Hi there!
For many different groups in by
, shift
is much slower than manual shifting.
See: http://stackoverflow.com/questions/35179911/shift-in-data-table-v1-9-6-is-slow-for-many-groups
and https://github.com/nachti/datatable_test/blob/master/leadtest.R for a detailed example.
Cheers,
Gerhard
That's not surprising. This'll go away when gforce
is optimised for :=
. It's on the list for this release, I believe.
+1 for this performance enhancement. shift()
is the main bottleneck in a lot of my code. Seems that for a fixed number of rows, the time it takes to run shift()
is proportional to the number of groups in the data.
library(data.table)
# Build table to store timings
timings <- CJ(RowCount = 10^7, Groups = 10^c(0:7))
timings[, SizePerGroup := RowCount/Groups]
# Loop through each experiment
for(i in 1:nrow(dt)){
print(paste0("Iteration: ", i))
# Build dataset
timings_i <- timings[i]
dt <- data.table(Grp = rep(seq_len(timings_i$Groups), each = timings_i$SizePerGroup))
dt[, Value := sample(100, size = .N, replace = T)]
# Measure the time it takes to insert a column indicating the previous value by group
elapsed <- system.time(dt[, PrevValueByGrp := shift(Value, type = "lag"), by = Grp])["elapsed"]
timings[i, Elapsed := elapsed]
}
library(ggplot2)
ggplot(timings, aes(x = Groups, y = Elapsed))+geom_line()+geom_point()
@ben519 Fyi, for the special case of when your code looks like that, there's a shortcut:
library(data.table)
dt <- data.table(Grp = rep(seq_len(1e6), each=10L))
dt[, Value := sample(100L, size = .N, replace = TRUE)]
system.time(dt[, PrevValueByGrp := shift(Value, type = "lag"), by = Grp][])
# user system elapsed
# 19.50 0.80 20.34
system.time(dt[, v := shift(Value, type = "lag")][rowid(Grp)==1L, v := NA][])
# user system elapsed
# 1.00 0.87 1.25
dt[, all.equal(v, PrevValueByGrp)]
# [1] TRUE
Most helpful comment
@ben519 Fyi, for the special case of when your code looks like that, there's a shortcut: