# Visualizing Analytics Languages With VennEuler.jl

It often doesn’t take much to get me off track, and on a holiday weekend…well, I was just begging for a fun way to shirk. Enter Harlan Harris:

Hey, I’m someone looking for something to do! And I like writing Julia code! So let’s have a look at recreating this diagram in Julia using VennEuler.jl (IJulia Notebook link):

Source: Revolution R/KDNuggets

http://blog.revolutionanalytics.com/2014/08/r-tops-kdnuggets-data-analysis-software-poll-for-4th-consecutive-year.html

## Installing VennEuler.jl

Because VennEuler.jl is not in METADATA as of the time of writing, instead of using Pkg.add() you’ll need to run:

 Pkg.clone("https://github.com/HarlanH/VennEuler.jl.git")

Note that VennEuler uses some of the more exotic packages (at least to me) like NLopt and Cairo, so you might need to have a few additional dependencies installed with the package.

## Data

The data was a bit confusing to me at first, since the percentages add up to more than 100% (people could vote multiple times). In order to create a dataset to use, I took the percentages, multiplied by 1000, then re-created the voting pattern. The data for the graph can be downloaded from this link.

## Code – Circles

With a few modifications, I basically re-purposed Harlan’s code from the package test files. The circle result is as follows:

Since the percentage of R, SAS, and Python users isn’t too dramatically different (49.81%, 33.42%, 40.97% respectively) and the visualizations are circles, it’s a bit hard to tell that R is about 16% points higher than SAS and 9% points higher than Python.

## Code – Rectangles

Alternatively, we can use rectangles to represent the areas:

Here, it’s a slight bit easier to see that SAS and Python are about the same area-wise and that R is larger, although the different dimensions do obscure this fact a bit.

## Summary

If I spent more time with this package, I’m sure I could make something even more aesthetically pleasing. And for that matter, it’s still a pre-production package that will no doubt get better in the future. But at the very least, there is a way to create an area-accurate representation of relationships using VennEuler.jl in Julia.