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checkr is a light-weight R package of expressive, assertive, pipe-friendly functions to check the properties of common R objects.

In the case of failure the functions, which are designed to be used in scripts and packages, issue informative error messages.

For an overview of the functions see the checkr-naming vignette and for a comparison with similar packages see the assertive-programming vignette.


The following code demonstrates the check_data() function


# the starwars data frame in the dplyr package fails many of these checks
check_data(dplyr::starwars, values = list(
  height = c(66L, 264L),
  name = "",
  mass = c(20,1358, NA),
  hair_color = c("blond", "brown", "black", NA),
  gender = c("male", "female", "hermaphrodite", "none", NA)), 
    order = TRUE, nrow = c(81, 84), key = "hair_color", error = FALSE)
#> Error in cc_and(colnames): could not find function "cc_and"


checkr uses objects to check the values of other objects using an elegant and expressive syntax.


To check the class simply pass an object of the desired class.

y <- c(2,1,0,1,NA)
check_vector(y, values = numeric(0))
check_vector(y, values = integer(0))
#> Error: y must be class integer

Missing Values

To check that a vector does not include missing values pass a single non-missing value (of the correct class).

check_vector(y, 1)
#> Error: y must not include missing values

To allow it to include missing values include a missing value.

check_vector(y, c(1, NA))

And to check that it only includes missing values only pass a missing value (of the correct class)

check_vector(y, NA_real_)
#> Error: y must only include missing values


To check the range of a vector pass two non-missing values (as well as the missing value if required).

check_vector(y, c(0, 2, NA))
check_vector(y, c(-1, -10, NA))
#> Error in cc_and(values[1:2]): could not find function "cc_and"

Specific Values

To check the vector only includes specific values pass three or more non-missing values or set only = TRUE.

check_vector(y, c(0, 1, 2, NA))
check_vector(y, c(1, 1, 2, NA))
#> Error in cc_or(values): could not find function "cc_or"
check_vector(y, c(1, 2, NA), only = TRUE)
#> Error in cc_or(values): could not find function "cc_or"

Naming Objects

By default, the name of an object is determined from the function call.

check_vector(list(x = 1))
#> Error: list(x = 1) must be an atomic vector

This simplifies things but results in less informative error messages when used in a pipe.

y %>% check_list()
#> Error: . must be a list

The argument x_name can be used to override the name.

y %>% check_list(x_name = "y")
#> Error: y must be a list


The four wrapper functions check_lgl(), check_int(), check_dbl() and check_str() check whether an object is an attribute-less non-missing scalar logical (flag), integer, double (number) or character (string). They are really useful for checking the types of arguments in functions

fun <- function(x) { check_lgl(x)}
fun(x = NA)
#> Error: x must not include missing values
fun(x = TRUE)
fun(x = 1)
#> Error: x must be class logical

Additional scalar wrappers are check_date() and check_dttm() for scalar Date and POSIXct objects. Alternatively you can roll your own using the more general check_scalar() function.


To install the latest official release from CRAN


To install the latest development version from GitHub


To install the latest development version from the Poisson drat repository



To cite checkr in publications use:

  Joe Thorley (2018). checkr: An R package for Assertive
  Programming. Journal of Open Source Software, 3(23), 624. URL

A BibTeX entry for LaTeX users is

    title = {checkr: {An} {R} package for {Assertive} {Programming}},
    author = {Joe Thorley},
    journal = {Journal of Open Source Software},
    year = {2018},
    volume = {3},
    number = {23},
    pages = {624},
    url = {},


Please report any issues.

Pull requests are always welcome.

Please note that this project is released with a Contributor Code of Conduct. By participating in this project you agree to abide by its terms.