Same operation done using: rowwise(), pmin(), pmap_dbl() via Mara Averick

Timing_rowwise_pmin_pmapdbl.md

library(tidyverse)
set.seed(406)
N <- 2000


library(rbenchmark)

benchmark(
  "rowwise"={
    df <- tibble(x=runif(N), y=runif(N), z=runif(N))
    df %>% rowwise() %>% mutate(m = min(c(x, y, z)))
    },
  "pmin"={
    df <- tibble(x=runif(N), y=runif(N), z=runif(N))
    df %>% mutate(m = pmin(x, y, z))
    },
  "pmap_dbl"={
    df <- tibble(x=runif(N), y=runif(N), z=runif(N))
    df %>% mutate(m = pmap_dbl(list(x, y, z), min))
    },
  replications=1000, 
  columns = 
    c("test", "replications", "elapsed", "relative", "user.self", "sys.self")
  )
#>       test replications elapsed relative user.self sys.self
#> 3 pmap_dbl         1000    8.11    7.651      7.92        0
#> 2     pmin         1000    1.06    1.000      1.03        0
#> 1  rowwise         1000    7.82    7.377      7.48        0

^{Created on 2020-02-26 by the reprex package (v0.3.0)}

Timing_rowwise_pmin_pmapdbl.R

library(tidyverse)
set.seed(406)
N <- 2000


library(rbenchmark)

benchmark(
  "rowwise"={
    df <- tibble(x=runif(N), y=runif(N), z=runif(N))
    df %>% rowwise() %>% mutate(m = min(c(x, y, z)))
    },
  "pmin"={
    df <- tibble(x=runif(N), y=runif(N), z=runif(N))
    df %>% mutate(m = pmin(x, y, z))
    },
  "pmap_dbl"={
    df <- tibble(x=runif(N), y=runif(N), z=runif(N))
    df %>% mutate(m = pmap_dbl(list(x, y, z), min))
    },
  replications=1000, 
  columns = 
    c("test", "replications", "elapsed", "relative", "user.self", "sys.self")
  )