![]() There may be instances where you want to have your Git repository be private until work is complete or other information is made publicly available, and you still want to be able to use CI services. Travis CI docs on writing YAML CI files for R.To start off with I would recommend using Travis CI (it is the easiest to get up and running). To see the output of the build/install and testing of this repo in Travis click on the build status badge at the top of the README (also here: ). Testing with CI is a great way to know that your analysis code is working exactly as expected in a reproducible environment from installation all the way through execution as you develop, revise, and improve it. Automating testing alongside development with CIĪssuming that you're using Git to develop your analysis code then you can have a continuous integration service (such as Travis CI or CircleCI) automatically test your code in a fresh environment every time you push to GitHub. So I would encourage you to explore those as well. There are testing frameworks in R, most notably testthat, which I assume are good. For the purposes of this demo repository it is also important as it allows for writing tests that use papermill (papermill's execute_notebook is only accessible through the Python API). I am biased, but I continue to be impressed with how nimble, powerful, and easy it is to work with. Pytest is the most comprehensive and scalable testing framework that I know of. Want to learn more? Check out the Test and Code podcast hosted by Brian Okken. Additionally, your analysis becomes (by necessity) a well documented and transparent process. If your analysis code has unit tests and the analysis itself exists in an automatically testable pipeline then you and your colleagues should have more confidence in your analysis. There are numerous reasons to test your code, but as a scientist an obvious one is ensured reproducibility of experimental results. Once you read though and understand what the testing file is doing execute the tests with pytest in the top level directory of the repo by running Once you have installed pytest and done some minimal reading of the docs then create a tests directory and write your test files in Python inside of it.Īn example of some very simple tests using papermill is provided in tests/test_notebooks.py. ![]() ![]() If you installed Jupyter with Conda then you can also install pytest with Conda.To provide testing for Jupyter notebooks we can use pytest in combination with papermill. To use with Jupyter notebooks running in the IRkernel install the R bindings for papermill ( papermillR)Ī toy example of how to use papermill is demonstrated in the example Jupyter notebook.This allows you to use Jupyter notebooks as components of an automated data analysis pipeline or for procedurally testing variations. This means that you can use papermill to externally run, manipulate, and test Jupyter notebooks. Papermill is a tool for parameterizing, executing, and analyzing Jupyter Notebooks. This is one way in which you could setup the same packrat environment on a different machine from the Git repository. This can be done in R with the packrat library. The first step in any project should be making sure that the library dependencies are specified and reproducible. Install the R kernel for Jupyter ( IRkernel)Įnable package dependency management with packrat.If you aren't familiar with Python and pip then just follow the instructions for installing with Anaconda.Automating testing alongside development with CIīefore you can begin you need to make sure that you have the following in your working environment.Enable package dependency management with packrat.This repo is just what I know from being able to read code and understanding how Jupyter works.īefore learning how to setup R in Jupyter, first go check out how cool it is in Binder! Just click the "launch Binder" badge above. This repo was inspired from a Tweet in a discussion about Episode 7 of The Bayes Factor podcast.ĭisclaimer: I am a physicist and primarily a Python and C++ programmer and I don't use/know R. An example of how to use R in Jupyter notebooks and then make a Binder environment to run them interactively on the web.
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