I have some data.frames with different number of columns. For each such data.frame, I need to delete all rows with non-missing values in columns 2:ncol(data.frame)-1, which means all the columns except for the first and last columns. How could I do that?
tidyverse? You can put the columns to exclude in the brackets.
df <- df %>% drop_na(-first_column_name, -last_column_name)
Could you please ask your questions with a minimal REPRoducible EXample (reprex)?
I know this question is not that complicated but It could bring speculation and unnecessary back and forths
Thanks. My task is computationally intensive, do we have a data.table version of drop_na?
If you need to subset a big dataframe it's often fast to do it like this:
keeps <- rowSums(is.na(df)) == 0 # identify rows to keep df <- df[keeps, ] # keep them
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