Mapping Flows in R … with data.table and lattice

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data.table, lattice, R, spatial data

Some days ago James Cheshire published the post Mapping Flows in R. I have implemented an alternative (faster) version using data.table to read and join the datasets (and lattice to display the results). If you are new to data.table you should read this wiki and this cheatsheet.

This is the code:

This is the result:

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meteoForecast 0.43: GFS, NAM, and RAP included

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forecast, meteorological data, NWP, R, WRF

Some months ago I published the meteoForecast package, with functions to download data from the Meteogalicia and OpenMeteo NWP-WRF services. Now there is a new version available at CRAN. This update includes a bunch of bug fixes, some additional features, and three new services: GFS, NAM, and RAP.

You will find detailed information and examples here.

meteoForecast, a package to obtain NWP-WRF forecasts in R

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forecast, NWP, R, raster, spatial, WRF

The Weather Research and Forecasting (WRF) Model is a numerical weather prediction (NWP) system. NWP refers to the simulation and prediction of the atmosphere with a computer model, and WRF is a set of software for this.

meteoForecast is a new R package that implements functions to download data from the Meteogalicia and OpenMeteo NWP-WRF services using the NetCDF Subset Service.

Read this introduction for additional information and examples.

rasterVis tutorials

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GoogleMaps, raster, raster layers, rasterVis, spatial data, visualization

Agustin Lobo has recently published some good tutorials about rasterVis:

• Introduction to rasterVis
• Overlay your raster layer on a backround GoogleMaps layer
• Overlay vectors on raster layers
• Vectorplot

Regarding the second tutorial, there has been an interesting discussion in the R-sig-Geo mailing list about the projection of the ggmap output.

Writing a book with a little help from Emacs and friends

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emacs, latex, orgmode, R, reproducible research, visualization

This post provides technical details about the making of my book “Displaying time series, spatial, and space-time data with R”, a fully reproducible book including hundreds of code chunks and graphics. Hopefully this post will help others when writing a reproducible document.

Tools

Among the tools that can create reproducible documents with R, I decided to use these gems of open-source software:

• GNU Emacs as development environment.
• R (of course!) with Emacs Speaks Statistics.
• org-mode for authoring text and code.
• LaTeX with AUCTeX to produce the final document.