All posts (Page 64 of 123)
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Introduction
ramlegacy is a new R package to download, cache and read in all the different versions of the RAM Legacy Stock Assessment Database, a public database containing stock assessment results of commercially exploited marine populations from around the world. The package accomplishes all this by:
- Providing a function
download_ramlegacy(), to download all the available versions of the RAM Database and cache them on the user’s computer in a location provided by the rappdirs package. This way once a version has been downloaded it doesn’t need to be re-downloaded for subsequent analysis. - Supporting reading in specified tables or all tables from a cached version of the database through the function
load_ramlegacy() - Providing a function
ram_dir()to view the path of the rappdirs location where the downloaded database was cached.
The primary motivation behind developing the caching behavior of the package was to save the user time and effort spent in re-downloading different versions of the database as part of any future analysis.
...We strive for high quality in our suite of packages, in practice via a system of software peer review, and via packaging guidelines that keep growing. There is therefore a risk of increasing the workload of package authors, who already have a lot on their plate. To avoid that, when explaining how to do things in our dev guide, we recommend existing automated tools to authors.
Inspired by the usethis package, we’ve started work on our specific helpers for rOpenSci package authors, rodev. In this note, we’ll present some of the helpers it contains at the moment, and ask for your feedback as an rOpenSci package author.
...As announced in our recent post about updates to our Software Peer Review system, all our package development, review and maintenance is available as an online book. Our goal is to update it approximately quarterly so it’s already time to present its second official version! You can read the changelog or this blog post to find out what’s new in our dev guide 0.2.0!
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A more legit and accessible book
Let’s start with very exciting news, the dev guide now has a cover, designed by Oz Locke from Locke Creatives!
...NASA generates and provides heaps of data to the scientific community. Not all of it is looking out at the stars. Some of it is looking back at us here on Earth. NASA’s Earth science program observes, understands and models the Earth system1. We can use these data to discover how our Earth is changing, to better predict change, and to understand the consequences for life on Earth.
The Earth science program includes the Prediction of Worldwide Energy Resource (POWER) project, which was initiated to improve upon the current renewable energy data set and to create new data sets from new satellite systems. The POWER project targets three user communities: 1) Renewable Energy (SSE), 2) Sustainable Buildings (SB) and 3) Agroclimatology (AG)1 and covers 140+ different parameters.
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Introduction
Open Trade Statistics (OTS) was created with the intention to lower the barrier to working with international economic trade data. It includes a public API, a dashboard, and an R package for data retrieval.
The project started when I was affected by the fact that many Latin American Universities have limited or no access to the United Nations Commodity Trade Statistics Database (UN COMTRADE).
There are alternatives to COMTRADE, for example the Base Pour L’Analyse du Commerce International (BACI) constitutes an improvement over COMTRADE as it is constructed using the raw data and a method that reconciles the declarations of the exporter and the importer. The main problem with BACI is that you need UN COMTRADE institutional access to download their datasets.
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