R dplyr: Write list output to dataframe

Question

Suppose I have the following function

SlowFunction = function(vector){
  return(list(
    mean =mean(vector),
    sd  = sd(vector)
    ))
  }

And I would like to use dplyr:summarise to write the results to a dataframe:

iris %>% 
  dplyr::group_by(Species) %>% 
  dplyr::summarise(
    mean = SlowFunction(Sepal.Length)$mean,
    sd   = SlowFunction(Sepal.Length)$sd
    )

Does anyone have a suggestion how I can do this by calling "SlowFunction" once instead of twice? (In my code "SlowFunction" is a slow function that I have to call many times.) Without splitting "SlowFunction" in two parts of course. So actually I would like to somehow fill multiple columns of a dataframe in one statement.

Will this help stackoverflow.com/a/52413631/786542?

Tung
– Tung

2019-04-11 01:56:09 +00:00
Commented Apr 11, 2019 at 1:56 — Tung
– Tung, Commented Apr 11, 2019 at 1:56

Ronak Shah · Accepted Answer · 2019-04-11 02:27:32Z

4

Without changing your current SlowFunction one way is to use do

library(dplyr)

iris %>% 
   group_by(Species) %>% 
   do(data.frame(SlowFunction(.$Sepal.Length)))

#  Species     mean    sd
#  <fct>      <dbl> <dbl>
#1 setosa      5.01 0.352
#2 versicolor  5.94 0.516
#3 virginica   6.59 0.636

Or with group_split + purrr::map_dfr

bind_cols(Species = unique(iris$Species), iris %>%
     group_split(Species) %>%
     map_dfr(~SlowFunction(.$Sepal.Length)))

edited Apr 11, 2019 at 2:27

answered Apr 11, 2019 at 2:08

Ronak Shah

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Comments

Maurits Evers · Accepted Answer · 2019-04-11 02:07:50Z

3

An option is to use to store the output of SlowFunction in a list column of data.frames and then to use unnest

iris %>%
    group_by(Species) %>%
    summarise(res = list(as.data.frame(SlowFunction(Sepal.Length)))) %>%
    unnest()
## A tibble: 3 x 3
#  Species     mean    sd
#  <fct>      <dbl> <dbl>
#1 setosa      5.01 0.352
#2 versicolor  5.94 0.516
#3 virginica   6.59 0.636

answered Apr 11, 2019 at 2:07

Maurits Evers

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1 Comment

Frank Over a year ago

Thanks, I've compared the answers and this one is in my case by far the fastest. It is 2x faster than using "do" and 2.5x faster than using group_map!

www · Accepted Answer · 2019-04-11 02:18:06Z

3

We can use group_map if you are using dplyr 0.8.0 or later. The output from SlowFunction needs to be converted to a data frame.

library(dplyr)

iris %>% 
  group_by(Species) %>% 
  group_map(~SlowFunction(.x$Sepal.Length) %>% as.data.frame())
# # A tibble: 3 x 3
# # Groups:   Species [3]
#   Species     mean    sd
#   <fct>      <dbl> <dbl>
# 1 setosa      5.01 0.352
# 2 versicolor  5.94 0.516
# 3 virginica   6.59 0.636

answered Apr 11, 2019 at 2:18

www

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Comments

akrun · Accepted Answer · 2019-04-11 02:44:20Z

3

We can change the SlowFunction to return a tibble and

SlowFunction = function(vector){
  tibble(
     mean =mean(vector),
      sd  = sd(vector)
     )
   }

and then unnest the summarise output in a list

iris %>% 
    group_by(Species) %>% 
    summarise(out = list(SlowFunction(Sepal.Length))) %>%
    unnest
# A tibble: 3 x 3
#  Species     mean    sd
#  <fct>      <dbl> <dbl>
#1 setosa      5.01 0.352
#2 versicolor  5.94 0.516
#3 virginica   6.59 0.636

answered Apr 11, 2019 at 2:44

akrun

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