## A very simple example

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 that’s it.

Now each time you edit biggest.R re-sourcing test_biggest.R reloads your function and runs your unit tests.

## Comparing results

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