Following up on Stefanie’s recap of unconf 17, we are following up this entire week with summaries of projects developed at the event. We plan to highlight 4-5 projects each day, with detailed posts from a handful of teams to follow.skimr Summary: skimr, a package inspired by Hadley Wickham’s precis package, aims to provide summary statistics iteratively and interactively as part of a pipeline.
We held our 4th annual unconference in Los Angeles, May 25-26, 2017. Scientists, R-software users and developers, and open data enthusiasts from academia, industry, government, and non-profits came together for two days to hack on projects they dreamed up and to give our online community an opportunity to connect in-person. The result?
As all other types of visualization, linguistic mapping has two main goals: data presentation and data analysis.
On 21-22 April, the London School of Economics hosted the Text Analysis Package Developers’ Workshop , a two-day event held in London that brought together developers of R packages for working with text and text-related data.
You can find members of the rOpenSci team at various meetings and workshops around the world. Come say ‘hi’, learn about how our packages can enable your research, or about our onboarding process for contributing new packages, discuss software sustainability or tell us how we can help you do open and reproducible research.Where’s rOpenSci?
There’s a lot of work that goes in to making software: the code that does the thing itself, unit testing, examples, tutorials, documentation, and support. rOpenSci software is created and maintained both by our staff and by our (awesome) community. In keeping with our aim to build capacity of software users and developers, three interns from our academic home at UC Berkeley are now working with us as well.
randgeo generates random points and shapes in GeoJSON and WKT formats foruse in examples, teaching, or statistical applications. Points and shapes are generated in the long/lat coordinate system and withappropriate spherical geometry; random points are distributed evenly acrossthe globe, and random shapes are sized according to a maximum great-circledistance from the center of the shape.
There is no problem in science quite as frustrating as other peoples’ data . Whether it’s malformed spreadsheets, disorganized documents, proprietary file formats, data without metadata, or any other data scenario created by someone else, scientists have taken to Twitter to complain about it. As a political scientist who regularly encounters so-called “open data” in PDFs, this problem is particularly irritating.
Version 2.0 of my data set validation package assertr hit CRAN just this weekend. It has some pretty great improvements over version 1. For those new to the package, what follows is a short and new introduction. For those who are already using assertr, the text below will point out the improvements. I can (and have) go on and on about the treachery of messy/bad datasets.
As a scientist who models plant diseases, I use a lot of weather data. Often this data is not available for areas of interest. Previously, I worked with the International Rice Research Institute (IRRI) and often the countries I was working with did not have weather data available or I was working on a large area covering several countries and needed a single source of data to work from.
