All posts (Page 55 of 120)
Testing is a crucial component to any software package. Testing makes sure that your code does what you expect it to do; and importantly, makes it safer to make changes moving forward because a good test suite will tell you if a change has broken existing functionality. Our recent community call on testing is a nice place to get started with testing.
One way to make testing even harder is through including HTTP requests. This adds complexity for many reasons:
...As of earlier this year, we are now automatically building binaries and pkgdown documentation for all rOpenSci packages. One issue we encountered is that some packages include vignettes that require some special tools/data/credentials, which are unavailable on generic build servers.
This post explains how to include such vignettes and articles in your package.
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On package vignettes
By default, R automatically recreates vignettes during R CMD check or when generating pkgdown sites by running all R code. This is useful because it provides some extra testing of your code and ensures that documentation is reproducible. However, sometimes it is not a good idea to run the code on every build server, every time. For example:
We’re thrilled to be introducing a new member of our team. Mark Padgham has joined rOpenSci as a Software Research Scientist working full-time from Münster, Germany. Mark will play a key role in research and development of statistical software standards and expanding our efforts in software peer review, enabled by new funding from the Sloan Foundation. He will work closely with Noam Ross, rOpenSci Leadership team member, and Scientist at EcoHealth Alliance and Karthik Ram, rOpenSci Project Lead....
The United States Deparment of Agriculture National Agricultural Statistics Service (USDA-NASS) provides a wide range of agricultural data that includes animal, crop, demographic, economic, and environmental measures across a number of geographies and time periods. This data is available by direct download or queriable via the Quick Stats interface. While the Quick Stats tool puts a large amount of data into the hands of users, the interface can be frustrating, especially when trying to access more than 50,000 records or hoping to automate downloading data when new data is released. I developed rnassqs as a solution to these frustrations. rnassqs provides a simple R interface for the Quick Stats API. By iterating over a set of parameters, R users can make processing NASS data reproducible and automated....
rOpenSci thrives because of volunteer contributions from community members - submitting and reviewing R packages, serving as editors for software peer review, writing blog posts, sharing information about packages and resources, contributing code and documentation and answering others’ questions. Recently our fiscal sponsor, NumFOCUS, gave us an opportunity to nominate two contributors for recognition at the NumFOCUS annual summit. Sometimes all we can do is publicly express our gratitude for the people who help make our software robust and sustainable, and make our community a welcoming place that adds value to people’s experiences....
