R: Convert several columns from [1,2] to Boolean [TRUE,FALSE] - r

Question

I have a data frame (imported with read.csv) which has many, but not all, columns which have boolean data which is encoded as 1=false, 2= true.
I would like to convert all of them to booleans. I know I can do
data$someCol <- data$someCol == 2
My questions:
Is this the best way?
Is there another in which I can specify BOTH "1" for FALSE and "2" for TRUE, with NA for the rest?
Can I somehow "mass-process" columns like this, selecting via grep?
Thanks!

Answer 1

You may convert the elements that are not 1 or 2 to NA and just use the logical condition df1==2 to transform it to a logical matrix with TRUE as 2, FALSE as 1, and the rest NA
is.na(df1) <- !(df1==1|df1==2)
df1==2
For large dataset, it may be better to use lapply to loop through the columns
df1[] <- lapply(df1, function(x) {x[!x %in% c(1,2)] <- NA
x==2})
Update
If we want to apply only a subset of columns with column names that start with 'XX', grep would be option to subset the columns and then loop with lapply on that subset of columns and replace that columns with the output of lapply.
indx <- grep('^XX', colnames(df2))
df2[indx] <- lapply(df2[indx], function(x) {x[!x %in% c(1,2)] <- NA
x==2})
Another option would be using mutate_each from dplyr
library(dplyr)
mutate_each(df2, funs((NA^!. %in% 1:2)*.==2), matches('^XX'))
We select the columns that have names that start with XX (matches('^XX')), create the logical condition within the funs. The . means any element within in a column.
. %in% 1:2
gives a logical output. If the element is 1 or 2, we get TRUE and if not FALSE.
(NA^!. %in% 1:2)
We negate (!) the output of TRUE/FALSE so that TRUE becomes FALSE and FALSE changes to TRUE, change the TRUE values to NA (NA^!...), thus converting values that are not 1 or 2 to NA and all other values to 1.
*.==2
Then we multiply * the values we got from the earlier output so that the NA value remain as NA and 1 value get changed to the value in that position, for e.g. 1*2=2. This can be made into a logical output by .==2. If the values are 2, will return as TRUE or else (i.e. 1) return FALSE.
Using mutate_each will not change the original object unless we assign to the original object name
df2 <- mutate_each(df2, funs((NA^!. %in% 1:2)*.==2), matches('^XX'))
Another option without the need to assign it back would be using %<>% operator from magrittr
library(magrittr)
df2 %<>%
mutate_each(funs((NA^!. %in% 1:2)*.==2), matches('^XX'))
data
set.seed(24)
df1 <- as.data.frame(matrix(sample(1:5, 20*5, replace=TRUE), ncol=5))
df2 <- df1
colnames(df2)[c(2,4)] <- paste0('XX', 1:2)