R equivalent of microbenchmark that includes memory as well as runtime

Background:
This is the "microbenchmark" package for R: https://cran.r-project.org/web/packages/microbenchmark/index.html

The first line in the reference manual says that it is built for "Accurate Timing Functions".

One problem with this is the intrinsic computer-time vs. computer-memory trade-off. Some solutions are memory intensive, but CPU fast. Some are CPU intensive, but have a very small memory footprint.

Question:
How do I simultaneously, and with good resolution, benchmark/microbenchmark not only the execution time, but the memory use during execution in R?

Solution

Better late than never: You can use bench::mark() to measure both time and memory usage of code (and some more variables).

I.e., (taken from the help page for ?mark)

library(bench)

dat <- data.frame(x = runif(100, 1, 1000), y=runif(10, 1, 1000))
mark(
  dat[dat$x > 500, ],
  dat[which(dat$x > 500), ],
  subset(dat, x > 500)
)
#> # A tibble: 3 x 6
#>   expression                     min   median `itr/sec` mem_alloc `gc/sec`
#>   <bch:expr>                <bch:tm> <bch:tm>     <dbl> <bch:byt>    <dbl>
#> 1 dat[dat$x > 500, ]          21.7µs   23.6µs    40663.    4.15KB     89.7
#> 2 dat[which(dat$x > 500), ]   22.2µs   24.1µs    40228.    2.77KB     92.7
#> 3 subset(dat, x > 500)          36µs   39.2µs    23867.   20.12KB     86.2

^{Created on 2020-03-02 by the reprex package (v0.3.0)}