Equivalent to rowMeans() for min()

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 min开发者_如何学Goimum 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?

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 

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

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.

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)

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.

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.