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Asked: 2026-06-14T17:19:16+00:00

I have a function call WOE, that return a data fame with 1 row,

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I have a function call WOE, that return a data fame with 1 row, and 7 columns:

WOE(inData, splitCol, range, tgtCol, adjfac)

Where inData is a data frame, splitCol, tgtCol and adjfac are numeric, range can be a single number or a 2×1 matrix,
e.g. range = 10 or range = c(10, 20)

Now I would like to write a function that when the range is a n-row matrix, then the function will do WOE row by row and return a data frame that have n row and 7 columns.
For now i am using a for loop with cbind:

df <- rbind(df, WOE(inData, splitCol, range[i,], tgtCol, adjfac))
for (i in 2:nrow(range)) {
  df <- rbind(df, WOE(inData, splitCol, range[i,], tgtCol, adjfac))
}

But I don’t like for-loop… I want to make it simpler.
So I also have tried to use mapply like this:

 mapply(t(WOR, list(inData), list(splitCol), split(range, nrow(range)), list(tgtCol), list(adjfac))

but the above line doesn’t return a data frame as I want, it return a data frame of a lot of lists, which is very difficult for me to do further calculation.

Does anyone have suggestions for me to aggregate my for-loop into less lines?
Thanks!

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1 Answer

  1. Editorial Team
    do.call(rbind, lapply(1:nrow(range), function(i) WOE(inData, splitCol, range[i,], tgtCol, adjfac)))

    I often use this idiom when I want precise control over how results from an “apply” operation are combined. This way I don’t have to remember what the rules for automatic simplification are (i.e. sapply or mapply with simplify=TRUE).

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