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Test coverage of the 10 most downloaded R packages

May 2, 2014
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Test coverage of the 10 most downloaded R packages

Test coverage of the 10 most downloaded R packages 2014-04-30 Source Introduction How do you know that your code is well tested ? The test coverage is the proportion of source code lines that are executed (covered) when running the tests. It is useful to find the parts of your code that are no exercised no matter how...

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An example of monkey patching a package

August 1, 2013
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An example of monkey patching a package

An example of monkey patching a package 2013-07-11 Source Scope This article is about R package development. Motivation In the same spirit that my previous post A dirty hack for importing packages that use Depends , I wanted to use an earlier version of the excellent gdata package in one of my packages, but as an Import instead of...

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A dirty hack for importing packages that use Depends

July 31, 2013
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A dirty hack for importing packages that use Depends

A dirty hack for importing packages that use Depends 2013-05-27 Source Scope This article is about R package development. Motivation As stated in the the Writing R Extensions manual and the Software for Data Analysis book (aka the R bible), packages should whenever possible use Imports instead of Depends, to avoid name collision (masking) and ensure trustworthy computations. See

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