Using R — Working with Geospatial Data (and ggplot2)

This is a follow-up blog-post to an earlier introductory post by Steven Brey: Using R: Working with Geospatial Data. In this post, we’ll learn how to plot geospatial data in ggplot2. Why might we want to do this? Well, it’s really about your personal taste. Some people are willing to forfeit the fine-grained control of base graphics in exchange for the elegance of a ggplot. The choice is entirely yours.   read more …

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Getting your Mac ready for netCDF

If you are working with weather or climate data or big 3-4D model datasets you will undoubtedly encounter netCDF. Unidata’s Network Common Data Format has been the workhorse data format for atmospheric and oceanographic modelers since the early 1990′s. With more and more scientists using Macs to get their work done, it is important to get them ready with the the necessary tools of the trade.  This post walks you through the steps for setting up a Mac to work with netCDF.   read more …

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Installing FFTW3 and the ‘fftw’ R package

If you have to compile code that uses Fourier Transforms you may wish to use the FFTW library which the authors describe thusly:

Our benchmarks, performed on on a variety of platforms, show that FFTW’s performance is typically superior to that of other publicly available FFT software, and is even competitive with vendor-tuned codes. In contrast to vendor-tuned codes, however, FFTW’s performance is portable: the same program will perform well on most architectures without modification. Hence the name, “FFTW,” which stands for the somewhat whimsical title of “Fastest Fourier Transform in the West.”

This post has some tips on compiling this code and the associated R package ‘fftw’.   read more …

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Installing NetCDF and R ‘ncdf’

If you work with large, gridded datasets you should probably be using NetCDF, the Network Common Data Form from Unidata:

NetCDF is a set of software libraries and self-describing, machine-independent data formats that support the creation, access, and sharing of array-oriented scientific data.

Lots of high-end analysis software can be made to support NetCDF and it is indispensable for working with gridded datasets that weigh in at tens of gigabytes or more. This brief post describes the easiest way to install the NetCDF libraries and the R ‘ncdf’ package on our favorite systems: CentOS, Ubuntu and Mac OSX.   read more …

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Running Ubuntu 12.04 on an iMac

I’ve been very happy with my late 2009, 27″ iMac. I recently maxed out the memory at 16 GB but it still runs great for the kind of scientific programming and data visualization I do. Because of this machine I have been able to maintain my vanity for a few years longer by not wearing reading glasses. (Just don’t ask about font sizes.)

Recently I had to add a virtual machine for Ubuntu 12.04 to stay in sync with a client’s operating system. In this post I will cover the simple steps needed to run Ubuntu 12.04 as a virtual machine on your iMac.   read more …

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