1. Presenting Multivariate Data Harry R. Erwin, PhD School of Computing and Technology University of Sunderland

2. Resources Everitt, BS, and G Dunn (2001) Applied Multivariate Data Analysis, London: Arnold. Everitt, BS (2005) An R and S-PLUS® Companion to Multivariate Analysis, London: Springer Tukey’s seminal paper: – Tufte’s work: –http://en.wikipedia.org/wiki/Edward_Tuftehttp://en.wikipedia.org/wiki/Edward_Tufte Murrell, P, (2006) R Graphics, Florida: Chapman & Hall/CRC.

3. Do not lie about the data. How do people lie? –By presenting data selectively –By distorting the visual representation of the data –By failing to extrapolate scales from one portion of the image to another. –By changing scales –By inflating vertical scales –By failing to show the zero point or 100% point of an axis. –By representing linear data by area –By representing areal data linearly –By omitting data

4. Five Principles Above all else, show the data Maximize the data/ink ratio Erase non-data ink Erase redundant data ink Revise and edit freely

5. Avoid Chartjunk Avoid moiré effects—shimmering Mute the grid Dump the duck—avoid self-promoting graphics

6. A Laundry-List of Plotting Commands in R Standard scatterplot commands –plot(dataset) # for two-column data –text(xval,yval,TextString) barplot() produces barplots. hist() produces histograms boxplot() produces boxplots pie() produces piecharts pairs() produces a pairs diagramme

7. What can you plot? The first argument(s) to any plot command is (are) very flexible –A dataframe –A pair of vectors –A relationship (pressure~temperature, data=pressure) –A model

8. Adding details to a plot text(locx, locy, TextString) points(vecx,vecy) to add points lines(vecx,vecy) to draw connecting lines matplot() par() to customise the graphics axis() to add an axis grid() to add a grid abline() to add a line to the plot arrows() to add arrows to the plot mtext() to add marginal text title() to add a title legend() to add a legend

9. Trellis Graphics package(lattice) # creates objects of class trellis Developed by Deepayan Sarkar Generates complete plots Operates just like traditional graphics, but optimised for us based on Bill Cleveland’s recommendations. Ensure accurate and faithful communication of information Supports ‘multi-panel conditioning’, which will be very useful.

10. The Lattice Graphics Model tplot<-xyplot(lat~long, data=quakes, pch=“.”) print(tplot) tplot2<-update(tplot, main=“Earthquakes in the Pacific Ocean (since 1964)”) Use the trellis.device() function instead of par()

11. Trellis Plots Trellis –barchart() –bwplot() –densityplot() –dotplot() –histogram() –qqmath() –stripplot() Standard –barplot() –boxplot() –nil –dotchart() –hist() –qqnorm() –stripchart()

12. More Trellis Plots Trellis –qq() –xyplot() –levelplot() –contourplot() –cloud() –wireframe() –splom() –parallel() Standard –qqplot() –plot() –image() –contour() –nil –persp() –pairs() –nil

13. Multipanel Conditioning depthgroup<-equal.count(quakes$depth, number=3, overlap=0) xyplot(lat~long|depthgroup, data=quakes, pch=“.”)

14. Demonstrations