All posts (Page 70 of 120)
Every R package has its story. Some packages are written by experts, some by
novices. Some are developed quickly, others were long in the making. This is the
story of jstor, a package which I developed during my time as a student of
sociology, working in a research project on the scientific elite within
sociology. Writing the package has taught me many things (more on that later)
and it is deeply gratifying to see, that others find the package useful....
Proper identification of individuals is crucial for acknowledging and studying their scientific work, be it journal articles or pieces of software. In this tech note, one year after CRAN started supporting ORCIDs, we shall explain why and how to use unique author identifiers in DESCRIPTION files.
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Why use ORCIDs on CRAN?
When analyzing the authorship of CRAN packages, one can look at authors’ names and email addresses. Names can be written with and without quotes, email addresses change, which makes it all tricky as noted by David Smith when he looked for the most prolific CRAN authors (notice our very own Scott Chamberlain and Jeroen Ooms in that scoreboard by the way?). Besides, several people can have the same name!
...At rOpenSci we are developing on a suite of packages that expose powerful graphics and imaging libraries in R. Our latest addition is av – a new package for working with audio/video based on the FFmpeg AV libraries. This ambitious new project will become the video counterpart of the magick package which we use for working with images.
install.packages("av")
av::av_demo()
The package can be installed directly from CRAN and includes a test function av_demo() which generates a demo video from random histograms.
Do you have code that accompanies a research project or manuscript? How do you review and archive that code before you submit a paper? Our next Community Call will present different perspectives on this hot topic, with plenty of time for Q&A.
- What’s the culture of the group around feedback and code collaboration?
- What are the use cases?
- What are some practices that can adopted?
🕘 Tuesday, October 16th, 9-10 AM PDT (find your timezone)
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Background
Surveys are ubiquitous in the social sciences, and the best of them are meticulously planned out. Statisticians often decide on a sample size based on a theoretical design, and then proceed to inflate this number to account for “sample losses”. This ensures that the desired sample size is achieved, even in the presence of non-response. Factors that reduce the pool of interviews include participant refusals, inability to contact respondents, deaths, and frame inaccuracies.1 The more non-response, the more a study becomes open to criticism about its veracity.
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