With the pointblank package it’s really easy to validate your data with workflows attuned to your data quality needs. The pointblank philosophy: a set of validation functions should work seamlessly with data in local data tables and with data in databases.

The two dominant workflows that pointblank enables are data quality reporting and pipeline-based data validations. Both workflows make use of a large collection of simple validation functions (e.g., are values in a specific column greater than those in another column or some fixed value?), and, both allow for stepwise, temporary mutation/alteration of the input table (through preconditions) to enable much more sophisticated validation checks.

The first workflow, data quality reporting allows for the easy creation of a data quality analysis report. This is most useful in a non-interactive mode where data quality for database tables and on-disk data files must be periodically checked. The reporting component (through a pointblank agent) allows for the collection of detailed validation measures for each validation step, the optional extraction of data rows that failed validation (with options on limits), and custom functions that are invoked by exceeding set threshold failure rates. Want to email the report regularly (or, only if certain conditions are met)? Yep, you can do all that.

The second workflow, pipeline-based data validations gives us a different validation scheme that is valuable for data validation checks during an ETL process. With pointblank’s validation functions, we can directly operate on data and trigger warnings, raise errors, or write out logs when exceeding specified failure thresholds. It’s a cinch to perform checks on import of the data and at key points during the transformation process, perhaps stopping everything if things are exceptionally bad with regard to data quality.

The pointblank package is designed to be both straightforward yet powerful. And fast! All validation checks on remote tables are done entirely in-database so we can add dozens or hundreds of validation steps without any long waits for reporting. Here is a brief example of how to use pointblank to validate a local table with an agent.

# Generate a simple `action_levels` object to
# set the `warn` state if a validation step
# has a single 'fail' unit
al <- action_levels(warn_at = 1)

# Create a pointblank `agent` object, with the
# tibble as the target table. Use two validation
# step functions, then, `interrogate()`. The
# agent now has some useful intel.
agent <- 
    a = c(5, 7, 6, 5, NA, 7),
    b = c(6, 1, 0, 6,  0, 7)
  ) %>%
  create_agent(name = "simple_tibble", actions = al) %>%
  col_vals_between(vars(a), 1, 9, na_pass = TRUE) %>%
  col_vals_lt(vars(c), 12, preconditions = ~ . %>% dplyr::mutate(c = a + b)) %>%

Because an agent was used, we can get a gt-based report by printing it.

Next up is an example that follows the second, agent-less workflow (where validation functions operate directly on data). We use the same two validation functions as before but, this time, use them directly on the data! In this workflow, by default, an error will occur if there is a single ‘fail’ unit in any validation step:

    a = c(5, 7, 6, 5, NA, 7),
    b = c(6, 1, 0, 6,  0, 7)
  ) %>%
  col_vals_between(vars(a), 1, 9, na_pass = TRUE) %>%
  col_vals_lt(vars(c), 12, preconditions = ~ . %>% dplyr::mutate(c = a + b))
Error: Exceedance of failed test units where values in `c` should have been < `12`.
The `col_vals_lt()` validation failed beyond the absolute threshold level (1).
* failure level (2) >= failure threshold (1) 

We can downgrade this to a warning with the warn_on_fail() helper function (assigning to actions). In this way, the data will be returned, but warnings will appear.

# This `warn_on_fail()` function is a nice
# shortcut for `action_levels(warn_at = 1)`;
# it works great in this data checking workflow
# (and the threshold can still be adjusted)
al <- warn_on_fail()

    a = c(5, 7, 6, 5, NA, 7),
    b = c(6, 1, 0, 6,  0, 7)
  ) %>%
  col_vals_between(vars(a), 1, 9, na_pass = TRUE, actions = al) %>%
  col_vals_lt(vars(c), 12, preconditions = ~ . %>% dplyr::mutate(c = a + b), actions = al)
#> # A tibble: 6 x 2
#>       a     b
#>   <dbl> <dbl>
#> 1     5     6
#> 2     7     1
#> 3     6     0
#> 4     5     6
#> 5    NA     0
#> 6     7     7

Warning message:
Exceedance of failed test units where values in `c` should have been < `12`.
The `col_vals_lt()` validation failed beyond the absolute threshold level (1).
* failure level (2) >= failure threshold (1) 

Should you need more fine-grained thresholds and resultant actions, the action_levels() function can be used to specify multiple failure thresholds and side effects for each failure state. However, the warn_on_fail() and stop_on_fail() (applied by default, with stop_at = 1) helpers should in most cases suffice for this workflow.

Using pointblank in an R Markdown workflow is enabled by default once the pointblank library is loaded. The framework allows for validation testing within specialized validation code chunks where the validate = TRUE option is set. Using pointblank validation functions on data in these marked code chunks will flag overall failure if the stop threshold is exceeded anywhere. All errors are reported in the validation code chunk after rendering the document to HTML, where green or red status buttons indicate whether all validations succeeded or failures occurred. Click them to reveal the otherwise hidden validation statements and any associated error messages.

The above R Markdown document is available as a template in the RStudio IDE (it’s called Pointblank Validation). Try it out!

While data validation is important, one has to be familiar with the data first. To that end, the scan_data() function is provided in pointblank for generating a comprehensive summary of a tabular dataset. The report content is customizable, can be used inside an R Markdown document, and (as with the validation report) it can be produced in five different languages: English, French, German, Italian, and Spanish. Here are several published examples of a Table Scan for each of these languages using the dplyr::storms dataset. Clicking any of these will take you to a highly interactive RPubs document.

Table Scan in English    Table Scan in French    Table Scan in German    Table Scan in Italian    Table Scan in Spanish

Database tables can be used with scan_data() as well. Here are two examples using the full_region table of the Rfam database (hosted publicly at “mysql-rfam-public.ebi.ac.uk”) and the assembly table of the Ensembl database (hosted publicly at “ensembldb.ensembl.org”).

Rfam: full_region    Ensembl: assembly

There are many functions available in pointblank for making comprehensive table validations. Each validation function is associated with an expectation function (of the form expect_*()). They are equivalent in usage and behavior to testthat tests with the big distinction that they check aspects of data tables (and not the results of function calls). Furthermore, each validation function has an associated test function (of the form test_*()) which always returns a logical value (TRUE or FALSE).

Want to try this out? The pointblank package is available on CRAN:


You can also install the development version of pointblank from GitHub:


If you encounter a bug, have usage questions, or want to share ideas to make this package better, feel free to file an issue.

How pointblank Fits in with Other Packages that Validate Tabular Data

The pointblank package isn’t the only one of its kind available for R. The reason for introducing yet another has to do with pointblank’s goals:

  • ability to work with local tables, database tables, and Spark DataFrames (via sparklyr) with minimal changes in the API
  • great flexibility in data validation workflows, allowing for: (1) report-based validations, (2) inline validations, (3) validation of data tables in unit tests (with the set of expect_*() functions), and (4) validation of data tables to support conditional expressions (with the set of test_*() functions)
  • extra tools for understanding new datasets (scan_data()) and validating data in specialized R Markdown code chunks (validate_rmd())
  • reporting outputs translated to multiple spoken languages
  • developing an API that closely follows tidyverse conventions by adhering to the tidyverse style guide
  • lots of attention on making the package documentation and examples the best they can be

While pointblank is trying to do something different with its own interface, it may not suit your specific needs. Here is a listing of some of the other validation packages available for R, with links to their respective project pages:


validate (GITHUB)

dataMaid (GITHUB)

Code of Conduct

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


MIT © Richard Iannone