`[.data.table` is very slow with a single integer

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Assessment

Difficulty
4/5
Estimated time
3-5 days
Newbie friendliness
38/100
Issue type
Bug
Clarity
Mostly clear
Activity status
Stale
Tech stack
r
Domain
data

Research direction

Start with the reproducible R example in the issue and profile the [.data.table call used by set() on a single integer row. Compare its runtime with repair_sum2one; done means the reported bottleneck is reduced and the example demonstrates the improvement without changing its results.

Written by the indexing model from the issue text.

Description

performance

In this code:

library(data.table)
parameters <- list(types = c(p1 = "r", p2 = "r", p3 = "r", dummy = "c"),
                   digits = 4)
n <- 10000
newConfigurations <- data.table(p1 = runif(n), p2 = runif(n), p3 = runif(n),
                                dummy = sample(c("d1", "d2"), n, replace=TRUE))

repair_sum2one <- function(configuration, parameters)
{
  isreal <- names(which(parameters$types[colnames(configuration)] == "r"))
  digits <- parameters$digits[isreal]
  c_real <- unlist(configuration[isreal])
  c_real <- c_real / sum(c_real)
  c_real[-1] <- round(c_real[-1], digits[-1])
  c_real[1] <- 1 - sum(c_real[-1])
  configuration[isreal] <- c_real
  return(configuration)
}
j <- colnames(newConfigurations)
for (i in seq_len(nrow(newConfigurations)))
      set(newConfigurations, i, j = j, value = repair_sum2one(as.data.frame(newConfigurations[i]), parameters))

More than half the time is spent in [.data.table. Even the function repair_sum2one is faster.

Originally posted by @MLopez-Ibanez in https://github.com/Rdatatable/data.table/issues/3735#issuecomment-1546753937

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