Why is using dplyr pipe (%>%) slower than an equivalent non-pipe expression, for high-cardinality group-by?

Question

I thought that generally speaking using %>% wouldn\'t have a noticeable effect on speed. But in this case it runs 4x slower.

library(dplyr         


        

Answer 1

But here is something I have learnt today. I am using R 3.5.0.

Code with x = 100 (1e2)

library(microbenchmark)
library(dplyr)

set.seed(99)
x <- 1e2
z <- sample(x, x / 2, TRUE)
timings <- microbenchmark(
  dp = z %>% unique %>% list, 
  bs = list(unique(z)))

print(timings)

Unit: microseconds
 expr    min      lq      mean   median       uq     max neval
   dp 99.055 101.025 112.84144 102.7890 109.2165 312.359   100
   bs  6.590   7.653   9.94989   8.1625   8.9850  63.790   100

Although, if x = 1e6

Unit: milliseconds
 expr      min       lq     mean   median       uq      max neval
   dp 27.77045 31.78353 35.09774 33.89216 38.26898  52.8760   100
   bs 27.85490 31.70471 36.55641 34.75976 39.12192 138.7977   100