writing a function to work with dplyr::mutate()

If you are willing to entertain another approach, have a look at ?cut. By
defining the 'breaks' argument appropriately, you can easily create a
factor that tells you which values should be looked up and which accepted
as is. If I understand correctly, this seems to be what you want. If I have
not, just ignore and wait for a more useful reply.

Cheers,
Bert

Bert Gunter

"The trouble with having an open mind is that people keep coming along and
sticking things into it."
-- Opus (aka Berkeley Breathed in his "Bloom County" comic strip )


On Tue, Jan 19, 2021 at 10:24 AM Steven Rigatti <sjrigatti at gmail.com>
wrote:

I am having some problems with what seems like a pretty simple issue. I
have some data where I want to convert numbers. Specifically, this is
cancer data and the size of tumors is encoded using millimeter
measurements. However, if the actual measurement is not available the
coding may imply a less specific range of sizes. For instance numbers 0-89
may indicate size in mm, but 90 indicates "greater than 90 mm" , 91
indicates "1 to 2 cm", etc. So, I want to translate 91 to 90, 92 to 15,
etc.

I have many such tables so I would like to be able to write a function
which takes as input a threshold over which new values need to be looked
up, and the new lookup table, returning the new values.

I successfully wrote the function:

translate_seer_numeric <- function(var, upper, lookup) {
    names(lookup) <- c('old','new')
    names(var) <- 'old'
    var <- as.data.frame(var)
    lookup2 <- data.frame(old = c(1:upper),
                          new = c(1:upper))
    lookup3 <- rbind(lookup, lookup2)
 print(var)
    res <- left_join(var, lookup3, by = 'old') %>%
         select(new)

    res

}

test1 <- data.frame(old = c(99,95,93, 8))lup <- data.frame(bif = c(93,
95, 99),
                  new = c(3, 5, NA))
translate_seer_numeric(test1, 90, lup)

The above test generates the desired output:

  old1  992  953  934   8
  new1  NA2   53   34   8

My problem comes when I try to put this in line with pipes and the mutate
function:

test1 %>%
     mutate(varb = translate_seer_numeric(var = old, 90, lup))####
 Error: Problem with `mutate()` input `varb`.
x Join columns must be present in data.
x Problem with `old`.
i Input `varb` is `translate_seer_numeric(var = test1$old, 90, lup)`.

Thoughts??

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