重塑既宽又长（稀疏）的R data.table

Question

I have a data.table that is both wide and long, and also sparse. This is the simplest example:

Row Val1 Val2
1   1
2        1

Reshaping from wide to long yields:

Row Idx Val
1   1   1
1   2
2   1
2   2   1

Reshaping from long (index is implicit based on non-missing rows, in this case row numbers) to wide yields:

Row Val1.1 Val2.1 Val1.2 Val2.2
1   1                    1

What I want is:

Row Idx Val
1   1   1
2   2   1

Missing values are structurally missing and should be discarded.

The data set is very complex (400+ columns); it is from a survey in which one question was replicated in six different ways for six different cases, with answers selectively filled based on the case. Each question has six binary answers, making 36 columns. These need to be collapsed into the eight columns representing the eight unique binary answers, plus a new column identifying the case.

There are several other questions with similar issues so I need to find an algorithm to do this, and I don't have the vocabulary to explain it to Google. What is the right way to do this?

Answer 1

Try

setDT(df1)[,list(Idx=which.max(.SD), Val=1) , Row]
#   Row Idx Val
#1:   1   1   1
#2:   2   2   1