How to extract several values from a function in a dplyr pipeline-CodePudding

is there a good way to create a dplyr pipeline with mutate extracting several columns from a function in a single step? For example, imagine that you have a dataframe like this:

and you have a function that returns both the sum and the product:

sum_and_product <- function(x, y) list(sum=x y,product=x*y)

so how does one make a pipeline producing the original dataframe enriched by sum and product columns calculated with a single call? Something like:

df %>% mutate_multiple(c(sum, product)=sum_and_product(x, y))

x y sum product
1 5 6   5
2 3 5   6
6 4 10  24

If this can't be done with a dplyr pipeline, what other alternatives are there?

To get you a better idea of what I'm trying to achieve in my actual real-life use case: I need to calculate structural change points for multiple time series stored in a single data frame. When I only calculate the moment in time when the break occurs, I can do it quite simply and efficiently:

df %>% group_by(timeseries_id) %>% mutate(cpt = my.cpt(time, value))

But the problem is, cpt must return 3 values instead of just one (the time of the change, the value before and the value after), and that breaks everything. When I do it with a loop, it's terribly slow (and also ugly). I guess I could write 3 functions, one per each value to extract, but obviously that's not ideal.

Any suggestions would be appreciated.

Best regards, Nikolai

CodePudding user response：

Change your function from list to data.frame and it will work, i.e.

library(dplyr)
sum_and_product <- function(x, y) data.frame(sum=x y,product=x*y)

df %>% 
 mutate(sum_and_product(x, y))
#  x y     sum     product
#1 1 5       6           5
#2 2 3       5           6
#3 6 4      10          24

CodePudding user response：

You can save the output from sum_and_product as a list and then use unnest_wider to get different columns from them.

library(dplyr)
library(tidyr)

sum_and_product <- function(x, y) list(sum=x y,product=x*y)

df %>%
  rowwise() %>%
  mutate(z = list(sum_and_product(x, y))) %>%
  unnest_wider(z)

#      x     y   sum product
#  <int> <int> <int>   <int>
#1     1     5     6       5
#2     2     3     5       6
#3     6     4    10      24