Equivalent to rowMeans() for min() - r

Question

I have seen this question being asked multiple times on the R mailing list, but still could not find a satisfactory answer.
Suppose I a matrix m
m <- matrix(rnorm(10000000), ncol=10)
I can get the mean of each row by:
system.time(rowMeans(m))
user system elapsed
0.100 0.000 0.097
But obtaining the minimum value of each row by
system.time(apply(m,1,min))
user system elapsed
16.157 0.400 17.029
takes more than 100 times as long, is there a way to speed this up?

Answer 1

You could use pmin, but you would have to get each column of your matrix into a separate vector. One way to do that is to convert it to a data.frame then call pmin via do.call (since data.frames are lists).
system.time(do.call(pmin, as.data.frame(m)))
# user system elapsed
# 0.940 0.000 0.949
system.time(apply(m,1,min))
# user system elapsed
# 16.84 0.00 16.95

Answer 2

Quite late to the party, but as the author of matrixStats and in case someone spots this, please note that matrixStats::rowMins() is very fast these days, e.g.
library(microbenchmark)
library(Biobase) # rowMin()
library(matrixStats) # rowMins()
options(digits=3)
m <- matrix(rnorm(10000000), ncol=10)
stats <- microbenchmark(
rowMeans(m), ## A benchmark by OP
rowMins(m),
rowMin(m),
do.call(pmin, as.data.frame(m)),
apply(m, MARGIN=1L, FUN=min),
times=10
)
> stats
Unit: milliseconds
expr min lq mean median uq max
rowMeans(m) 77.7 82.7 85.7 84.4 90.3 98.2
rowMins(m) 72.9 74.1 88.0 79.0 90.2 147.4
rowMin(m) 341.1 347.1 395.9 383.4 395.1 607.7
do.call(pmin, as.data.frame(m)) 326.4 357.0 435.4 401.0 437.6 657.9
apply(m, MARGIN = 1L, FUN = min) 3761.9 3963.8 4120.6 4109.8 4198.7 4567.4

Answer 3

If you want to stick to CRAN packages, then both the matrixStats and the fBasics packages have the function rowMins [note the s which is not in the Biobase function] and a variety of other row and column statistics.

Answer 4

library("sos")
findFn("rowMin")
gets a hit in the Biobase package, from Bioconductor ...
source("http://bioconductor.org/biocLite.R")
biocLite("Biobase")
m <- matrix(rnorm(10000000), ncol=10)
system.time(rowMeans(m))
## user system elapsed
## 0.132 0.148 0.279
system.time(apply(m,1,min))
## user system elapsed
## 11.825 1.688 13.603
library(Biobase)
system.time(rowMin(m))
## user system elapsed
## 0.688 0.172 0.864
Not as fast as rowMeans, but a lot faster than apply(...,1,min)

Answer 5

I've been meaning to try out the new compiler package in R 2.13.0. This essentially follows the post outlined by Dirk here.
library(compiler)
library(rbenchmark)
rowMin <- function(x, ind) apply(x, ind, min)
crowMin <- cmpfun(rowMin)
benchmark(
rowMin(m,1)
, crowMin(m,1)
, columns=c("test", "replications","elapsed","relative")
, order="relative"
, replications=10)
)
And the results:
test replications elapsed relative
2 crowMin(m, 1) 10 120.091 1.0000
1 rowMin(m, 1) 10 122.745 1.0221
Anticlimatic to say the least, though looks like you've gotten some other good options.

Answer 6

Not particularly R-idiosyncratic, but surely the fastest method is just to use pmin and loop over columns:
x <- m[,1]
for (i in 2:ncol(m)) x <- pmin(x, m[,i])
On my machine that takes just 3 times longer than rowMeans for the 1e+07x10 matrix, and is slightly faster than the do.call method via data.frame.