Review: Kölner R Meeting 12 April 2013

Our 5th Cologne R user group meeting was the best attended meeting so far, with 20 members finding their way to the Institute of Sociology for two talks by Diego de Castillo on shiny and Stephan Holtmeier on cluster analysis, followed by beer and schnitzel at the Lux, a gastropub nearby.

Shiny

Diego gave an overview of the design principles behind shiny, which provides a powerful API to build web apps in pure R. His explanation of the reactive programming model was particularly helpful to understand how shiny works under the hood and why it is so responsive. His live demonstrations of shiny even included shiny server, which he had running in a virtual machine. Diego's slides are available via our Meetup site.

Diego de Castillo: Introduction to shiny

You can hear more from Diego and me at the UseR!2013 conference in Albacete, where we will give a googleVis tutorial. We will touch on googleVis on shiny as well. A dedicated shiny tutorial will be given in the afternoon by Josh and Winston from RStudio.

Cluster analysis

Stephan Holtmeier, who is a psychologist by background, presented an introduction to cluster analysis with R, motivated by his work in analysing survey data. As a toy example he used a 360° feedback survey of a group of managers within a big company. In his example he wanted to understand the profile of those managers better. Stephan illustrated how a cluster analysis can help to identify groups of managers with similar strengths, e.g. for communication, leadership and/or performance. Depending on how he measured the distance between managers he could look for people who have similar levels of competency or a similar profile (correlation). Stephan also touched on the differences between hierarchical and centroid based cluster analysis, such as k-means. You can find Stephan's slides (in German) also on our Meetup site.

Stephan Holtmeier: Cluster Analysis with R

For more information on cluster analysis functions in R see also the cluster task view on CRAN. If you would like to get an overview of how psychologists look at data, then check out William Revelle's vignette of the psych package. Finally, if you are interested in how a k-means cluster analysis can be used for image manipulation, see an earlier post of mine.

Next Kölner R meeting, 19 July 2013

The next meeting has been scheduled for 19 July. Günter Faes will present his experiences using the XLConnect package as an interface between R and Excel. Dietmar Janetzko agreed to present how he used R and Twitter to predict exchange rate movements. Of course, the evening will close with a few Kölsch in a nearby beer-garden.

Please get in touch if you would like to present and share your experience, or indeed if you have a request for a topic you would like to hear more about. For more details see also our Meetup page.

Thanks again to Bernd Weiß for hosting the event and Revolution Analytics for their sponsorship.

Now I see it! K-means cluster analysis in R

Of course, a picture on a computer monitor is a coloured plot of x and y coordinates or pixels. Still, I was smitten by David Sparks' posts on is.r(), where he shows how easy it is to read images into R to analyse them. In two posts [1], [2] he replicates functionality of image manipulation programmes like GIMP.

I can't resist to write about this here as well. David's first post is about k-means cluster analysis. One of the popular algorithms for k-means is Lloyd's algorithm. So, on that note I will use a picture of the Lloyd's of London building to play around with David's code, despite the fact that the two Lloyds have nothing to do with each other. Lloyd's provides pictures of its building copyright free on its web site. However, I will use a reduced file size version hosted on wikimedia.

The ReadImages package by Markus Löcher [3] allows me to load a jpeg-file into R. The R object of the images is an array, which has the structure of three layered matrices, representing the value of the colours red, green and blue for each x and y coordinate. I convert the array into a data frame, as this is an accepted structure by k-means and plot the data.

library("ReadImages")
url <- "http://upload.wikimedia.org/wikipedia/commons/6/6a/6414A_1_copy.jpg"
fn <- tempfile()
download.file(url, destfile=fn)
readImage <- read.jpeg(fn)

dm <- dim(readImage)
rgbImage <- data.frame(
                    x=rep(1:dm[2], each=dm[1]),
                    y=rep(dm[1]:1, dm[2]),
                    r.value=as.vector(readImage[,,1]),
                    g.value=as.vector(readImage[,,2]),
                    b.value=as.vector(readImage[,,3]))

plot(y ~ x, data=rgbImage, main="Lloyd's building",
     col = rgb(rgbImage[c("r.value", "g.value", "b.value")]), 
     asp = 1, pch = ".")

Running a k-means analysis on the three colour columns in my data frame allows me to reduce the picture to k colours. The output gives me for each x and y coordinate the colour cluster it belongs to. Thus, I plot my picture again, but replace the original colours with the cluster colours.Read more »