Reading function input values defined in `…` from an CSV file in R

Question

Suppose I have an R function like foo below. This function has 4 fixed arguments, and any number of arbitrary arguments defined in ... .

All input values for foo arguments are stored in THIS CSV file.

In my code below, I can successfully run foo using the 4 fixed arguments imported from the CSV file in a lapply loop. BUT I'm wondering how I can insert the arguments defined in ... in the lapply command?

foo <- function(n = NULL, r = NULL, post, control, ...){ ## the function

data.frame(n = n, r = r, post, control, ...)

}

D <- read.csv("https://raw.githubusercontent.com/izeh/i/master/j.csv", h = T) # CSV file
L <- split(D, D$study.name) ; L[[1]] <- NULL

# the fixed args values:
      n <- lapply(1:length(L), function(i) L[[i]]$n)
      r <- lapply(1:length(L), function(i) L[[i]]$r)
   post <- lapply(1:length(L), function(i) L[[i]]$post)
control <- lapply(1:length(L), function(i) L[[i]]$control)

# names of args defined in `...`:
dot.names <- names(L[[1]])[!names(L[[1]]) %in% formalArgs(foo)][-1]

# the `...` args values:
a <- lapply(dot.names, function(i) lapply(L, function(j) j[grep(i, names(j))]))

## RUN `foo` function:
lapply(1:length(L), function(i) foo(n = n[[i]], r = r[[i]], post = post[[i]], 
                                     control = control[[i]])) # BUT! how can I insert the 
                                                              # arguments defined in `...` 
                                                              # in the function?

Answer 1

We can also use Map with do.call . We can extract the arguments to the foo in a single call to lapply by extracting the columns 'n', 'r', 'post', control' and the extra columns ( ... ) based on output of 'dot.names', then transpose (from purrr - or use the same approach as mentioned here ) and pass it on Map

args <- lapply(L, function(x) unclass(x[c("n", "r", "post", "control", dot.names)]))
library(purrr)
unname(do.call(Map, c(f = foo, transpose(args))))
#[[1]]
#   n   r post control ESL prof scope type
#1 13 0.5    1   FALSE   1    2     0    1
#2 13 0.5    2   FALSE   1    2     0    1
#3 15 0.5    1   FALSE   1    2     0    1
#4 15 0.5    2   FALSE   1    2     0    1
#5 16 0.5    1    TRUE   1    2     0    1
#6 16 0.5    2    TRUE   1    2     0    1

#[[2]]
#   n   r post control ESL prof scope type
#1 13 0.5    1   FALSE   0    1     1    0
#2 13 0.5    2   FALSE   0    1     1    0
#3 15 0.5    1   FALSE   0    1     1    0
#4 15 0.5    2   FALSE   0    1     1    0
#5 16 0.5    1    TRUE   0    1     1    0
#6 16 0.5    2    TRUE   0    1     1    0

#[[3]]
#   n   r post control ESL prof scope type
#1 13 0.5    1   FALSE   1    3     0    1
#2 13 0.5    2   FALSE   1    3     0    1
#3 13 0.5    3   FALSE   1    3     0    1
#4 15 0.5    1   FALSE   1    3     0    1
#5 15 0.5    2   FALSE   1    3     0    1
#6 15 0.5    3   FALSE   1    3     0    1
#7 16 0.5    1    TRUE   1    3     0    1
#8 16 0.5    2    TRUE   1    3     0    1
#9 16 0.5    3    TRUE   1    3     0    1

The OP mentioned about replacing the transpose with a base R option

m1 <- simplify2array(lapply(names(args[[1]]), function(nm) 
     lapply(args, function(l1) l1[nm])))
do.call(Map, c(f = foo, unname(split(m1, col(m1)))))

---

Of if we can use tidyverse

library(tidyverse)
map(L, ~ 
       .x %>%
           select(n, r, post, control, dot.names) %>% 
           as.list) %>% 
    transpose %>% 
    pmap(., foo)
#$Ellis.sh1
#   n   r post control ESL prof scope type
#1 13 0.5    1   FALSE   1    2     0    1
#2 13 0.5    2   FALSE   1    2     0    1
#3 15 0.5    1   FALSE   1    2     0    1
#4 15 0.5    2   FALSE   1    2     0    1
#5 16 0.5    1    TRUE   1    2     0    1
#6 16 0.5    2    TRUE   1    2     0    1

#$Goey1
#   n   r post control ESL prof scope type
#1 13 0.5    1   FALSE   0    1     1    0
#2 13 0.5    2   FALSE   0    1     1    0
#3 15 0.5    1   FALSE   0    1     1    0
#4 15 0.5    2   FALSE   0    1     1    0
#5 16 0.5    1    TRUE   0    1     1    0
#6 16 0.5    2    TRUE   0    1     1    0

#$kabla
#   n   r post control ESL prof scope type
#1 13 0.5    1   FALSE   1    3     0    1
#2 13 0.5    2   FALSE   1    3     0    1
#3 13 0.5    3   FALSE   1    3     0    1
#4 15 0.5    1   FALSE   1    3     0    1
#5 15 0.5    2   FALSE   1    3     0    1
#6 15 0.5    3   FALSE   1    3     0    1
#7 16 0.5    1    TRUE   1    3     0    1
#8 16 0.5    2    TRUE   1    3     0    1
#9 16 0.5    3    TRUE   1    3     0    1

Update

Based on the example showed here , the structure is slightly different, so we can transpose the list with names (for base R )

argsT <- setNames(lapply(names(args[[1]]), 
      function(nm) lapply(args, `[[`, nm)), names(args[[1]]))


out1 <- unname(do.call(Map, c(f = d.prepos, argsT)))
out2 <- unname(do.call(Map, c(f = d.prepos, purrr::transpose(args))))
identical(out1, out2)
#[1] TRUE

Answer 2

Use mapply for this type of problem.
In the code below I have changed the way you define n , r , post and control .

n <- lapply(L, `[[`, 'n')
r <- lapply(L, `[[`, 'r')
post <- lapply(L, `[[`, 'post')
control <- lapply(L, `[[`, 'control')

The only difference is that these results have their names attribute set.

Then also change the way the list of lists a is created. Swap the two cycles.

a <- lapply(L, function(i) lapply(dot.names, function(k) i[grep(k, names(i))]))

Now the solution to the problem. It is mandatory to set SIMPLIFY = FALSE , the default TRUE gives a really bad output.

mapply(FUN = foo, n, r, post, control, a, SIMPLIFY = FALSE)