The assertr package supplies a suite of functions designed to verify assumptions about data early in an analysis pipeline so that data errors are spotted early and can be addressed quickly.
This package does not need to be used with the magrittr/dplyr piping mechanism but the examples in this README use them for clarity.
You can install the latest version on CRAN like this
or you can install the bleedingedge development version like this:
This package offers five assertion functions, assert
, verify
, insist
, assert_rows
, and insist_rows
, that are designed to be used shortly after dataloading in an analysis pipeline…
Let’s say, for example, that the R’s builtin car dataset, mtcars
, was not builtin but rather procured from an external source that was known for making errors in data entry or coding. Pretend we wanted to find the average miles per gallon for each number of engine cylinders. We might want to first, confirm  that it has the columns “mpg”, “vs”, and “am”  that the dataset contains more than 10 observations  that the column for ‘miles per gallon’ (mpg) is a positive number  that the column for ‘miles per gallon’ (mpg) does not contain a datum that is outside 4 standard deviations from its mean, and  that the am and vs columns (automatic/manual and v/straight engine, respectively) contain 0s and 1s only  each row contains at most 2 NAs  each row is unique jointly between the “mpg”, “am”, and “wt” columns  each row’s mahalanobis distance is within 10 median absolute deviations of all the distances (for outlier detection)
This could be written (in order) using assertr
like this:
library(dplyr)
library(assertr)
mtcars %>%
verify(has_all_names("mpg", "vs", "am", "wt")) %>%
verify(nrow(.) > 10) %>%
verify(mpg > 0) %>%
insist(within_n_sds(4), mpg) %>%
assert(in_set(0,1), am, vs) %>%
assert_rows(num_row_NAs, within_bounds(0,2), everything()) %>%
assert_rows(col_concat, is_uniq, mpg, am, wt) %>%
insist_rows(maha_dist, within_n_mads(10), everything()) %>%
group_by(cyl) %>%
summarise(avg.mpg=mean(mpg))
If any of these assertions were violated, an error would have been raised and the pipeline would have been terminated early.
Let’s see what the error message look like when you chain a bunch of failing assertions together.
> mtcars %>%
+ chain_start %>%
+ assert(in_set(1, 2, 3, 4), carb) %>%
+ assert_rows(rowMeans, within_bounds(0,5), gear:carb) %>%
+ verify(nrow(.)==10) %>%
+ verify(mpg < 32) %>%
+ chain_end
There are 7 errors across 4 verbs:

verb redux_fn predicate column index value
1 assert <NA> in_set(1, 2, 3, 4) carb 30 6.0
2 assert <NA> in_set(1, 2, 3, 4) carb 31 8.0
3 assert_rows rowMeans within_bounds(0, 5) ~gear:carb 30 5.5
4 assert_rows rowMeans within_bounds(0, 5) ~gear:carb 31 6.5
5 verify <NA> nrow(.) == 10 <NA> 1 NA
6 verify <NA> mpg < 32 <NA> 18 NA
7 verify <NA> mpg < 32 <NA> 20 NA
Error: assertr stopped execution
assertr
give me?verify
 takes a data frame (its first argument is provided by the %>%
operator above), and a logical (boolean) expression. Then, verify
evaluates that expression using the scope of the provided data frame. If any of the logical values of the expression’s result are FALSE
, verify
will raise an error that terminates any further processing of the pipeline.
assert
 takes a data frame, a predicate function, and an arbitrary number of columns to apply the predicate function to. The predicate function (a function that returns a logical/boolean value) is then applied to every element of the columns selected, and will raise an error if it finds any violations. Internally, the assert
function uses dplyr
’s select
function to extract the columns to test the predicate function on.
insist
 takes a data frame, a predicategenerating function, and an arbitrary number of columns. For each column, the the predicategenerating function is applied, returning a predicate. The predicate is then applied to every element of the columns selected, and will raise an error if it finds any violations. The reason for using a predicategenerating function to return a predicate to use against each value in each of the selected rows is so that, for example, bounds can be dynamically generated based on what the data look like; this the only way to, say, create bounds that check if each datum is within x zscores, since the standard deviation isn’t known a priori. Internally, the insist
function uses dplyr
’s select
function to extract the columns to test the predicate function on.
assert_rows
 takes a data frame, a row reduction function, a predicate function, and an arbitrary number of columns to apply the predicate function to. The row reduction function is applied to the data frame, and returns a value for each row. The predicate function is then applied to every element of vector returned from the row reduction function, and will raise an error if it finds any violations. This functionality is useful, for example, in conjunction with the num_row_NAs()
function to ensure that there is below a certain number of missing values in each row. Internally, the assert_rows
function uses dplyr
’sselect
function to extract the columns to test the predicate function on.
insist_rows
 takes a data frame, a row reduction function, a predicategenerating function, and an arbitrary number of columns to apply the predicate function to. The row reduction function is applied to the data frame, and returns a value for each row. The predicategenerating function is then applied to the vector returned from the row reduction function and the resultant predicate is applied to each element of that vector. It will raise an error if it finds any violations. This functionality is useful, for example, in conjunction with the maha_dist()
function to ensure that there are no flagrant outliers. Internally, the assert_rows
function uses dplyr
’sselect
function to extract the columns to test the predicate function on.
assertr
also offers four (so far) predicate functions designed to be used with the assert
and assert_rows
functions:
not_na
 that checks if an element is not NAwithin_bounds
 that returns a predicate function that checks if a numeric value falls within the bounds supplied, andin_set
 that returns a predicate function that checks if an element is a member of the set supplied. (also allows inverse for “not in set”)is_uniq
 that checks to see if each element appears only onceand predicate generators designed to be used with the insist
and insist_rows
functions:
within_n_sds
 used to dynamically create bounds to check vector elements with based on standard zscoreswithin_n_mads
 better method for dynamically creating bounds to check vector elements with based on ‘robust’ zscores (using median absolute deviation)and the following row reduction functions designed to be used with assert_rows
and insist_rows
:
num_row_NAs
 counts number of missing values in each rowmaha_dist
 computes the mahalanobis distance of each row (for outlier detection). It will coerce categorical variables into numerics if it needs to.col_concat
 concatenates all rows into stringsduplicated_across_cols
 checking if a row contains a duplicated value across columnsand, finally, some other utilities for use with verify
has_all_names
 check if the data frame or list has all supplied nameshas_only_names
 check that a data frame or list have only the names requestedhas_class
 checks if passed data has a particular classFor more info, check out the assertr
vignette
Or read it here