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 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?
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)