# Be assertive!

May 30, 2012
By

(This article was first published on 4D Pie Charts » R, and kindly contributed to R-bloggers)

assertive, my new package for writing robust code, is now on CRAN. It consists of lots of is functions for checking variables, and corresponding assert functions that throw an error if the condition doesn’t hold. For example, is_a_number checks that the input is numeric and scalar.

is_a_number(1)     #TRUE
is_a_number("a")   #FALSE
is_a_number(1:10)  #FALSE


In the last two cases, the return value of FALSE has an attribute “cause” that indicates the cause of failure. When “a” is the input, the cause is “"a" is not of type 'numeric'.“, whereas for 1:10, the cause is “1:10 does not have length one.“. You can get or set the cause attribute with the cause function.

m <- lm(uptake ~ 1, CO2)
ok <- is_empty_model(m)
if(!ok) cause(ok)


The assert functions call an is function, and if the result is FALSE, they throw an error; otherwise they do nothing.

assert_is_a_number(1)   #OK
assert_is_a_number("a") #Throws an error


There are also some has functions, primarily for checking the presence of attributes.

has_names(c(foo = 1, bar = 4, baz = 9))
has_dims(matrix(1:12, nrow = 3))


Some functions apply to properties of vectors. In this case, the assert functions can check that all the values conform to the condition, or any of the values conform.

x <- -2:2
is_positive(x)              #The last two are TRUE
assert_any_are_positive(x)  #OK
assert_all_are_positive(x)  #Error


“Why would you want to use these functions?”, you may be asking. The dynamic typing and extreme flexibility of R means that it is very easy to have variables that are the wrong format. This is particularly true when you are dealing with user input. So while you know that the sales totals passed to your function should be a vector of non-negative numbers, or that the regular expression should be a single string rather than a character vector, your user may not. You need to check for these invalid conditions, and return an error message that the user can understand. assertive makes it easy to do all this.

Since this is the first public release of assertive, it hasn’t been widely tested. I’ve written a moderately comprehensive unit-test suite, but there are likely to be a few minor bugs here and there. In particular, I suspect there may be one or two typos in the documentation. Please give the package a try, and let me know if you find any errors, or if you want any other functions adding.

Tagged: assertive, packages, r, robust-code, stats