Extracing columns names with tidyselect: fastest implementation?

tidyselect_colnames_benchmark.md

library(dplyr)
#> 
#> Attaching package: 'dplyr'
#> The following objects are masked from 'package:stats':
#> 
#>     filter, lag
#> The following objects are masked from 'package:base':
#> 
#>     intersect, setdiff, setequal, union
library(tidyselect)

get_cols <- function(df, m){
  cols <- colnames(df)
  cols[starts_with(m,  vars = cols)]
  
}

get_cols2 <- function(df, m){
  pos <- eval_select(starts_with(m), df)
  colnames(df)[pos]
}

get_cols3 <- function(df, expr){
  pos <- eval_select(expr, df)
  colnames(df)[pos]
}

get_cols4 <- function(df, ...){
  pos <- eval_select(expr(c(...)), df)
  colnames(df)[pos]
}


get_cols_full <- function(df, ...){
  df %>% 
    select(...) %>% 
    colnames
}

get_cols(iris, "Sepal")
#> [1] "Sepal.Length" "Sepal.Width"
get_cols2(iris, "Sepal")
#> [1] "Sepal.Length" "Sepal.Width"
get_cols3(iris, starts_with("Sepal"))
#> [1] "Sepal.Length" "Sepal.Width"
get_cols3(iris, c(starts_with("Sepal"), contains("Species")))
#> [1] "Sepal.Length" "Sepal.Width"  "Species"
get_cols4(iris, starts_with("Sepal"))
#> [1] "Sepal.Length" "Sepal.Width"
get_cols_full(iris, starts_with("Sepal"))
#> [1] "Sepal.Length" "Sepal.Width"


microbenchmark::microbenchmark(a=get_cols(iris, "Sepal"),
                               a2=get_cols(iris, "Sepal"),
                               a3=get_cols3(iris, starts_with("Sepal")),
                               a4=get_cols4(iris, starts_with("Sepal")),
                               b=get_cols_full(iris, starts_with("Sepal")),
                               check="equal") %>% 
  summary() %>% 
  as.data.frame() %>% 
  knitr::kable()

expr	min	lq	mean	median	uq	max	neval	cld
a	37.546	47.6365	57.24979	53.0745	58.9625	220.526	100	a
a2	37.685	49.7940	56.36087	53.3735	59.7245	88.708	100	a
a3	284.418	295.4355	351.84057	311.8375	334.2105	1923.586	100	b
a4	844.777	880.8530	1019.41088	957.0495	1069.0475	2666.677	100	c
b	1052.824	1119.2700	1293.29644	1209.8315	1364.9575	2796.941	100	d

^{Created on 2020-11-22 by the reprex package (v0.3.0)}