All posts (Page 71 of 120)
Remember our recent post showing that one can wrangle Markdown files
programmatically without regex? That
tech note showed how to convert Markdown bodies to XML in order to
extract information from them. Now, this post goes one step further and
presents tinkr, a package for
converting .md and .Rmd files to XML, editing them, and… writing them
back as Markdown!
🔗
General tinkr workflow
The goal of tinkr is to convert Markdown files to XML and back to
allow their editing with xml2 (XPath!) instead of numerous complicated
regular expressions. The XML represents the full Markdown syntax tree
(or AST). If new
to XPath refer to this great
intro. The package
offers two functions, to_xml() and to_md().
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).
We will focus on Somalia and Kenya, two of the hardest hit countries.
We’ll also lean on another rOpenSci package, rnoaa, to link precipitation to soil moisture....
You can find members of the rOpenSci team at various meetings and workshops around the world. Come say ‘hi’, learn about how our software packages can enable your research, or about our process for open peer software review and onboarding, how you can get connected with the community or tell us how we can help you do open and reproducible research....
Sharing data sets for collaboration or publication has always been challenging, but it’s become increasingly problematic as complex and high dimensional data sets have become ubiquitous in the life sciences. Studies are large and time consuming; data collection takes time, data analysis is a moving target, as is the software used to carry it out.
In the vaccine space (where I work) we analyze collections of high-dimensional immunological data sets from a variety of different technologies (RNA sequencing, cytometry, multiplexed antibody binding, and others). These data often arise from clinical trials and can involve tens to hundreds of subjects. The data are analyzed by teams of researchers with a diverse variety of goals.
...In the second post of the series where we obtained data from eBird we determined what birds were observed in the county of Constance, and we complemented this knowledge with some taxonomic and trait information in the fourth post of the series. Now, we could be curious about the occurrence of these birds in scientific work. In this post, we will query the scientific literature and an open scientific data repository for species names: what have these birds been studied for? Read on if you want to learn how to use R packages allowing to do so!...
