Creating multiple functions with dplyr non-standard evaluation - r

Question

I am trying to use mutate_() to create multiple columns where each is based on a custom function called with different inputs. I can use paste() to create multiple quoted function calls, but this doesn't work because dplyr's NSE requires formulas (~) rather than quoted strings to be able to find the function. How can I write the "dots = " line below so that the function can be found? I tried experimenting with ~, as.formula(), and lazyeval::interp(), but couldn't get any to work. My actual "prefixes" is a long vector so I don't want to separately write out the function calls for each new column. Thanks
library(dplyr)
library(lazyeval)
library(nycflights13)
myfunc = function(x, y) { x - y }
# this works
flights1 <- mutate(flights, dep_time_sched = myfunc(dep_time, dep_delay),
arr_time_sched = myfunc(arr_time, arr_delay))
# this doesn't - Error: could not find function "myfunc"
prefixes <- c('dep', 'arr')
dots = as.list(paste0('myfunc(',
paste0(prefixes, '_time'), ', ',
paste0(prefixes, '_delay)')))
flights2 <- mutate_(flights, .dots = setNames(dots, paste0(prefixes, '_time_sched')))

Answer 1

You could approach this by using interp with lapply to loop through your prefixes and get a list in the desired format for mutate_.
dots = lapply(prefixes, function(var) interp(~myfunc(x, y),
.values = list(x = as.name(paste0(var, "_time")),
y = as.name(paste0(var, "_delay")))))
dots
[[1]]
~myfunc(dep_time, dep_delay)
<environment: 0x0000000019e51f00>
[[2]]
~myfunc(arr_time, arr_delay)
<environment: 0x0000000019f1e5b0>
This gives the same results as your flights1.
flights2 = mutate_(flights, .dots = setNames(dots, paste0(prefixes, '_time_sched')))
identical(flights1, flights2)
[1] TRUE

Answer 2

My actual "prefixes" is a long vector so I don't want to separately write out the function calls for each new column.
If that's the case, you should really transform your data to long format. To clarify what I mean, let's look at a smaller example:
mydat <- flights[1:5, c(paste0(prefixes,"_time"), paste0(prefixes,"_delay"))]
# dep_time arr_time dep_delay arr_delay
# (int) (int) (dbl) (dbl)
# 1 517 830 2 11
# 2 533 850 4 20
# 3 542 923 2 33
# 4 544 1004 -1 -18
# 5 554 812 -6 -25
library(data.table)
longdat <- setDT(mydat)[, .(
pref = rep(prefixes, each=.N),
time = unlist(mget(paste0(prefixes,"_time"))),
delay = unlist(mget(paste0(prefixes,"_delay")))
)]
longdat[, time_sched := myfunc(time, delay) ]
# pref time delay time_sched
# 1: dep_ 517 2 515
# 2: dep_ 533 4 529
# 3: dep_ 542 2 540
# 4: dep_ 544 -1 545
# 5: dep_ 554 -6 560
# 6: arr_ 830 11 819
# 7: arr_ 850 20 830
# 8: arr_ 923 33 890
# 9: arr_ 1004 -18 1022
# 10: arr_ 812 -25 837
Besides being simpler, calling the function a single time takes advantage of its vectorization.
While I used data.table to construct longdat, I'm sure there's a tool to do the same thing in the tidyr package (companion to dplyr). Similarly, the addition of the time_sched column is just a mutate.
Alternative ways of reshaping Thanks to #akrun, here is another way to get to longdat, using melt function syntax that will be available in the next version of data.table (1.9.8, not released yet):
longdat <- melt(mydat,
measure = patterns('time$','delay$'),
variable.name = "pref",
value.name = c('time', 'delay')
)[, pref := prefixes[pref]]
or, also thanks to #akrun, here is a way to reshape that automatically constructs the prefixes, given the suffixes (time and delay), using #AnandaMahto's splitstackshape package:
library(splitstackshape)
longdat <- merged.stack(transform(mydat, ind=1:nrow(mydat)),
var.stubs = c('_time', '_delay'),
sep = 'var.stubs',
atStart = FALSE)