**OUseful.Info, the blog... » Rstats**, and kindly contributed to R-bloggers)

Doodling with some Gapminder data on child mortality and GDP per capita in PPP$, I wondered whether a 3d plot of the data over the time would show different trajectories over time for different countries, perhaps showing different development pathways over time.

Here are a couple of quick sketches, generated using R (this is the first time I’ve tried to play with 3d plots…)

library(xlsx) #data downloaded from Gapminder #dir() #wb=loadWorkbook("indicator gapminder gdp_per_capita_ppp.xlsx") #names(getSheets(wb)) #Set up dataframes gdp=read.xlsx("indicator gapminder gdp_per_capita_ppp.xlsx", sheetName = "Data") mort=read.xlsx("indicator gapminder under5mortality.xlsx", sheetName = "Data") #Tidy up the data a bit library(plyr) gdpm=melt(gdp,id.vars = 'GDP.per.capita',variable.name='year') gdpm$year = as.integer(gsub('X', '', gdpm$year)) gdpm=rename(gdpm, c("GDP.per.capita"="country", "value"="GDP.per.capita")) mortm=melt(mort,id.vars = 'Under.five.mortality',variable.name='year') mortm$year = as.integer(gsub('X', '', mortm$year)) mortm=rename(mortm, c("Under.five.mortality"="country", "value"="Under.five.mortality")) #The following gives us a long dataset by country and year with cols for GDP and mortality gdpmort=merge(gdpm,mortm,by=c('country','year')) #Filter out some datasets by country x.us=gdpmort[gdpmort['country']=='United States',] x.bg=gdpmort[gdpmort['country']=='Bangladesh',] x.cn=gdpmort[gdpmort['country']=='China',]

Now let’s have a go at some charts. First, let’s try a static 3d line plot using the `scatterplot3d` package:

library(scatterplot3d) s3d = scatterplot3d(x.cn$year,x.cn$Under.five.mortality,x.cn$GDP.per.capita, color = "red", angle = -50, type='l', zlab = "GDP.per.capita", ylab = "Under.five.mortality", xlab = "year") s3d$points3d(x.bg$year,x.bg$Under.five.mortality, x.bg$GDP.per.capita, col = "purple", type = "l") s3d$points3d(x.us$year,x.us$Under.five.mortality, x.us$GDP.per.capita, col = "blue", type = "l")

Here’s what it looks like… (it’s worth fiddling with the angle setting to get different views):

A 3d bar chart provides a slightly different view:

s3d = scatterplot3d(x.cn$year,x.cn$Under.five.mortality,x.cn$GDP.per.capita, color = "red", angle = -50, type='h', zlab = "GDP.per.capita", ylab = "Under.five.mortality", xlab = "year",pch = " ") s3d$points3d(x.bg$year,x.bg$Under.five.mortality, x.bg$GDP.per.capita, col = "purple", type = "h",pch = " ") s3d$points3d(x.us$year,x.us$Under.five.mortality, x.us$GDP.per.capita, col = "blue", type = "h",pch = " ")

As well as static 3d plots, we can generate interactive ones using the

Here’s the code to generate an interactive 3d plot that you can twist and turn with a mouse:

#Get the data from required countries - data cols are GDP and child mortality x.several = gdpmort[gdpmort$country %in% c('United States','China','Bangladesh'),] library(rgl) plot3d(x.several$year,x.several$Under.five.mortality,log10(x.several$GDP.per.capita), col=as.integer(x.several$country), size=3)

We can also set the 3d chart spinning….

play3d(spin3d(axis = c(0, 0, 1)))

We can also grab frames from the spinning animation and save them as individual png files. If you have Imagemagick installed, there’s a function that will generate the image files and weave them into an animated gif automatically.

It’s easy enough to install on a Mac if you have the Homebrew package manager installed. On the command line:

`brew install imagemagick`

Then we can generate the movie:

movie3d(spin3d(axis = c(0, 0, 1)), duration = 10, dir = getwd())

Here’s what it looks like:

Handy…:-)

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