Slightly faster than quantile with
na.rm=TRUE. The internal implementation uses the 'C++' function
std::nth_element, which is significantly faster than base R
implementation when the length of input x is less than 1e7.
Examples
# create input x with NAs
x <- rnorm(10000)
x[sample(10000, 10)] <- NA
# compute median
res <- fastquantile(x, 0.5)
res
#> [1] -0.001544295
# base method
res == quantile(x, 0.5, na.rm = TRUE)
#> 50%
#> TRUE
res == median(x, na.rm = TRUE)
#> [1] TRUE
# Comparison
microbenchmark::microbenchmark(
{
fastquantile(x, 0.5)
},{
quantile(x, 0.5, na.rm = TRUE)
},{
median(x, na.rm = TRUE)
}
)
#> Unit: microseconds
#> expr min lq mean median
#> { fastquantile(x, 0.5) } 88.816 149.8695 162.7785 159.9635
#> { quantile(x, 0.5, na.rm = TRUE) } 239.878 263.7325 327.8180 285.5580
#> { median(x, na.rm = TRUE) } 167.693 189.8900 252.0953 208.9200
#> uq max neval
#> 181.6190 204.682 100
#> 415.0145 563.352 100
#> 328.0480 414.965 100