frank by group is much slower than rank

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Assessment

Difficulty
4/5
Estimated time
3-5 days
Newbie friendliness
35/100
Issue type
Bug
Clarity
Mostly clear
Activity status
Stale
Tech stack
r
Domain
performance

Research direction

Start with the supplied grouped frank benchmark and compare it with rank(unclass(val), ties.method = "first") using the example data. Trace the frank entry point and grouped execution path, then verify that the completed change improves performance for many groups without changing ranking results.

Written by the indexing model from the issue text.

Description

GForce

I have asked a question at Stackoverflow to speed up a simple code involving calculating the ranks of dates by groups.

library(data.table)
library(lubridate)
library(microbenchmark)
set.seed(1)
NN <- 1000000
EE <- 10   
# Just an example.
todo <- data.table(id=paste0("ID",rep(1:NN, each=EE)), 
          val=dmy("1/1/1980") + sample(1:14000,NN*EE,replace=T))
# I want to benchmark this:
todo[,ord := frank(val, ties.method="first"), by=id]

https://stackoverflow.com/questions/58503115/how-to-compute-the-ranking-of-dates-by-groups-faster-with-data-table-and-lubri/58505724#58505724

and someone (sindri_baldur) has posted a simple alternative using rank(unclass(...)) that is almost 10 times faster.

todo[, rank(unclass(val), ties.method = "first"), by = id]

At the end it seems the slowness is not due to the fact of being a date but that frank takes a long time when calculated on many groups.

I'm using R 3.5.3 Open on Windows 10. I don't know about the other guy.
data.table 1.12.3

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