Often you want to set the fixed colors for particular range of your dataset to be sure that the visual output is correctly represented. This is particularly useful for time series data, where the range or your dataset might drastically … Continue reading →
As I was getting a root canal last week, my dental X-Rays reminded me anew of an optical illusion that stumped us for a short time recently when we were developing our heatmapping engine.My X-Rays, before during and after a recent root canal. The...
Happy Thanksgiving, everyone. Earlier today, I found an interesting post from Bo Allen on pseudo-random vs random numbers, where the author uses a simple bitmap (heat map) to show that the rand function in PHP has a systematic pattern and compares these to truly random numbers obtained from random.org. The post’s results suggest that pseudo-randomness in PHP is
Earlier, I found an interesting post from Bo Allen on pseudo-random vs random numbers, where the author uses a simple bitmap (heat map) to show that the rand function in PHP has a systematic pattern and compares these to truly random numbers obtained from random.org. The post’s results suggest that pseudo-randomness in
I have become quite a big fan of graphics that combine the features of traditional figures (e.g. bar charts, histograms, etc.) with tables. That is, the combination of numerical results with a visual representation has been quite useful for exploring descriptive statistics. I have wrapped two of my favorites (build around ggplot2) and included them as part
I have become quite a big fan of graphics that combine the features of traditional figures (e.g. bar charts, histograms, etc.) with tables. That is, the combination of numerical results with a visual representation has been quite useful for exploring...
I have become quite a big fan of graphics that combine the features of traditional figures (e.g. bar charts, histograms, etc.) with tables. That is, the combination of numerical results with a visual representation has been quite useful for exploring...
Time Series as calendar heat maps + All of my running data since April 1, 2009 = Generated by the following code: #Sample Code based on example program at: source(file = "calendarHeat.R") run<- read.csv("log.csv", header = TRUE, sep=",") sum(run$Distance) date <- c() for (i in 1: dim(run)){ if(run$DistanceUnit== 'Kilometer'){ miles <- c(miles,run$Distance * 0.62) }
I've been getting a lot of questions about the method used to map the hotspots in the seasonal drunk-driving risk maps. It uses the GAM (Geographical Analysis Machine), a way of detecting spatial clusters from two data inputs: the data of interes...
Now, that the 2011 F1 season is over I decided to quickly scrub the Formula 1 data of the F1.com website, such as the list of drivers, ordered by the approximate amount of salary driver is getting (top list driver is making the most, approx. 30MM) and position at the end of each race. There 
Zero Inflated Models and Generalized Linear Mixed Models with R.
Zuur, Saveliev, Ieno (2012).
