> Warning in diff(as.integer(chars)): NAs introduced by coercion
#> Warning in diff(as.integer(chars)): NAs introduced by coercion
#> ...
#> Warning in diff(as.integer(chars)): NAs introduced by coercion
df
#> id A B C D
#> 1 a 1 5 2 6
#> 2 a 2 6 3 5
#> 3 a NA NA 5 89
#> 4 a 67 9 NA 3
#> 5 a 8 8 NA 2
#> 6 b 9 1 2 9
#> 7 b 0 NA 7 NA
#> 8 b 6 9 6 12
#> 9 b 7 7 4 NA
#> 10 b 9 4 6 8
Created on 2020-03-31 by the reprex package (v0.3.0)