Column-wise subset of data frame in R - r

Question

I need some help with subset/filter of data.frame. Below is the code for my random dataset.
A <- c(1,1,1,1,2,2,2,2,3,3,3,3,4,4,4,4)
B <- c(3,3,3,3,4,4,4,4,1,1,1,1,2,2,2,2)
C <- c(1,1,1,1,3,3,3,3,2,2,2,2,4,4,4,4)
Fakey <- data.frame(A, B, C)
Filter_Fakey <- subset(Fakey, (Fakey>1 & Fakey<4))
That last line of coode results in the following:
> Filter_Fakey
A B C
5 2 4 3
6 2 4 3
7 2 4 3
8 2 4 3
9 3 1 2
10 3 1 2
11 3 1 2
12 3 1 2
NA NA NA NA
NA.1 NA NA NA
NA.2 NA NA NA
NA.3 NA NA NA
NA.4 NA NA NA
NA.5 NA NA NA
NA.6 NA NA NA
NA.7 NA NA NA
NA.8 NA NA NA
NA.9 NA NA NA
NA.10 NA NA NA
NA.11 NA NA NA
NA.12 NA NA NA
NA.13 NA NA NA
NA.14 NA NA NA
NA.15 NA NA NA
But What I really want is this,
> Filter_Fakey
A B C
5 2 3 3
6 2 3 3
7 2 3 3
8 2 3 3
9 3 2 2
10 3 2 2
11 3 2 2
12 3 2 2
NA NA NA NA
NA.1 NA NA NA
NA.2 NA NA NA
NA.3 NA NA NA
NA.4 NA NA NA
NA.5 NA NA NA
NA.6 NA NA NA
NA.7 NA NA NA
NA.8 NA NA NA
NA.9 NA NA NA
NA.10 NA NA NA
NA.11 NA NA NA
NA.12 NA NA NA
NA.13 NA NA NA
NA.14 NA NA NA
NA.15 NA NA NA
I've tried subset(), subset(with a negation condition), filter{dplyr}, and the different bracket notations ('[' and '[['). Thanks for helping me out.

Answer 1

Use lapply to loop through columns of the data frame, and set values out of conditions to be NA if that is what you are after. Use order(is.na(...)) to arrange NA values to the last positions:
do.call(cbind, lapply(Fakey, function(col) {
col[col <= 1 | col >= 4] <- NA; col[order(is.na(col))]
}))
A B C
1 2 3 3
2 2 3 3
3 2 3 3
4 2 3 3
5 3 2 2
6 3 2 2
7 3 2 2
8 3 2 2
9 NA NA NA
10 NA NA NA
11 NA NA NA
12 NA NA NA
13 NA NA NA
14 NA NA NA
15 NA NA NA
16 NA NA NA

Answer 2

Another option is using length<- to pad NA at the end after subsetting each of the columns using the logical condition.
data.frame(lapply(Fakey, function(x) `length<-`(x[x > 1 & x <4], nrow(Fakey))))
# A B C
#1 2 3 3
#2 2 3 3
#3 2 3 3
#4 2 3 3
#5 3 2 2
#6 3 2 2
#7 3 2 2
#8 3 2 2
#9 NA NA NA
#10 NA NA NA
#11 NA NA NA
#12 NA NA NA
#13 NA NA NA
#14 NA NA NA
#15 NA NA NA
#16 NA NA NA