I don't do much GIS but I like to. It's rather enjoyable and involves a tremendous skill set. Often you will find your self grabbing data sets from some site, scraping, data cleaning and reshaping, and graphing. On the ride … Continue reading →

R news and tutorials contributed by (552) R bloggers

During our last webinar, we covered some of the basic ideas behind ggplot2, the R Visualization package by Dr. Hadley Wickham. In this blog post I will walk through the example that I covered during the webinar. In order to carry out the examples yourself, you may download the dummy datasets from this link Creating

ggplot Tutorial I liked the following ggplot2 tutorial which is featured in Gabriela de Queiroz’s blog called unbiasedestimator. The tutorial looks very neatly presented and I’m sure that it will be very helpful to anyone just getting started with ggplot2 before they jump into ggplot2: Elegant Graphics for Data Analysis by Hadley Wickham or R Graphics Cookbook by...

or, Is the Seattle Mariners outfield a disaster?The BackstoryEarlier this week (2013-06-10), a blog post by Dave Cameron appeared at USS Mariner under the title “Maybe It's Time For Dustin Ackley To Play Some Outfield”. In the first paragraph, Cameron describes to the Seattle Mariners outfield this season as “a complete disaster” and Raul Ibanez as...

Occasionally I find myself wanting to draw several regression lines on the same plot, and of course ggplot2 has convenient facilities for this. As usual, don’t expect anything profound from this post, just a quick tip! There are several reasons we might end up with a table of regression coefficients connecting two variables in different

In a recent tutorial in the eLife journal, Huang, Rattner, Liu & Nathans suggested that researchers who draw scatterplots should start providing not one but three regression lines. I quote, Plotting both regression lines gives a fuller picture of the data, and comparing their slopes provides a simple graphical assessment of the correlation coefficient. Plotting

A common way of illustrating the idea behind statistical power in null hypothesis significance testing, is by plotting the sampling distributions of the null hypothesis and the alternative hypothesis. Typically, these illustrations highlight the regions that correspond to making a type II error, type I error and correctly rejecting the null hypothesis (i.e. the test's power). In this post...

In this post I show some different examples of how to work with map projections and how to plot the maps using ggplot. Many maps that are using the default projection are shown in the longlat-format, which is far from optimal. Here I show how to use either the Robinson or Winkel Tripel projection. Read more

A client has a specific audit they perform quarterly across 200 of their manufacturing plants. The audit has 8 distinct sections examining the different areas of the plant (shipping, receiving, storage, packaging,etc.) Instead of having one cumulative final score, the audit displays a final score for each section. I wanted to examine the distribution of