Pairs plots (section 5.1.17) are a useful way of displaying the pairwise relations between variables in a dataset. But the default display is unsatisfactory when the variables aren't all continuous. In this entry, we discuss ways to improve these dis...
This is my first blog since joiningR-bloggers. I’m quite excited to be part of this group and apologize if I boreany experienced R users with my basic blogs for learning R or offendprogrammers with my inefficient, sloppy coding. Hopefully writing for...
This is my first blog since joining R-bloggers. I’m quite excited to be part of this group and apologize if I bore any experienced R users with my basic blogs for learning R or offend programmers with my inefficient, sloppy … Continue reading →
Here’s a bit of code used to produce one of the figures in my recent paper dealing with modeling rocky intertidal snail body temperatures. This was my first foray into ggplot2, and it only involved a few hours of head-scratching. The plot is a co...
The scatterplot is one of the most ubiquitous, and useful graphics. It's also very basic. One of its shortcomings is that it can hide important aspects of the marginal distributions of the two variables. To address this weakness, you can add a histo...
In this post, I’ll be graphing some results from a recent referendum held here in the UK and combining it with the results of a set of local elections that were held at the same time. I’ll give some examples of graphing stuff using ggplot2 and will also show some info regarding merging datasets. At
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
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', 
(The image above is called a “Beeswarm Boxplot” , the code for producing this image is provided at the end of this post) The above plot is implemented under different names in different softwares. This “Scatter Dot Beeswarm Box Violin – plot” (in the lack of an agreed upon term) is a one-dimensional scatter plot
This is the 11th post in a series attempting to recreate the figures in Lattice: Multivariate Data Visualization with R (R code available here) with ggplot2.
Previous parts in this series: Part 1, Part 2, Part 3, Part 4, Part 5, Part 6, Part 7, Part 8, Part 9, Part 10.
Chapter 11 – Manipulating the “trellis” 
Zero Inflated Models and Generalized Linear Mixed Models with R.
Zuur, Saveliev, Ieno (2012).
