The launch or official opening or whatever it was of the Open Data Institute this week provided another chance to grab a snapshot of notable folk in the community, as for example demonstrated by people commonly followed by users of the #ODIlaunch hashtag on Twitter. The PR campaign also resulted in the appearance of some 
Today’s example is a little odd, in that the code isn’t pretty and the example isn’t really something you’d actually produce in real life — but if you’ll overlook those oddities, you’ll find that the spsample(...
The advent of municipal open data initiatives has been both a blessing and curse for my particular brand of data nerd. On one hand, it has opened up the possibility of developing deep and useful knowledge about the places we...
I have found that I get data from many different sources. These sources range from simple .csv files to more complex relational databases, to structure XML or JSON files. I have compiled the different approaches that one can use to easily access these datasets. Local Column Delimited Files This is probably the most common and
As you probably know, I am one of the strongest proponents of the Shiny package for developing interactive web applications Amongst the latest news from RStudio is that what was planned to be commercial software will now be free and Open Source (AGPLv3 license) To celebrate this momentous announcement, I have produced an Earthquake app.
This is a brief example using the maps in R and to highlight a source of data. This is real-time data and it comes from the U.S. Geological Society. This shows the location of earthquakes with magnitude of at least 1.0 in the lower 48 states. library(maps) library(maptools) library(rgdal) eq = read.table(file="http://earthquake.usgs.gov/earthquakes/catalogs/eqs7day-M1.txt", fill=TRUE, sep=",", header=T) plot.new()
NJ Hurricane Sandy Landfall Data These data come from the National Climatic Data Center (NCDC). Using the above link will download all of the data collected by the NCDC on the day of Hurricane Sandy. The data can also be obtained directly from the source at http://cdo.ncdc.noaa.gov/qclcd/QCLCD. The purpose of this post is not a discussion
My company, Kwelia, is sitting on mountains of data, so I decided to try my hand at mapping. I have played around with JGR but it’s just too buggy, at least on my mac, so I went looking for other alternatives and found a good write up here. I decided on mapping prices per sqft 
The MODIS package for R For people who work in GIS in R there has been a bit of challenge in working with data from Modis. I’ll use an example from my own work on UHI to illustrate. A while back I decided that I wanted to look at albedo data from MODIS. If you 
One of the most popular posts on this blog is the very first one, solving the issue of mapping certain ranges of values to particular colors in heatmaps. Given the abundance of ggplot2 usage in R plotting, I thought I’d … Continue reading →
Zero Inflated Models and Generalized Linear Mixed Models with R.
Zuur, Saveliev, Ieno (2012).
