Psych's reverse.code function producing NAs in R
Here is the relevant line of the source code of reverse.code()
, where new
is the object holding the reverse-coded data:
new[abs(new) > 999] <- NA
As you can see, setting values larger than 9999 to missing is hard-coded into the routine. You could write a new version of the function that didn't do that. For example, in the function below, we just make a much larger threshold:
my.reverse.code <- function (keys, items, mini = NULL, maxi = NULL)
{
if (is.vector(items)) {
nvar <- 1
}
else {
nvar <- dim(items)[2]
}
items <- as.matrix(items)
if (is.null(maxi)) {
colMax <- apply(items, 2, max, na.rm = TRUE)
}
else {
colMax <- maxi
}
if (is.null(mini)) {
colMin <- apply(items, 2, min, na.rm = TRUE)
}
else {
colMin <- mini
}
colAdj <- colMax + colMin
if (length(keys) < nvar) {
temp <- keys
if (is.character(temp))
temp <- match(temp, colnames(items))
keys <- rep(1, nvar)
keys[temp] <- -1
}
if (is.list(keys) | is.character(keys)) {
keys <- make.keys(items, keys)
keys <- diag(keys)
}
keys.d <- diag(keys, nvar, nvar)
items[is.na(items)] <- -99999999999
reversed <- items %*% keys.d
adj <- abs(keys * colAdj)
adj[keys > 0] <- 0
new <- t(adj + t(reversed))
new[abs(new) > 99999999999] <- NA
colnames(new) <- colnames(items)
colnames(new)[keys < 0] <- paste(colnames(new)[keys < 0],
"-", sep = "")
return(new)
}
The reason they used a numeric value threshold is that for the recoding they do to work, they needed all values to be numeric. So, they set missing values to -999 and then later turn them back into missing values. The same is done above, but with a lot bigger number.
keys <- c(1,-1,-1,-1) #Where column 1 = ID and the rest are my variables to be reversed
rev_dat <- data.frame(
id = 9998:10002,
x = 1:5,
y = 5:1,
z = 1:5
)
library(psych)
reverse.code(keys, rev_dat)
# id x- y- z-
# [1,] NA 5 1 5
# [2,] NA 4 2 4
# [3,] NA 3 3 3
# [4,] NA 2 4 2
# [5,] NA 1 5 1
my.reverse.code(keys, rev_dat)
# id x- y- z-
# [1,] 9998 5 1 5
# [2,] 9999 4 2 4
# [3,] 10000 3 3 3
# [4,] 10001 2 4 2
# [5,] 10002 1 5 1