Recently I was working on a data visualization project. I wanted to visualize summary statistics by category of the data. Specifically I wanted to see a simple dispersion of data with confidence intervals for each category of data. R i...
Some time over the past 6 weeks I randomly saw a tweet announcing the “Data Scientist Summit” and shortly below it I saw that it would be held in Las Vegas at the Venetian. Being a Data Scientist myself is reason enough to not pass up this opportunity, but Vegas definitely sweetens the deal! On Wednesday I woke up...
In Tangente n⁰42, there was a dataset about the size of cuckoo eggs against the species (goldcrest and warbler) which built the nest. (The whole dataset from Latter is analysed in Maindonald and Braun’s Data Analysis and Graphics Using R, with a degree of caution about how trustworthy this data is…) This is 
So I previously took a look at some data of player performance from a computer game. In this post, I’m going to do some further visualisations using ggplot2. The data consists of different types of player character, different roles for those characters, and their overall damage output (the unit here is damage per second, or
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 