Last year I showed you how to create manhattan plots, and later how to highlight regions of interest, using ggplot2 in R. The code was slow, required a lot of memory, and was difficult to maintain and modify.I finally found time to rewrite the code usi...
For a project I’m working on at work, I’m building a predictive model that categorizes something (I can’t tell you what) into two bins. There is a default bin that 95% of the things belong to and a bin that the business cares a lot about, containing 5% of the things. Some readers may be
Yesterday Alasdair Allan and Pete Warden shocked the world by revealing that iPhones and iPads have been keeping track of our every move, and saving the data in obfuscated back up files. As my friend Vince Buffalo mentioned on Twitter, part of me was disgusted by the secret stalking Steve Jobs was doing, but my
What are the alpha shapes?: “The concept of alpha shapes formalizes the intuitive notion of “shape” for spatial point set data, which occurs frequently in the computational sciences. An alpha shape is a concrete geometric object that is uniquely defined for a particular point set. Alpha shapes are generalizations of the convex hull. Given a 
The Atmospheric Science Data Center (ASDC) at NASA Langley Research Center offers several data sources. For example, it is possible to download a text file with the 22-year (July 1983 – June 2005) monthly and annual average of global horizontal irradiation. nasafile <- 'http://eosweb.larc.nasa.gov/sse/global/text/global_radiation' nasa <- read.table(file=nasafile, skip=13, header=TRUE) With this data, R and the 
A violin plot is a combination of a boxplot and a kernel density plot. Lattice includes the panel.violin function for this graphical tool. This example draws a violin and a boxplot together. First, let’s download some solar radiation data from the NASA webpage: nasafile <- 'http://eosweb.larc.nasa.gov/sse/global/text/global_radiation' nasa <- read.table(file=nasafile, skip=13, header=TRUE) Now, I plot a 
The version 0.22 of solaR includes a new method, mergesolaR. It is designed to merge daily time series of several solaR objects. For example, we can obtain the daily irradiation of the whole set of meteorological stations of Madrid (Spain) and use this information to calculate the productivity of a grid connected PV system. It 
In lattice, there is a function called splom for the display of scatter plot matrices. For large datasets, the panel.hexbinplot from the hexbin package is a better option than the default panel. As an example, let’s use some meteorological data from MAPA-SIAR: library(solaR) library(hexbin) aranjuez <- readMAPA(prov=28, est=3, start='01/01/2004', end='31/12/2010') aranjuezDF <- subset(as.data.frame(getData(aranjuez)), select=c('TempMedia', 'TempMax', 
I am a regular reader of the FlowingData blog by Nathan Yau. It is an excellent reference for anyone interested in statistical visualization of data. One of his posts that caught my attention was a visualization of the growth of Walmart in the US. Given my research interests in retail, it was a fascinating insight 
Last month, Amanda Cox from The New York Times Graphic Department gave a great talk to the NYC R Statistical Programming Meetup. I’ve just got around to uploading the video, which has been broken into a part one and part two. You can also view the videos embedded after the jump.
Amanda made use of
Zero Inflated Models and Generalized Linear Mixed Models with R.
Zuur, Saveliev, Ieno (2012).
