How to efficiently apply multiple functions simultaneously to the same dataframe and save the results as a list of dataframes?

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I want to apply several different functions simultaneously to one dataframe, then put the results into a list of dataframes. So, for example, I could arrange by one column, then save the output as a new dataframe. Or I could filter some data, then save as another new dataframe (and so on). I feel like there must be an easy way to do this with purrr or apply, but am unsure how to proceed. So, I'm wondering if there is a way to give a list of functions, then return a list of dataframes. Here are some example functions that I apply to mtcars:

library(tidyverse)

filter_df <- function(x, word) {
  x %>% 
    tibble::rownames_to_column("ID") %>% 
    filter(str_detect(ID, word))
}
a <- filter_df(mtcars, "Merc")


mean_n_df <- function(x, grp, mean2) {
  x %>%
    group_by({{grp}}) %>%
    summarise(mean = mean({{mean2}}), n = n())
}
b <- mean_n_df(mtcars, grp = cyl, mean2 = wt)



rating <- function(x, a, b, c) {
  x %>% 
    rowwise %>% 
    mutate(rating = ({{a}}*2) + ({{b}}-5) * abs({{c}} - 30))
  
}
c <- rating(mtcars, a = cyl, b = drat, c = qsec)



pct <- function(data, var, round = 4){
  var_expr <- rlang::enquo(var)
  colnm_expr <- paste(rlang::get_expr(var_expr), "pct", sep = "_")
  
  data %>%
    mutate(!! colnm_expr := !!var_expr/sum(!!var_expr) %>%
             round(round))
}
d <- pct(mtcars, mpg)

I know that I could run the code above, then just bind each dataframe into a list.

df_list <- list(mtcars, a, b, c, d)

str(df_list, 1)[[1]]

List of 5
 $ :'data.frame':   32 obs. of  11 variables:
 $ :'data.frame':   7 obs. of  12 variables:
 $ : tibble [3 × 3] (S3: tbl_df/tbl/data.frame)
 $ : rowwise_df [32 × 12] (S3: rowwise_df/tbl_df/tbl/data.frame)
  ..- attr(*, "groups")= tibble [32 × 1] (S3: tbl_df/tbl/data.frame)
 $ :'data.frame':   32 obs. of  12 variables:
purrr apply r
2 Answers

This seems a bit bespoke (since each function requires different parameters), but I'd use Map (or purrr::map2 or purrr::pmap), passing a function and the args for it:

filter_df <- function(x, word) {
  x %>% 
    tibble::rownames_to_column("ID") %>% 
    filter(str_detect(ID, word))
}
mean_n_df <- function(x, grp, mean2) {
  x %>%
    group_by({{grp}}) %>%
    summarise(mean = mean({{mean2}}), n = n())
}
rating <- function(x, a, b, c) {
  x %>% 
    rowwise %>% 
    mutate(rating = ({{a}}*2) + ({{b}}-5) * abs({{c}} - 30))
}
pct <- function(data, var, round = 4){
  var_expr <- rlang::enquo(var)
  colnm_expr <- paste(rlang::get_expr(var_expr), "pct", sep = "_")
  data %>%
    mutate(!! colnm_expr := !!var_expr/sum(!!var_expr) %>%
             round(round))
}

The call:

out <- Map(
  function(fun, args) do.call(fun, c(list(mtcars), args)),
  list(filter_df, mean_n_df, rating, pct),
  list(list("Merc"), list(grp = quo(cyl), mean2 = quo(wt)),
       list(a = quo(cyl), b = quo(drat), c = quo(qsec)),
       list(quo(mpg)))
)

lapply(out, head, 3)
# [[1]]
#          ID  mpg cyl  disp  hp drat   wt qsec vs am gear carb
# 1 Merc 240D 24.4   4 146.7  62 3.69 3.19 20.0  1  0    4    2
# 2  Merc 230 22.8   4 140.8  95 3.92 3.15 22.9  1  0    4    2
# 3  Merc 280 19.2   6 167.6 123 3.92 3.44 18.3  1  0    4    4
# [[2]]
# # A tibble: 3 x 3
#     cyl  mean     n
#   <dbl> <dbl> <int>
# 1     4  2.29    11
# 2     6  3.12     7
# 3     8  4.00    14
# [[3]]
# # A tibble: 3 x 12
# # Rowwise: 
#     mpg   cyl  disp    hp  drat    wt  qsec    vs    am  gear  carb rating
#   <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>  <dbl>
# 1  21       6   160   110  3.9   2.62  16.5     0     1     4     4  -2.89
# 2  21       6   160   110  3.9   2.88  17.0     0     1     4     4  -2.28
# 3  22.8     4   108    93  3.85  2.32  18.6     1     1     4     1  -5.10
# [[4]]
#                mpg cyl disp  hp drat    wt  qsec vs am gear carb    mpg_pct
# Mazda RX4     21.0   6  160 110 3.90 2.620 16.46  0  1    4    4 0.03266449
# Mazda RX4 Wag 21.0   6  160 110 3.90 2.875 17.02  0  1    4    4 0.03266449
# Datsun 710    22.8   4  108  93 3.85 2.320 18.61  1  1    4    1 0.03546430

A few things:

  • Because you demonstrated using the unevaluated symbols (grp=cyl), we have to quote them first, otherwise they would be evaluated before reaching the functions.

  • You can general this out to arbitrary data by not hard-coding it in the Map anon-func, with:

    out <- Map(
  function(x, fun, args) do.call(fun, c(list(x), args)),
  list(mtcars),
  list(filter_df, mean_n_df, rating, pct),
  list(list("Merc"), list(grp = quo(cyl), mean2 = quo(wt)),
       list(a = quo(cyl), b = quo(drat), c = quo(qsec)),
       list(quo(mpg)))
)

    where the list(.) around mtcars is intentional: it appears as length-1 to Map, so it is recycled for the other args (length 4 each). Without list, Map would fail because the first function would see the first column (as a vector), second function second column (and/or warning with longer argument not a multiple of length of shorter ... I really wish mis-aligned recycling in R would fail harder than that).

    This generalization allows this sequence of functions to be applied each to multiple datasets:

    out2 <- lapply(list(mtcars[1:10,], mtcars[11:32,]), function(XYZ) {
  Map(
    function(x, fun, args) do.call(fun, c(list(x), args)),
    list(XYZ),
    list(filter_df, mean_n_df, rating, pct),
    list(list("Merc"), list(grp = quo(cyl), mean2 = quo(wt)),
         list(a = quo(cyl), b = quo(drat), c = quo(qsec)),
         list(quo(mpg)))
  )
})

    Not sure if you're intending the inception of applying a list of functions to a list of datasets ...

user_33582720

Using invoke with map2 from purrr

library(purrr)
df_list2 <- c(list(mtcars), map2(list(filter_df, mean_n_df, rating, pct), 
   list("Merc", expression(grp = cyl, mean2 = wt), 
       expression(a = cyl, b= drat, c = qsec), quote(mpg)), 
     ~ invoke(.x, c(list(mtcars), as.list(.y)))))

-checking

all.equal(df_list2, df_list, check.attributes = FALSE)
[1] TRUE
user_37322710
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