Simple comparison of performance of lookups of environments vs lists in R

lookup_list_vs_env.R

# lookup_list_vs_env.R
# comparing the performance of lookups with lists as opposed to 
# environments in R. As the size of the collection increases, the better
# performance of the environments for lookups is multiples better.
library(microbenchmark)
library(purrr)
library(stringr)

e1 <- new.env()
l1 <- list()

walk(1:200, ~{
  keytxt <- str_c("key",.x)
  valtxt <- str_c("#DD25",.x)
  e1[[keytxt]] <- valtxt
  l1[keytxt] <<- valtxt
})


x <- sample(names(e1),100,TRUE)

microbenchmark(
  walk(x,~e1[[.x]]),
  walk(x,~l1[[.x]])
)
# e.g.
# Unit: microseconds
#               expr     min       lq    mean  median       uq      max neval
# walk(x, ~e1[[.x]]) 152.400 155.2515 168.497 157.051 159.4015  868.102   100
# walk(x, ~l1[[.x]]) 226.002 229.4515 247.902 231.701 235.3010 1284.001   100

walk(1:1000, ~{
  keytxt <- str_c("key",.x)
  valtxt <- str_c("#DD25",.x)
  e1[[keytxt]] <- valtxt
  l1[keytxt] <<- valtxt
})

x <- sample(names(e1),100,TRUE)

microbenchmark(
  walk(x,~e1[[.x]]),
  walk(x,~l1[[.x]]),
  times=1000
)
# e.g.
# Unit: microseconds
#                expr     min      lq    mean   median       uq      max neval
#  walk(x, ~e1[[.x]]) 154.001 163.151 204.752 170.1015 187.3015 8936.901  1000
#  walk(x, ~l1[[.x]]) 540.401 553.401 599.476 561.7005 583.7010 4437.400  1000