pubchunks: extract parts of scholarly XML articles

October 16, 2018

By: Scott Chamberlain

pubchunks is a package grown out of the fulltext package. fulltext provides a single interface to many sources of full text scholarly articles. As part of the user flow in fulltext there is an extraction step where fulltext::chunks() pulls parts of articles out of XML format article files. As part of making fulltext more maintainable and focused on simply fetching articles, and realizing that pulling out bits of structured XML files is a more general problem, we broke out pubchunks into a separate package.

Parsing Metadata with R - A Package Story

October 9, 2018

By: Thomas Klebel

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.

The av Package: Production Quality Video in R

October 6, 2018

By: Jeroen Ooms

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.

outcomerate: Transparent Communication of Quality in Social Surveys

October 2, 2018

By: Rafael Pilliard Hellwig

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.

Mapping the 2018 East Africa floods from space with smapr

September 25, 2018

By: Max Joseph

Hundreds of thousands of people in east Africa have been displaced and hundreds have died as a result of torrential rains which ended a drought but saturated soils and engorged rivers, resulting in extreme flooding in 2018. This post will explore these events using the R package smapr, which provides access to global satellite-derived soil moisture data collected by the NASA Soil Moisture Active-Passive (SMAP) mission and abstracts away some of the complexity associated with finding, acquiring, and working with the HDF5 files that contain the observations (shout out to Laura DeCicco and Marco Sciaini for reviewing smapr, and Noam Ross for editing in the rOpenSci onboarding process).

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