Archives for posts with tag: R

The idea of reproducibility in research is fundamental, and I’d like to share some links if you haven’t heard of reproducible research yet:

If you want to get into it then R is a great choice because it is already fit for reproducible research. Just go to CRAN and check out the task view on reproducible research.

The original idea behind sos4R was very much inspired by the chance to use near-real time information and still allow reproducibility. If you think about using sos4R but don’t have high experiences with OGC Sensor Observation Services (SOS), let me know – I’m looking for good use cases for demos and publication.

… says Forbes Magazine. I agree, but what does a magazine do with that assumption? From the blog post:

This post is part of an ambitious project to crowdsource the January issue of Forbes Magazine. It’s based on a suggestion to “Names You Need To Know” by one of our community members (Hat Tip, Kurt Grela). That R is rapidly augmenting or replacing other statistical analysis packages at universities, is being written about in The Register, The New York Times, and Forbes, and is exposing data analysis to millions of Do-It-Yourselfers makes R a Name You Need to Know in 2011.

I recommend to read the whole article. It makes a few interesting points, which are not new, but might give a good overview of what is going on (and where R is heading) for people new to R.

(Via Flowingdata)

The topic of today’s post looks ahead in the project a little bit. Within the 52° North Geostatistics Community we currently design a model for spatio-temporal data in R. In other words, how one can save and easily access and query information from a common data structure for data with both a spatial and a temporal component.

This naturally relates to the sos4R project, as it would be great to return exactly these classes as the result of a query to a sensor observation service. On the one side, I do not have to implement these classes alone, on the other side I can count on the R community to develop transformation functions from such a common data model into data structures that can be the basis for a manifold of analyses. I see these output formats as an important part for the acceptance of sos4R in the community.

Our efforts are at a very early stage, but I hope I will be able to contribute as much as I can. We started with an examination of the available R packages for time series and time formats and we think we can use some of them (even if only as orientation). Whether we want to be dependent on one of these packages or rather provide conversion functions is still part of the discussion.

You can check out the current status (not much at the moment despite a graphic with the class structure!) of the package sptX from the 52° North SVN here and participate via the geostatistics mailing list. The goal is to provide classes that are compatible with sp (class conversion etc.) and support various time series and spatial data formats, both regular as well as irregular kinds. sos4R will probably one of the first applications of this package.