R - Running time by line of code in dplyr

Question

When I want to estimate the running time of a R code, I use the function system.time().

library(dplyr)

system.time({
    Titanic %>%
        as.data.frame() %>%
        mutate(Dataset = 1) %>%
        bind_rows(as.data.frame(Titanic)) %>%
        mutate_all(funs(replace_na(., NA))) %>% 
        filter(Dataset != 1)
})

# utilisateur     système      écoulé 
#        0.02        0.00        0.02

Question: Is there a way to know the running time of each operations, operations between each pipe (the mutate, then the bind_rows, then the filter, etc.) without running each one by one or without writing several system.time() ?

In this example it is not useful, but sometimes I received a long script, with a long running time, and I would like to identify which operations are the lowest.

I made some research but I didn't find something useful.

Answer 1

You might be interested into the %L>% pipe from my package pipes :

# devtools::install_github("moodymudskipper/pipes")
library(pipes)
Titanic %L>%
  as.data.frame() %L>%
  mutate(Dataset = 1) %L>%
  bind_rows(as.data.frame(Titanic)) %L>%
  mutate_all(list(~replace_na(., NA))) %L>% 
  filter(Dataset != 1)

# as.data.frame(.)   ~  0.03 sec
# mutate(., Dataset = 1)   ~  0 sec
# bind_rows(., as.data.frame(Titanic))   ~  0 sec
# mutate_all(., list(~replace_na(., NA)))   ~  0 sec
# filter(., Dataset != 1)   ~  0.03 sec
# [1] Class    Sex      Age      Survived Freq     Dataset 
# <0 rows> (or 0-length row.names)