reprex::reprex_info()
#> Created by the reprex package v0.1.1.9000 on 2017-10-16

library(dplyr, warn.conflicts = FALSE)

replace_cols1 <- function(df, cols, pos) {
  cbind(
    df[, 1:(pos-1), drop=FALSE],
    cols,
    df[, (pos+1):ncol(df), drop=FALSE]
  )
}

replace_cols1_2 <- function(df, cols, pos) {
  bind_cols(
    df[, 1:(pos-1), drop=FALSE],
    cols,
    df[, (pos+1):ncol(df), drop=FALSE]
  )
}

replace_cols2 <- function(df, cols, pos) {
  data.frame(append(df, cols, pos))[, -pos]
}

replace_cols2_2 <- function(df, cols, pos) {
  as.data.frame(append(df, cols, pos))[, -pos]
}

replace_cols3 <- function(df, cols, pos) {
  col_nm <- append(colnames(df)[-pos], names(cols), pos-1L)
  cbind(df, cols)[,col_nm]
}

replace_cols3_2 <- function(df, cols, pos) {
  col_nm <- append(colnames(df)[-pos], names(cols), pos-1L)
  bind_cols(df, cols)[,col_nm]
}


df_part <- dplyr::select(iris, SL = Sepal.Length, PL = Petal.Length)
microbenchmark::microbenchmark(
  replace_cols1(iris, df_part, 2L),
  replace_cols1_2(iris, df_part, 2L),
  replace_cols2(iris, df_part, 2L),
  replace_cols2_2(iris, df_part, 2L),
  replace_cols3(iris, df_part, 2L),
  replace_cols3_2(iris, df_part, 2L)
)
#> Unit: microseconds
#>                                expr     min       lq      mean   median
#>    replace_cols1(iris, df_part, 2L) 102.321 125.4325  170.2560 139.8520
#>  replace_cols1_2(iris, df_part, 2L) 414.815 475.2600  662.5270 556.4455
#>    replace_cols2(iris, df_part, 2L) 567.310 610.7660 1024.4829 692.9385
#>  replace_cols2_2(iris, df_part, 2L) 525.827 586.2725  855.6373 655.0135
#>    replace_cols3(iris, df_part, 2L)  74.667  99.3580  157.4599 112.5930
#>  replace_cols3_2(iris, df_part, 2L) 354.371 399.0125  614.9774 507.2600
#>        uq      max neval
#>  168.0985 1004.247   100
#>  741.1365 2518.916   100
#>  953.4825 7756.254   100
#>  991.2110 3482.472   100
#>  161.5800 1084.840   100
#>  632.6920 2453.730   100

big_iris <- iris[rep(seq_len(nrow(iris)), 100L),]
big_df_part <- dplyr::select(big_iris, SL = Sepal.Length, PL = Petal.Length)
microbenchmark::microbenchmark(
  replace_cols1(big_iris, big_df_part, 2L),
  replace_cols1_2(big_iris, big_df_part, 2L),
  replace_cols2(big_iris, big_df_part, 2L),
  replace_cols2_2(big_iris, big_df_part, 2L),
  replace_cols3(big_iris, big_df_part, 2L),
  replace_cols3_2(big_iris, big_df_part, 2L)
)
#> Unit: microseconds
#>                                        expr     min       lq      mean
#>    replace_cols1(big_iris, big_df_part, 2L) 780.643 956.0500 2302.5244
#>  replace_cols1_2(big_iris, big_df_part, 2L) 423.902 538.0750 1195.2366
#>    replace_cols2(big_iris, big_df_part, 2L) 578.371 676.7410 1569.6369
#>  replace_cols2_2(big_iris, big_df_part, 2L) 519.112 610.9630 1405.0698
#>    replace_cols3(big_iris, big_df_part, 2L) 726.915 863.4080 1946.8223
#>  replace_cols3_2(big_iris, big_df_part, 2L) 361.482 490.6675  933.9741
#>     median        uq      max neval
#>  1348.7415 2145.7795 13209.69   100
#>   679.9020 1038.8155 28910.24   100
#>   778.6675 1154.3715 34127.83   100
#>   761.2850 1179.6555 16451.57   100
#>  1125.3345 1932.4460 16254.43   100
#>   611.7535  900.5435 10620.06   100