mages' blog

The guys at RStudio have done a fantastic job with shiny. It is really easy to build web apps with R using shiny. With the help of Joe Cheng from RStudio we figured out a way to make googleVis work on shiny as well. This allows you to make use of the Google Charts Tools in your shiny app directly from R. What I present here are three initial examples which seem to work in most browsers. The third example even uses a neat trick to create an animated geo chart.



However, before we upload the next version of googleVis to CRAN we decided to present a preview of version 0.4.0 here, asking for feedback. It would not be fair on the guys behind CRAN to release something into the wild, only to be told by users within a few days that we missed something. Hence, you can get the new version of googleVis only from the download page of our project site for the time being.

You may have read the post on RStudio's blog that the shiny API changed slightly: the reactivePlot and reactiveText functions have been renamed to renderPlot and renderText with simplified input parameters. Thanks to Joe, there is now also a renderGvis function as part of the googleVis package, which works in very much the same way as the other two.

To run the following examples you need shiny version 0.4.0 and googleVis version 0.4.0 or higher.

Last week I attended a seminar where a talk was given about the economic opportunities in the SAAAME (South-America, Asia, Africa and Middle East) regions. Of course a map was shown with those regions highlighted. The map was not that disimilar to the one below.

library(RColorBrewer)
library(rworldmap)
data(countryExData)
par(mai=c(0,0,0.2,0),xaxs="i",yaxs="i")
mapByRegion( countryExData, 
             nameDataColumn="GDP_capita.MRYA",
             joinCode="ISO3", nameJoinColumn="ISO3V10",
             regionType="Stern", mapTitle=" ", addLegend=FALSE,
             FUN="mean", colourPalette=brewer.pal(6, "Blues"))

It is a map that most of us in the Northern hemisphere see often. However, it shows a distorted picture of the world.

Greenland appears to be of the same size as Brazil or Australia and Africa seems to be only about four times as big as Greenland. Of course this is not true. Africa is about 14 times the size of Greenland, while Brazil and Australia are about four times the size of Greenland, with Brazil slightly larger than Australia and nine times the population. Thus, talking about regional opportunities without a comparable scale can give a misleading picture.

Of course you can use a different projection and XKCD helps you to find one which fits your personality. On the other hand, Michael Gastner and Mark Newman developed cartograms, which can reshape the world based on data [1]. Doing this in R is a bit tricky. Duncan Temple Lang provides the Rcartogram package on Omegahat based on Mark Newman's code and Mark Ward has some examples using the package on his 2009 fall course page to get you started.

A simple example of a cartogram is given as part of the maps package. It shows the US population by state: