#testthat — Public Fediverse posts

Discover the power of property-based testing in R with the #quickcheck package! Seamlessly integrates with #testthat and offers a variety of generators for atomic vectors, lists, and tibbles. Perfect for ensuring your code's reliability. Check it out: https://github.com/armcn/quickcheck #rstats #rprogramming #testing

Discover the power of property-based testing in R with the #quickcheck package! Seamlessly integrates with #testthat and offers a variety of generators for atomic vectors, lists, and tibbles. Perfect for ensuring your code's reliability. Check it out: https://github.com/armcn/quickcheck #rstats #rprogramming #testing

{testthat} is great for automatic testing. Here are some tricks for the heavy user: https://blog.r-hub.io/2020/11/18/testthat-utility-belt/ #rstats #testing #unittest #testthat

{testthat} is great for automatic testing. Here are some tricks for the heavy user: https://blog.r-hub.io/2020/11/18/testthat-utility-belt/ #rstats #testing #unittest #testthat

Its good to have many tests in your R package, but it can be a pain to debug some failing tests when it happens. {lazytest} for the rescue: only rerun the failing tests, until they pass: https://lazytest.cynkra.com/ #RStats #testthat #lazy

Its good to have many tests in your R package, but it can be a pain to debug some failing tests when it happens. {lazytest} for the rescue: only rerun the failing tests, until they pass: https://lazytest.cynkra.com/ #RStats #testthat #lazy

Discover the power of property-based testing in R with the #quickcheck package! Seamlessly integrates with #testthat and offers a variety of generators for atomic vectors, lists, and tibbles. Perfect for ensuring your code's reliability. Check it out: https://github.com/armcn/quickcheck #rstats #rprogramming #testing

Discover the power of property-based testing in R with the #quickcheck package! Seamlessly integrates with #testthat and offers a variety of generators for atomic vectors, lists, and tibbles. Perfect for ensuring your code's reliability. Check it out: https://github.com/armcn/quickcheck #rstats #rprogramming #testing

{testthat} is great for automatic testing. Here are some tricks for the heavy user: https://blog.r-hub.io/2020/11/18/testthat-utility-belt/ #rstats #testing #unittest #testthat

{testthat} is great for automatic testing. Here are some tricks for the heavy user: https://blog.r-hub.io/2020/11/18/testthat-utility-belt/ #rstats #testing #unittest #testthat

Its good to have many tests in your R package, but it can be a pain to debug some failing tests when it happens. {lazytest} for the rescue: only rerun the failing tests, until they pass: https://lazytest.cynkra.com/ #RStats #testthat #lazy

Its good to have many tests in your R package, but it can be a pain to debug some failing tests when it happens. {lazytest} for the rescue: only rerun the failing tests, until they pass: https://lazytest.cynkra.com/ #RStats #testthat #lazy

Wrote about using #googlejules to migrate an #RStats test suite to #testthat

https://www.linkedin.com/posts/mirai-solutions-gmbh_from-runit-to-testthat-with-coding-agent-activity-7399043585066655744-yRX6

#aiagents

Discover the power of property-based testing in R with the #quickcheck package! Seamlessly integrates with #testthat and offers a variety of generators for atomic vectors, lists, and tibbles. Perfect for ensuring your code's reliability. Check it out: https://github.com/armcn/quickcheck #rstats #rprogramming #testing

Discover the power of property-based testing in R with the #quickcheck package! Seamlessly integrates with #testthat and offers a variety of generators for atomic vectors, lists, and tibbles. Perfect for ensuring your code's reliability. Check it out: https://github.com/armcn/quickcheck #rstats #rprogramming #testing

{testthat} is great for automatic testing. Here are some tricks for the heavy user: https://blog.r-hub.io/2020/11/18/testthat-utility-belt/ #rstats #testing #unittest #testthat

{testthat} is great for automatic testing. Here are some tricks for the heavy user: https://blog.r-hub.io/2020/11/18/testthat-utility-belt/ #rstats #testing #unittest #testthat

Its good to have many tests in your R package, but it can be a pain to debug some failing tests when it happens. {lazytest} for the rescue: only rerun the failing tests, until they pass: https://lazytest.cynkra.com/ #RStats #testthat #lazy

Its good to have many tests in your R package, but it can be a pain to debug some failing tests when it happens. {lazytest} for the rescue: only rerun the failing tests, until they pass: https://lazytest.cynkra.com/ #RStats #testthat #lazy

Discover the power of property-based testing in R with the #quickcheck package! Seamlessly integrates with #testthat and offers a variety of generators for atomic vectors, lists, and tibbles. Perfect for ensuring your code's reliability. Check it out: https://github.com/armcn/quickcheck #rstats #rprogramming #testing

Discover the power of property-based testing in R with the #quickcheck package! Seamlessly integrates with #testthat and offers a variety of generators for atomic vectors, lists, and tibbles. Perfect for ensuring your code's reliability. Check it out: https://github.com/armcn/quickcheck #rstats #rprogramming #testing

{testthat} is great for automatic testing. Here are some tricks for the heavy user: https://blog.r-hub.io/2020/11/18/testthat-utility-belt/ #rstats #testing #unittest #testthat

{testthat} is great for automatic testing. Here are some tricks for the heavy user: https://blog.r-hub.io/2020/11/18/testthat-utility-belt/ #rstats #testing #unittest #testthat

Its good to have many tests in your R package, but it can be a pain to debug some failing tests when it happens. {lazytest} for the rescue: only rerun the failing tests, until they pass: https://lazytest.cynkra.com/ #RStats #testthat #lazy

Its good to have many tests in your R package, but it can be a pain to debug some failing tests when it happens. {lazytest} for the rescue: only rerun the failing tests, until they pass: https://lazytest.cynkra.com/ #RStats #testthat #lazy

Discover the power of property-based testing in R with the #quickcheck package! Seamlessly integrates with #testthat and offers a variety of generators for atomic vectors, lists, and tibbles. Perfect for ensuring your code's reliability. Check it out: https://github.com/armcn/quickcheck #rstats #rprogramming #testing

Discover the power of property-based testing in R with the #quickcheck package! Seamlessly integrates with #testthat and offers a variety of generators for atomic vectors, lists, and tibbles. Perfect for ensuring your code's reliability. Check it out: https://github.com/armcn/quickcheck #rstats #rprogramming #testing

{testthat} is great for automatic testing. Here are some tricks for the heavy user: https://blog.r-hub.io/2020/11/18/testthat-utility-belt/ #rstats #testing #unittest #testthat

{testthat} is great for automatic testing. Here are some tricks for the heavy user: https://blog.r-hub.io/2020/11/18/testthat-utility-belt/ #rstats #testing #unittest #testthat

Its good to have many tests in your R package, but it can be a pain to debug some failing tests when it happens. {lazytest} for the rescue: only rerun the failing tests, until they pass: https://lazytest.cynkra.com/ #RStats #testthat #lazy

Its good to have many tests in your R package, but it can be a pain to debug some failing tests when it happens. {lazytest} for the rescue: only rerun the failing tests, until they pass: https://lazytest.cynkra.com/ #RStats #testthat #lazy

Discover the power of property-based testing in R with the #quickcheck package! Seamlessly integrates with #testthat and offers a variety of generators for atomic vectors, lists, and tibbles. Perfect for ensuring your code's reliability. Check it out: https://github.com/armcn/quickcheck #rstats #rprogramming #testing

Discover the power of property-based testing in R with the #quickcheck package! Seamlessly integrates with #testthat and offers a variety of generators for atomic vectors, lists, and tibbles. Perfect for ensuring your code's reliability. Check it out: https://github.com/armcn/quickcheck #rstats #rprogramming #testing

{testthat} is great for automatic testing. Here are some tricks for the heavy user: https://blog.r-hub.io/2020/11/18/testthat-utility-belt/ #rstats #testing #unittest #testthat

{testthat} is great for automatic testing. Here are some tricks for the heavy user: https://blog.r-hub.io/2020/11/18/testthat-utility-belt/ #rstats #testing #unittest #testthat

A common #TestThat frustration I have is trying to track down where messages are coming from in the tests so I can test for them and then suppress them.

Does anyone know a good way of sussing them out other than running test by test?

#RStats

A common #TestThat frustration I have is trying to track down where messages are coming from in the tests so I can test for them and then suppress them.

Does anyone know a good way of sussing them out other than running test by test?

#RStats

Its good to have many tests in your R package, but it can be a pain to debug some failing tests when it happens. {lazytest} for the rescue: only rerun the failing tests, until they pass: https://lazytest.cynkra.com/ #RStats #testthat #lazy

Its good to have many tests in your R package, but it can be a pain to debug some failing tests when it happens. {lazytest} for the rescue: only rerun the failing tests, until they pass: https://lazytest.cynkra.com/ #RStats #testthat #lazy

#rstats #testthat #codecov
Edit: uploading results from the different runners worked! I was just impatient.

Question

I have a function that has different behavior on Windows and (MacOS/Linux). I have tests that correctly check all of this (using testthat::skip_on_os() ). Everything is working as intended.

But when I run my CI checks, codecov marks the “windows” lines as untested. My naive solution of running the codecov action on a windows runner is also not solving it.

Any suggestions?

#rstats #testthat #codecov
Edit: uploading results from the different runners worked! I was just impatient.

Question

I have a function that has different behavior on Windows and (MacOS/Linux). I have tests that correctly check all of this (using testthat::skip_on_os() ). Everything is working as intended.

But when I run my CI checks, codecov marks the “windows” lines as untested. My naive solution of running the codecov action on a windows runner is also not solving it.

Any suggestions?

Discover the power of property-based testing in R with the #quickcheck package! Seamlessly integrates with #testthat and offers a variety of generators for atomic vectors, lists, and tibbles. Perfect for ensuring your code's reliability. Check it out: https://github.com/armcn/quickcheck #rstats #rprogramming #testing

Discover the power of property-based testing in R with the #quickcheck package! Seamlessly integrates with #testthat and offers a variety of generators for atomic vectors, lists, and tibbles. Perfect for ensuring your code's reliability. Check it out: https://github.com/armcn/quickcheck #rstats #rprogramming #testing

{testthat} is great for automatic testing. Here are some tricks for the heavy user: https://blog.r-hub.io/2020/11/18/testthat-utility-belt/ #rstats #testing #unittest #testthat

{testthat} is great for automatic testing. Here are some tricks for the heavy user: https://blog.r-hub.io/2020/11/18/testthat-utility-belt/ #rstats #testing #unittest #testthat

Its good to have many tests in your R package, but it can be a pain to debug some failing tests when it happens. {lazytest} for the rescue: only rerun the failing tests, until they pass: https://lazytest.cynkra.com/ #RStats #testthat #lazy

Its good to have many tests in your R package, but it can be a pain to debug some failing tests when it happens. {lazytest} for the rescue: only rerun the failing tests, until they pass: https://lazytest.cynkra.com/ #RStats #testthat #lazy

Hive mind, do you employ (unit) testing in data analysis scripts? If so, how and what are your best practices?

We frequently develop packages with unit testing. But equally often write "run-once" analysis scripts/notebooks. Those often contain functions and (more often) assumptions on loaded data but we rarely test/check these. This is something to improve but I wonder how others handle this.

#RStats #rmarkdown #testthat #assertthat #python #jupyter #unittesting #datascience #bioinformatics

Hive mind, do you employ (unit) testing in data analysis scripts? If so, how and what are your best practices?

We frequently develop packages with unit testing. But equally often write "run-once" analysis scripts/notebooks. Those often contain functions and (more often) assumptions on loaded data but we rarely test/check these. This is something to improve but I wonder how others handle this.

#RStats #rmarkdown #testthat #assertthat #python #jupyter #unittesting #datascience #bioinformatics

{testthat} is great for automatic testing. Here are some tricks for the heavy user: https://blog.r-hub.io/2020/11/18/testthat-utility-belt/ #rstats #testing #unittest #testthat

{testthat} is great for automatic testing. Here are some tricks for the heavy user: https://blog.r-hub.io/2020/11/18/testthat-utility-belt/ #rstats #testing #unittest #testthat

Its good to have many tests in your R package, but it can be a pain to debug some failing tests when it happens. {lazytest} for the rescue: only rerun the failing tests, until they pass: https://lazytest.cynkra.com/ #RStats #testthat #lazy

Its good to have many tests in your R package, but it can be a pain to debug some failing tests when it happens. {lazytest} for the rescue: only rerun the failing tests, until they pass: https://lazytest.cynkra.com/ #RStats #testthat #lazy

{testthat} is great for automatic testing. Here are some tricks for the heavy user: https://blog.r-hub.io/2020/11/18/testthat-utility-belt/ #rstats #testing #unittest #testthat

{testthat} is great for automatic testing. Here are some tricks for the heavy user: https://blog.r-hub.io/2020/11/18/testthat-utility-belt/ #rstats #testing #unittest #testthat

Its good to have many tests in your R package, but it can be a pain to debug some failing tests when it happens. {lazytest} for the rescue: only rerun the failing tests, until they pass: https://lazytest.cynkra.com/ #RStats #testthat #lazy

Its good to have many tests in your R package, but it can be a pain to debug some failing tests when it happens. {lazytest} for the rescue: only rerun the failing tests, until they pass: https://lazytest.cynkra.com/ #RStats #testthat #lazy

Is it just me, or does {bit64} and {testthat} behave unexpectdly?

In this example, {testthat} thinks that these two bit64::integer64 values are the same, and does not throw an error?

A reproduction on someone else's machine would be welcomed!

Is it because {testthat} is doing a comparison of the underlying floating point value, rather than using bit64's idea of equality?

#RStats #testthat #bit

{tetthat} is great for automatic testing. Here are some tricks for the heavy user: https://blog.r-hub.io/2020/11/18/testthat-utility-belt/ #rstats #testing #unittest #testthat