Hello,
Something like this?
First normalize the data.
Then a apply loop creates a logical matrix giving which numbers are in
the range -3 to 3.
If they are all TRUE then their sum by rows is equal to the number of
columns. This creates a logical index i.
Use that index i to subset the scaled data set.
# test data set, remove the Species column (not numeric)
df1 <- iris[-5]
df1_norm <- scale(df1)
i <- rowSums(apply(df1_norm, 2, \(x) x > -3 & x < 3)) == ncol(df1_norm)
# returns a matrix
df1_norm[i, ]
# returns a data.frame
as.data.frame(df1_norm[i,])
Hope this helps,
Rui Barradas
Às 09:23 de 09/05/2022, Paul Bernal escreveu:
Dear friends,
I have a dataframe which every single (i,j) entry (i standing for ith row,
j for jth column) has been normalized (converted to z-scores).
Now I want to filter or subset the dataframe so that I only end up with a a
dataframe containing only entries greater than -3 or less than 3.
How could I accomplish this?
Best,
Paul
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