In this post I will provide some examples of what has changed between rtweet 0.7.0 and rtweet 1.0.2.I hope both the changes and this guide will help all users.I highlight the most important and interesting changes in this blog post, and for a full list of changes you can consult it on the NEWS. Big breaking changes More consistent output This is probably what will affect the most users.All functions that return data about
These days web Application Programming Interfaces (APIs) are everywhere (scientific data sources, your system for Customer relationship management, cat facts API…). Do you need to write some R code wrapping a web resource such as an API? Packaging it up might be useful to you or your team for the same reason as any code.
terrainr version 0.4.0 is now on CRAN! This version is a relatively minor updatethat shouldn’t impact most workflows, but makes some changes to improve thelogic and consistency of the package. The rest of this post runs through thechanges you can expect if you update.packages any time soon!What’s a terrainr? terrainr is an R package for theretrieval and visualization of spatial data.
Introduction Few months ago, I embarked on a full stack spatial data project at work. The project kicked off amazingly, until I was almost backed to the wall when I discovered that some of the data sources were served via a GraphQL API. Before now, I haven’t worked with GraphQL. But, I have heard a lot about it and how amazing it is for querying data.
In October last year we wrote about the CRAN Checks API (https://cranchecks.info). Since then there have been four new major items introduced: documentation, notifications, search, and a new version of the cchecks R package.
Testing is a crucial component to any software package. Testing makes surethat your code does what you expect it to do; and importantly, makes it safer to makechanges moving forward because a good test suite will tell you if a change has brokenexisting functionality. Our recent community call on testing is a niceplace to get started with testing. One way to make testing even harder is through including HTTP requests.
The United States Deparment of Agriculture National AgriculturalStatistics Service (USDA-NASS) provides a wide range of agriculturaldata that includes animal, crop, demographic, economic, andenvironmental measures across a number of geographies and time periods.This data is available by direct download or queriable via theQuick Stats interface.
If you have an R package on CRAN, you probably know about CRAN checks. Each package on CRAN, that is not archived on CRAN 1 , has a checks page, like this one for ropenaq:https://cloud.r-project.org/web/checks/check_results_ropenaq.html The table above is results of running R CMD CHECK on the package on a combination of different operating systems, R versions and compilers.
There seem to be a lot of ways to write about your R package, and rather than haveto decide on what to focus on I thought I’d write a little bit about everything.To begin with I thought it best to describe what problem rdhs tries to solve,why it was developed and how I came to be involved in this project.
Introduction When I was in grad school at Emory, I had a favorite desk in the library. The desk wasn’t particularly cozy or private, but what it lacked in comfort it made up for in real estate. My books and I needed room to operate. Students of the ancient world require many tools, and when jumping between commentaries, lexicons, and interlinears, additional clutter is additional “friction”, i.e., lapses in thought due to frustration.
