Replace negative values by zero

I would use pmax because ifelse can be a bit slow at times and subset-replacement creates an additional vector (which can be an issue with large data sets).

set.seed(21)
pred_precipitation <- rnorm(25,2,4)
p <- pmax(pred_precipitation,0)

Subset-replacement is by-far the fastest though:

library(rbenchmark)
gsk3 <- function(x) { x[x<0] <- 0; x }
jmsigner <- function(x) ifelse(x<0, 0, x)
joshua <- function(x) pmin(x,0)
benchmark(joshua(p), gsk3(p), jmsigner(p), replications=10000, order="relative")
         test replications elapsed relative user.self sys.self
2     gsk3(p)        10000   0.215 1.000000     0.216    0.000
1   joshua(p)        10000   0.444 2.065116     0.416    0.016
3 jmsigner(p)        10000   0.656 3.051163     0.652    0.000

Thanks for the reproducible example. This is pretty basic R stuff. You can assign to selected elements of a vector (note an array has dimensions, and what you've given is a vector not an array):

> pred_precipitation[pred_precipitation<0] <- 0
> pred_precipitation
 [1] 1.2091281 0.0000000 7.7665555 0.0000000 0.0000000 0.0000000 0.5151504 0.0000000 1.8281251
[10] 0.5098688 2.8370263 0.4895606 1.5152191 4.1740177 7.1527742 2.8992215 4.5322934 6.7180530
[19] 0.0000000 1.1914052 3.6152333 0.0000000 0.3778717 0.0000000 1.4940469

Benchmark wars!

@James has found an even faster method and left it in a comment. I upvoted him, if only because I know his victory will be short-lived.

First, I try compiling, but that doesn't seem to help anyone:

p <- rnorm(10000)
gsk3 <- function(x) { x[x<0] <- 0; x }
jmsigner <- function(x) ifelse(x<0, 0, x)
joshua <- function(x) pmin(x,0)
james <- function(x) (abs(x)+x)/2
library(compiler)
gsk3.c <- cmpfun(gsk3)
jmsigner.c <- cmpfun(jmsigner)
joshua.c <- cmpfun(joshua)
james.c <- cmpfun(james)

microbenchmark(joshua(p),joshua.c(p),gsk3(p),gsk3.c(p),jmsigner(p),james(p),jmsigner.c(p),james.c(p))
           expr      min        lq    median        uq      max
1     gsk3.c(p)  251.782  255.0515  266.8685  269.5205  457.998
2       gsk3(p)  256.262  261.6105  270.7340  281.3560 2940.486
3    james.c(p)   38.418   41.3770   43.3020   45.6160  132.342
4      james(p)   38.934   42.1965   43.5700   47.2085 4524.303
5 jmsigner.c(p) 2047.739 2145.9915 2198.6170 2291.8475 4879.418
6   jmsigner(p) 2047.502 2169.9555 2258.6225 2405.0730 5064.334
7   joshua.c(p)  237.008  244.3570  251.7375  265.2545  376.684
8     joshua(p)  237.545  244.8635  255.1690  271.9910  430.566

But wait! Dirk wrote this Rcpp thing. Can a complete C++ incompetent read his JSS paper, adapt his example, and write the fastest function of them all? Stay tuned, dear listeners.

library(inline)
cpp_if_src <- '
  Rcpp::NumericVector xa(a);
  int n_xa = xa.size();
  for(int i=0; i < n_xa; i++) {
    if(xa[i]<0) xa[i] = 0;
  }
  return xa;
'
cpp_if <- cxxfunction(signature(a="numeric"), cpp_if_src, plugin="Rcpp")
microbenchmark(joshua(p),joshua.c(p),gsk3(p),gsk3.c(p),jmsigner(p),james(p),jmsigner.c(p),james.c(p), cpp_if(p))
         expr      min        lq    median        uq       max
1   cpp_if(p)    8.233   10.4865   11.6000   12.4090    69.512
2     gsk3(p)  170.572  172.7975  175.0515  182.4035  2515.870
3    james(p)   37.074   39.6955   40.5720   42.1965  2396.758
4 jmsigner(p) 1110.313 1118.9445 1133.4725 1164.2305 65942.680
5   joshua(p)  237.135  240.1655  243.3990  250.3660  2597.429

That's affirmative, captain.

This modifies the input p even if you don't assign to it. If you want to avoid that behavior, you have to clone:

cpp_ifclone_src <- '
  Rcpp::NumericVector xa(Rcpp::clone(a));
  int n_xa = xa.size();
  for(int i=0; i < n_xa; i++) {
    if(xa[i]<0) xa[i] = 0;
  }
  return xa;
'
cpp_ifclone <- cxxfunction(signature(a="numeric"), cpp_ifclone_src, plugin="Rcpp")

Which unfortunately kills the speed advantage.