(This article was first published on

**Recology - R**, and kindly contributed to R-bloggers)## So I was trying to figure out a fast way to make matrices with randomly allocated 0 or 1 in each cell of the matrix. I reached out on Twitter, and got many responses (thanks tweeps!).

### Here is the solution I came up with. See if you can tell why it would be slow.

```
mm <- matrix(0, 10, 5)
apply(mm, c(1, 2), function(x) sample(c(0, 1), 1))
```

```
[,1] [,2] [,3] [,4] [,5]
[1,] 1 0 1 0 1
[2,] 0 0 1 1 1
[3,] 0 0 0 0 1
[4,] 0 1 1 0 1
[5,] 0 1 1 1 1
[6,] 1 0 1 1 1
[7,] 0 1 0 1 0
[8,] 0 0 1 0 1
[9,] 1 0 1 1 1
[10,] 1 0 0 1 1
```

### Ted Hart (@distribecology) replied first with:

```
matrix(rbinom(10 * 5, 1, 0.5), ncol = 5, nrow = 10)
```

```
[,1] [,2] [,3] [,4] [,5]
[1,] 1 1 0 1 1
[2,] 1 0 0 1 0
[3,] 0 1 0 0 0
[4,] 0 0 1 0 0
[5,] 1 0 1 0 0
[6,] 0 0 0 0 1
[7,] 1 0 0 0 0
[8,] 0 1 0 1 0
[9,] 1 1 1 1 0
[10,] 0 1 1 0 0
```

### Next, David Smith (@revodavid) and Rafael Maia (@hylospar) came up with about the same solution.

```
m <- 10
n <- 5
matrix(sample(0:1, m * n, replace = TRUE), m, n)
```

```
[,1] [,2] [,3] [,4] [,5]
[1,] 0 0 0 0 1
[2,] 0 0 0 0 0
[3,] 0 1 1 0 1
[4,] 1 0 0 1 0
[5,] 0 0 0 0 1
[6,] 1 0 1 1 1
[7,] 1 1 1 1 0
[8,] 0 0 0 1 1
[9,] 1 0 0 0 1
[10,] 0 1 0 1 1
```

### Then there was the solution by Luis Apiolaza (@zentree).

```
m <- 10
n <- 5
round(matrix(runif(m * n), m, n))
```

```
[,1] [,2] [,3] [,4] [,5]
[1,] 0 1 1 0 0
[2,] 1 0 1 1 0
[3,] 1 0 1 0 0
[4,] 1 0 0 0 1
[5,] 1 0 1 1 0
[6,] 1 0 0 0 0
[7,] 1 0 0 0 0
[8,] 1 1 1 0 0
[9,] 0 0 0 0 1
[10,] 1 0 0 1 1
```

### Last, a solution was proposed using `RcppArmadillo`

, but I couldn’t get it to work on my machine, but here is the function anyway if someone can.

```
library(inline)
library(RcppArmadillo)
f <- cxxfunction(body = "return wrap(arma::randu(5,10));", plugin = "RcppArmadillo")
```

### And here is the comparison of system.time for each solution.

```
mm <- matrix(0, 10, 5)
m <- 10
n <- 5
system.time(replicate(1000, apply(mm, c(1, 2), function(x) sample(c(0, 1), 1)))) # @recology_
```

```
user system elapsed
0.470 0.002 0.471
```

```
system.time(replicate(1000, matrix(rbinom(10 * 5, 1, 0.5), ncol = 5, nrow = 10))) # @distribecology
```

```
user system elapsed
0.014 0.000 0.015
```

```
system.time(replicate(1000, matrix(sample(0:1, m * n, replace = TRUE), m, n))) # @revodavid & @hylospar
```

```
user system elapsed
0.015 0.000 0.014
```

```
system.time(replicate(1000, round(matrix(runif(m * n), m, n)), )) # @zentree
```

```
user system elapsed
0.014 0.000 0.014
```

### If you want to take the time to learn C++ or already know it, the RcppArmadillo option would likely be the fastest, but I think (IMO) for many scientists, especially ecologists, we probably don’t already know C++, so will stick to the next fastest options.

### Get the .Rmd file used to create this post at my github account.

### Written in Markdown, with help from knitr, and nice knitr highlighting/etc. in in RStudio.

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