Is there a vectorized parallel max() and min()?

Sounds like you're looking for pmax and pmin ("parallel" max/min):

Extremes                 package:base                  R DocumentationMaxima and MinimaDescription:     Returns the (parallel) maxima and minima of the input values.Usage:     max(..., na.rm = FALSE)     min(..., na.rm = FALSE)     pmax(..., na.rm = FALSE)     pmin(..., na.rm = FALSE)     pmax.int(..., na.rm = FALSE)     pmin.int(..., na.rm = FALSE)Arguments:     ...: numeric or character arguments (see Note).   na.rm: a logical indicating whether missing values should be          removed.Details:     ‘pmax’ and ‘pmin’ take one or more vectors (or matrices) as     arguments and return a single vector giving the ‘parallel’ maxima     (or minima) of the vectors.  The first element of the result is     the maximum (minimum) of the first elements of all the arguments,     the second element of the result is the maximum (minimum) of the     second elements of all the arguments and so on.  Shorter inputs     are recycled if necessary.  ‘attributes’ (such as ‘names’ or     ‘dim’) are transferred from the first argument (if applicable).

Here's a version I implemented using Rcpp. I compared pmin with my version, and my version is roughly 3 times faster.

library(Rcpp)cppFunction("  NumericVector min_vec(NumericVector vec1, NumericVector vec2) {    int n = vec1.size();    if(n != vec2.size()) return 0;    else {      NumericVector out(n);      for(int i = 0; i < n; i++) {        out[i] = std::min(vec1[i], vec2[i]);      }      return out;    }  }")x1 <- rnorm(100000)y1 <- rnorm(100000)microbenchmark::microbenchmark(min_vec(x1, y1))microbenchmark::microbenchmark(pmin(x1, y1))x2 <- rnorm(500000)y2 <- rnorm(500000)microbenchmark::microbenchmark(min_vec(x2, y2))microbenchmark::microbenchmark(pmin(x2, y2))

The microbenchmark function output for 100,000 elements is:

> microbenchmark::microbenchmark(min_vec(x1, y1))Unit: microseconds            expr     min       lq     mean  median       uq min_vec(x1, y1) 215.731 222.3705 230.7018 224.484 228.1115     max neval 284.631   100> microbenchmark::microbenchmark(pmin(x1, y1))Unit: microseconds         expr     min       lq     mean  median      uq      max pmin(x1, y1) 891.486 904.7365 943.5884 922.899 954.873 1098.259 neval   100

And for 500,000 elements:

> microbenchmark::microbenchmark(min_vec(x2, y2))Unit: milliseconds            expr      min       lq     mean   median       uq min_vec(x2, y2) 1.493136 2.008122 2.109541 2.140318 2.300022     max neval 2.97674   100> microbenchmark::microbenchmark(pmin(x2, y2))Unit: milliseconds         expr      min       lq     mean   median       uq pmin(x2, y2) 4.652925 5.146819 5.286951 5.264451 5.445638      max neval 6.639985   100

So you can see the Rcpp version is faster.

You could make it better by adding some error checking in the function, for instance: check that both vectors are the same length, or that they are comparable (not character vs. numeric, or boolean vs. numeric).

If your data.frame name is dat.

dat$pmin <- do.call(pmin,dat[c("a","b")])dat$pmax <- do.call(pmax,dat[c("a","b")])