Equivalent to rowMeans() for min()

r optimization

14,342

Solution 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

Solution 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

Solution 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.

Solution 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)

Solution 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.

View more solutions

14,342

Author by

johannes

Updated on June 06, 2022

Comments

johannes almost 2 years
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?
Chase almost 13 years

I like the use of do.call. I thought of pmin, but didn't think of a slick way to incorporate it. All the cool kids seem to be able to use do.call to achieve their goals...I need to do some reading on this.
Joshua Ulrich almost 13 years

do.call comes in handy when you want to be able to create function arguments dynamically (generally when the number of arguments passed via ... isn't known).
johannes almost 13 years

Nice answer, thanks! with pmin.int() it was even a tiny bit faster
johannes almost 13 years

thanks, I wasn't aware of the sos package and rowMin solves my problem too.
johannes almost 13 years

thanks for your answer, I will have to look deeper into your answer, that's new terrain for me :)
Marek almost 13 years

Hadley have nice vocabulary of functions that you need to know. There is pmin too.
Marek almost 13 years

Compiler is better in optimization of explicit loops. Try e.g.: rowMin <- function(x) {n <- nrow(x);r <- numeric(n);for (i in 1:n) r[i] <- min(x[i,]);r}
Roman Luštrik almost 13 years

Care to time the do.call solution as well?
Marek almost 13 years

And another speed gain by pmin(m[,1], m[,2], m[,3], m[,4], m[,5], m[,6], m[,7], m[,8], m[,9], m[,10]). Joshua as.data.frame is time consuming.
Marek almost 13 years

Some variation of your answer: do.call(pmin, lapply(seq_len(ncol(m)), function(i) m[,i]))
mdsumner almost 13 years

not speedy for typing though, or general to different inputs :)
Marek almost 13 years

I add more general solution in comment to Joshua answer.
danas.zuokas almost 12 years

@Marek, how to handle NA values in your solution?
Joshua Ulrich almost 12 years

@danas.zuokas: do.call(pmin, c(na.rm=TRUE, lapply(...)))
skan almost 9 years

@HenirkB it would be great if matrixStats rowMins also worked on data.frames, (without the need of transform it to matrix first)
HenrikB almost 9 years

@skan, unfortunately it's not obvious that this belongs to matrixStats for various reasons, please see github.com/HenrikBengtsson/matrixStats/issues/18