summarise_all requested

[mattdowle]

Users can already use dplyr::summarise_all on .SD. But two people said it would still be nice to see built in to data.table :
https://twitter.com/Pertplus1/status/1151090599648550912
https://twitter.com/ftvalen/status/1151099177402818561
Next step would be to benchmark: https://twitter.com/MattDowle/status/1151959251029389312

[franknarf1]

Related (if I understand what summarise_all does): #782 and #1063 as colwise()

[ftvalentini]

Here goes an example using
A. dplyr with summarise_at
B. datatable with lapply
C. datatable with summarise_all

B seems to do the trick -- C is by far the slowest solution. An example:

library(magrittr)
library(purrr)
library(data.table)
library(dplyr)
library(tictoc)
library(devtools)

# nrow of data
n = 1e6
# number of columns by type
p_int = 100
p_double = 100
p_char = 30

# create columns
set.seed(100)
# grouping columns
id1 = sample(letters, n, rep=T)
id2 = sample(letters, n, rep=T)
# filter columns
f = sample(12, n, rep=T)
# integer columns
ints = matrix(sample(100L, p_int*n, rep=T), ncol=p_int) %>% as.data.table()
names(ints) = paste0("int",seq_along(ints))
# double columns
doubles = (matrix(sample(100, p_double*n, rep=T), ncol=p_double)/7) %>% as.data.table() 
names(doubles) = paste0("double",seq_along(doubles))
# character columns
chars = matrix(sample(letters, p_char*n, rep=T), ncol=p_char) %>% as.data.table() 
names(chars) = paste0("char",seq_along(chars))

# bind columns
dat = cbind(id1, id2, f, ints, doubles, chars) 

# columns to summarise
cols_sum = map_lgl(dat, is.numeric) %>% which() %>% names()


# summarise with mean,max,sum

tic.clearlog()
# A: dplyr
tic("A")
out_a = as_tibble(dat) %>% 
  dplyr::filter(f < 6) %>% 
  group_by(id1, id2) %>%
  summarise_at(cols_sum, list(max = max, sum = sum, mean = mean))
toc(log=T)

# B: datatable
tic("B")
func_sum = function(x) c(max = max(x), sum = sum(x), mean = mean(x))
out_b = dat[f < 6,
          by = .(id1,id2),
          as.list(unlist(lapply(.SD, func_sum))),
          .SDcols = cols_sum]
toc(log=T)

# C: datatable with summarise_all
tic("C")
out_c = dat[f < 6,
          by = .(id1,id2),
          summarise_all(.SD, list(max = max, sum = sum, mean = mean)),
          .SDcols = cols_sum]
toc(log=T)

# time
tic.log(format = TRUE)

Session info:

> devtools::session_info()
Session info ---------------------------------------------------
 setting  value                       
 version  R version 3.6.0 (2019-04-26)
 system   x86_64, mingw32             
 ui       RStudio (1.2.1335)          
 language (EN)                        
 collate  Spanish_Argentina.1252      
 tz       America/Buenos_Aires        
 date     2019-07-18                  

Packages -------------------------------------------------------
 package   * version date       source        
 base      * 3.6.0   2019-04-26 local         
 compiler    3.6.0   2019-04-26 local         
 datasets  * 3.6.0   2019-04-26 local         
 devtools  * 1.13.6  2018-06-27 CRAN (R 3.5.1)
 digest      0.6.18  2018-10-10 CRAN (R 3.5.1)
 graphics  * 3.6.0   2019-04-26 local         
 grDevices * 3.6.0   2019-04-26 local         
 memoise     1.1.0   2017-04-21 CRAN (R 3.5.1)
 methods   * 3.6.0   2019-04-26 local         
 packrat     0.4.9-3 2018-06-01 CRAN (R 3.5.1)
 stats     * 3.6.0   2019-04-26 local         
 tools       3.6.0   2019-04-26 local         
 utils     * 3.6.0   2019-04-26 local         
 withr       2.1.2   2018-03-15 CRAN (R 3.5.0)
> 

[mattdowle]

Thanks @ftvalentini - that's beyond my wildest dreams of reproducibility! Just one thing: can you add the timings that you see too please. That way, when we reproduce it in some weeks time, we know we get the same ballpark timings as you.

[ftvalentini]

running times:

> tic.log(format = TRUE)
[[1]]
[1] "A: 21.08 sec elapsed"

[[2]]
[1] "B: 5.25 sec elapsed"

[[3]]
[1] "C: 868.97 sec elapsed"

Note: I had to stop execution of C before it ended :/

[jangorecki]

dplyr seems to be deprecating summarise_all so not sure if we still want to have this... we could eventually use their API for same functionality, whatever it is now, but it is a little bit risky as it may eventually get deprecated as well.