The Luxembourg Income Study Database contains various income measures at selected percentiles for different countries over the last few decades. I think that a series of 2D plots would be better than a 3D plot for this sort of time series data, but it works as an example in how to create a scatterplot with lines joining the points in each series together.

You can change the percentile with the drop-down menus below the plot. (I don't know what the story is with France's p5 in 2010.) Values are equivalised disposable household incomes.

Mouse controls: Left-click and drag to rotate; alt (Mac)- or ctrl (Windows)-click-drag or middle-click-drag to pan; scroll or shift-click-drag to zoom. Touch screen controls: one finger to rotate; two-finger scroll to pan; pinch to zoom. Click/tap on the cube icons to snap to a side-on view.

Colours:

y-axis: p5 p10 p25 p50 mean p75 p90 p95

z-axis: p5 p10 p25 p50 mean p75 p90 p95

You can see how the plot is constructed in the HTML source:

• A CSV file and the R script I used to turn the relevant fields into JavaScript are here; the code sources my main JS-writing function write_three_d.R.
• Apart from cosmetic additions to the params object (in particular setting join_points to true), it's compulsory to define the parameters for the groups (countries), which is done in params.groups: an array of objects, one object per group, containing that group's name and default parameters. (Here the only parameter is the default_color, but you could have default_point_height as well.)
• To update the plot, a new params object is created and sent to three_d.change_data(). Values are read from three_d.plots[0].points[i].input_data.other, where they had been stored in the plot's creation.

Posted 2016-12-22.