Before everybody made their way to the unconf via LAX and Lyft, attendees discussed potential project ideas online. The packagemetrics package was our answer to two related issues. The first proposal centered on creating and formatting tables in a reproducible workflow.

Two years ago at #runconf15, there was a great discussion about best practices for organizing R-based analysis projects that yielded a nice guidance document describing research compendia . Compendia, as we described them, were minimal products of reproducible research, using parts of R package structure to organize the inputs, analyses, and outputs of research projects.

Textual data and natural language processing are still a niche domain within the R ecosytstem.

When Timm, Laura, Elber and I first ran the @everytreenyc Twitter bot almost a year ago, we knew that it wasn’t actually sampling from a list that included every street tree in New York City. The Parks Department’s 2015 Tree Census was a huge undertaking, and was not complete by the time they organized the Trees Count! Data Jam last June. There were large chunks of the city missing, particularly in Southern and Eastern Queens.

Since July 2016 I have been working as Associate Application Systems in the Teaching and Learning Applications group at Columbia University. I have developed several apps, including this Photo Roster, an LTI plugin to the Canvas Learning Management System. The back end of the Photo Roster is written in Python and Flask.

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.

camsRad is a lightweight R client for the CAMS Radiation Service, that provides satellite-based time series of solar irradiation for the actual weather conditions as well as for clear-sky conditions. Satellite-based solar irradiation data have been around roughly as long our modern era satellites. But the price tag has been very high, in the range of several thousand euros per site.