R Tip: use isTRUE()

By John Mount

(This article was first published on R – Win-Vector Blog, and kindly contributed to R-bloggers)

R Tip: use isTRUE().

A lot of R functions are type unstable, which means they return different types or classes depending on details of their values.

For example consider all.equal(), it returns the logical value TRUE when the items being compared are equal:

all.equal(1:3, c(1, 2, 3))
# [1] TRUE

However, when the items being compared are not equal all.equal() instead returns a message:

all.equal(1:3, c(1, 2.5, 3))
# [1] "Mean relative difference: 0.25"

This can be inconvenient in using functions similar to all.equal() as tests in if()-statements and other program control structures.

The saving functions is isTRUE(). isTRUE() returns TRUE if its argument value is equivalent to TRUE, and returns FALSE otherwise. isTRUE() makes R programming much easier.

Some examples of isTRUE() are given below:

# [1] TRUE


# [1] FALSE

# [1] FALSE

isTRUE(all.equal(1:3, c(1, 2.5, 3)))
# [1] FALSE

isTRUE(all.equal(1:3, c(1, 2, 3)))
# [1] TRUE


Using isTRUE() one can write safe and legible code such as the following:

# Pretend this assignment was performed by somebody else.
lst  3)) {
  lst$y = lst$x


# $x
# [1] 5
# $y
# [1] 5

R now also has isFALSE(), but by design it does not mean the same thing as !isTRUE(). The ideas is: for a value v at most of one of isTRUE() or isFALSE() is set, and both are non-NA unnamed scalar logical values. (example: isTRUE(5), isFALSE(5)).

Or as help(isTRUE) puts it:

… if(isTRUE(cond)) may be preferable to if(cond) …

Note: prior to R 3.5 isTRUE (the current version!) was defined as “isTRUE ” (please see change-log here). This seemed clever, but failed on named logical values (violating a principle of least surprise):


This caused a lot of problems (example taken from R3.5.0 NEWS):



To leave a comment for the author, please follow the link and comment on their blog: R – Win-Vector Blog.

R-bloggers.com offers daily e-mail updates about R news and tutorials on topics such as: Data science, Big Data, R jobs, visualization (ggplot2, Boxplots, maps, animation), programming (RStudio, Sweave, LaTeX, SQL, Eclipse, git, hadoop, Web Scraping) statistics (regression, PCA, time series, trading) and more...

Source:: R News