Suppose you have a file `biggest.R`

with the following
function:

`biggest <- function(x,y) {max(c(x,y))}`

To test this create a file called `test_biggest.R`

in the
same directory containing:

```
library(unittest, quietly = TRUE)
source('biggest.R')
ok(biggest(3,4) == 4, "two numbers")
ok(biggest(c(5,3),c(3,4)) == 5, "two vectors")
```

Now in an `R`

session `source`

the test
file:

`source('test_biggest.R')`

and you will see output like this

```
ok - two numbers
ok - two vectors
```

and thatâ€™s it.

Now each time you edit `biggest.R`

re-sourcing
`test_biggest.R`

reloads your function and runs your unit
tests.

Suppose our `biggest`

function was broken, for
example:

`biggest <- function(x,y) { 4 }`

Our tests from earlier would return:

```
ok - two numbers
not ok - two vectors
# Test returned non-TRUE value:
# [1] FALSE
```

It would be more useful if we saw what `biggest()`

actually returned, to help work out the problem.

To help with this we can use `ut_cmp_equal`

. If we rewrite
our test to:

```
library(unittest, quietly = TRUE)
source('biggest.R')
ok(ut_cmp_equal(biggest(3,4), 4), "two numbers")
ok(ut_cmp_equal(biggest(c(5,3),c(3,4)), 5), "two vectors")
```

Now the test output shows what we did get (in red) and what we expected (in green):

ok - two numbers not ok - two vectors # Test returned non-TRUE value: # Mean relative difference: 0.25 # --- biggest(c(5, 3), c(3, 4)) # +++ 5 # [1] [-4-]{+5+}

This is particularly useful when there are many values returned:

> ok(ut_cmp_equal(c(1,2,3,4,5), c(1,8,8,4,5))) not ok - ut_cmp_equal(c(1, 2, 3, 4, 5), c(1, 8, 8, 4, 5)) # Test returned non-TRUE value: # Mean relative difference: 2.2 # --- c(1, 2, 3, 4, 5) # +++ c(1, 8, 8, 4, 5) # [1] 1 [-2 3-]{+8 8+} 4 5