A couple of weeks ago Bob wrote about a post about a research *note* that was recently accepted to the iConference. In it we outline the beginnings of a research project where we look at the interaction of different media platforms (Twitter and Blogs) with more traditional sources. In this post I go through the R code we used to plot, and visually compare, the volume of different information sources.

The data for this example is randomly drawn along a Pareto distribution so anyone should be able to just open the file, run it and have plots. Like I did in the last R example, I have used comments in the code to explain what I’m doing in the creation of these plots. After the code I give a brief introduction on the tool I use to select colors.

^{?}Download download.R

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 | # # Synchronized volume over time with events for different sources # # used to adjust the alpha (transparency) of rgb colors below. library(VGAM) # set up some params for later use. I could have surfaced more plot # params but for this plot these are things I was fiddling with plot.type <- "h" text.adjust <- c(.5,.3) # left-right & up-down with .5 being center # this is volume of data flow over hours, so how many hours. Normally # you have this or will calculate it, but I am making random data num.hours <- 300 my.local.x <- 1:num.hours # points in time when events happened. These are entirely arbitrary. event.x.vec <- c(45, 75, 275) # for this plot we are comparing three different information flow # volumes: tweets, blog posts and news. Yes, blogs and news can be # a fuzzy distinction, but this is about the R code. # I use a random Pareto distribution because it is a reasonable # approximation for some types of information flows/attention # (and if you want a reference supporting that let me know). news.text <- "news" tweets.counts <- rparetoI(num.hours, scale=10000, shape=3) news.color <- rgb(128,30,128,alpha=255,maxColorValue=255) blogs.text <- "blogs" blogs.counts <- rparetoI(num.hours, scale=100, shape=2) blogs.color <- rgb(32,135,91,alpha=255,maxColorValue=255) tweets.text <- "tweets" news.counts <- rparetoI(num.hours, scale=1, shape=1.5) tweets.color <- rgb(196,159,47,alpha=255,maxColorValue=255) # probably not the best practice here, but I take a vector # of x values for events at hour X and a vector of y min # and max. So for an x, draw a dotted line from y.min to # y.max at x[i] plot.events <- function(x.vec, y.min.max) { for (i in 1:length(x.vec)) { event.x <- x.vec[i] lines(c(event.x, event.x), y.min.max, col="black", lty=2) } } # work horse function. Again, I could surface more plotting # parameters, but I am focused on a specific kind of comparison # I set a few defaults. X is expected to be a sequence, but it # doesn't have to start at 0. I'm using hours, but it could be # any units. plot.vol <- function(x, y, event.x.vec, caption="", plot.type="h", col="black", adj=c(.5,.5)) { # after the basic plot we want to add the events and then the # caption. Simple calcs to find where to put 'em: my.local.y.lim <- c(0, max(y)) # I tried c(min(y), max(y)) but like the above better for # histogram type plots my.local.y.mid <- round(mean(my.local.y.lim), 0) my.local.x.mid <- round(median(x), 0) plot(x, y, ylim=my.local.y.lim, yaxt="n" , type=plot.type, lwd=2, col=col, bty="n" , main="", xlab="", ylab="") # I keep the captions simple and I put them in the # plot so that the data set plots are close enough together # for comparison plot.events(event.x.vec, my.local.y.lim) text(my.local.x.mid, my.local.y.mid, labels=caption, col=adjustcolor(col, alpha.f = .5), cex=5, adj=text.adjust) mtext("hour", side=1, line=1.5, cex=.8, adj=.1) mtext("volume", side=2, line=3.5, cex=.8) # #padj=-5, ) #adj=c(0,1), cex=.8, outer=T) axis(2, at=axTicks(2), labels=format(axTicks(2), scientific=F, big.mark=","), las=2, tick=F, line=-2.2) } # now plot out each set. I set mfrow to get all three on the # same screen. The xpd param controls clipping for plot area. # I only seem to need to set this for histogram type plots #par(mar=c(5,4.5,2,2), mfrow=c(3,1), xpd=F) # c(bottom, left, top, right) c(5, 4, 4, 2) + 0.1. par(mar=c(3.5, 5.5, 2, 2), mfrow=c(3,1), xpd=F) # c(bottom, left, top, right) c(5, 4, 4, 2) + 0.1. plot.vol (my.local.x, tweets.counts, event.x.vec, tweets.text, plot.type, tweets.color, text.adjust) plot.vol (my.local.x, blogs.counts, event.x.vec, blogs.text, plot.type, blogs.color, text.adjust) plot.vol (my.local.x, news.counts, event.x.vec, news.text, plot.type, news.color, text.adjust) # gimmy back my defaults. par(mar=c(5.1,5.1,4.1,2.1), mfrow=c(1,1), xpd=NA) |

So how do I choose my colors? I use Color Scheme Designer which allows you to move a slider around a color wheel to pick your main color and then select if you want monochrome, complement, triad and other color schemes that work with your main color. You can use the tab selection on the bottom to alter saturation and brightness of the scheme as well. Once you have the colors you like, you can export in different formats. I usually export to rgb for R code.

That’s all for now. You can contact me on Twitter @JeffHemsley. Happy to answer any questions.