The things you already can do with Makie are mind blowing, including 2D and 3D plots, animations, dashboards and more. If you want to see some showcases check out the Beautiful Makie website.
Today I want to write about AlgebraOfGraphics.jl package defines a language for data visualization, that is especially useful with tabular data.
It is natural to compare AlgebraOfGraphics.jl to ggplot2 as indeed both ecosystems allow to declaratively create graphics. In this post I want to highlight one important difference. The ggplot2 is designed to be a stand alone ecosystem. While AlgebraOfGraphics.jl is an addition to Makie.jl that makes standard tabular data visualization easy. However, you still can apply all low-level operations that Makie.jl provides to the created plots. Therefore you get the best of both worlds:
- a convenient declarative system for defining plots;
- ability of easy modification of generated plots to tune them to the exact needs of a data scientist.
I want to show you how this is achieved by example.
The post was written under Julia 1.9.0-rc1, AlgebraOfGraphics v0.6.14, CSV v0.10.9, CairoMakie v0.10.4, and DataFrames v1.5.0.
Setting up the scene
We will want to plot the following data stored in CSV format:
aog_csv = """ problem,language,time,size binary-trees,Java,1.5886075949367087,1.0321384425216316 binary-trees,Julia,4.6075949367088604,0.7836835599505563 binary-trees,Python,28.29113924050633,0.8158220024721878 fannkuch-redux,Java,1.3825857519788918,1.408791208791209 fannkuch-redux,Julia,1.0329815303430079,1.1725274725274726 fannkuch-redux,Python,45.04617414248021,1.043956043956044 fasta,Java,1.5384615384615383,1.7382091592617908 fasta,Julia,1.4487179487179485,0.7395762132604238 fasta,Python,47.30769230769231,1.330827067669173 k-nucleotide,Java,1.2196969696969697,1.203187250996016 k-nucleotide,Julia,1.2474747474747476,0.6314741035856574 k-nucleotide,Python,11.694444444444445,1.3061088977423638 mandelbrot,Java,3.1538461538461533,0.7013215859030837 mandelbrot,Julia,1.0923076923076922,0.5453744493392071 mandelbrot,Python,136.4230769230769,0.6061674008810573 n-body,Java,3.1784037558685445,0.9118187385180649 n-body,Julia,1.9765258215962442,0.6803429271279853 n-body,Python,254.15023474178406,0.7323943661971831 pidigits,Java,1.4107142857142856,0.7009174311926606 pidigits,Julia,1.732142857142857,0.46422018348623856 pidigits,Python,2.071428571428571,0.5201834862385321 regex-redux,Java,6.675,0.6649964209019327 regex-redux,Julia,2.175,0.5433070866141733 regex-redux,Python,1.675,1.0042949176807445 reverse-complement,Java,3.829268292682927,1.110941475826972 reverse-complement,Julia,3.5121951219512195,0.26564885496183205 reverse-complement,Python,16.146341463414636,0.41424936386768446 spectral norm,Java,3.780487804878049,0.631578947368421 spectral norm,Julia,2.707317073170732,0.3583959899749373 spectral norm,Python,275.5365853658537,0.34001670843776105 """
The CSV stores information on speed and code size of programs written in Java, Julia, and Python relative to C for ten selected computational problems. The data was taken from The Computer Language Benchmarks Game website. I discuss the interpretation of this data in Chapter 1 of Julia for Data Analysis book.
Let us first load this data to a data frame:
using AlgebraOfGraphics using CairoMakie using CSV using DataFrames aog_df = CSV.read(IOBuffer(aog_csv), DataFrame)
You should get the following output:
30×4 DataFrame Row │ problem language time size │ String31 String7 Float64 Float64 ─────┼─────────────────────────────────────────────────── 1 │ binary-trees Java 1.58861 1.03214 2 │ binary-trees Julia 4.60759 0.783684 3 │ binary-trees Python 28.2911 0.815822 4 │ fannkuch-redux Java 1.38259 1.40879 5 │ fannkuch-redux Julia 1.03298 1.17253 6 │ fannkuch-redux Python 45.0462 1.04396 7 │ fasta Java 1.53846 1.73821 8 │ fasta Julia 1.44872 0.739576 9 │ fasta Python 47.3077 1.33083 10 │ k-nucleotide Java 1.2197 1.20319 11 │ k-nucleotide Julia 1.24747 0.631474 12 │ k-nucleotide Python 11.6944 1.30611 13 │ mandelbrot Java 3.15385 0.701322 14 │ mandelbrot Julia 1.09231 0.545374 15 │ mandelbrot Python 136.423 0.606167 16 │ n-body Java 3.1784 0.911819 17 │ n-body Julia 1.97653 0.680343 18 │ n-body Python 254.15 0.732394 19 │ pidigits Java 1.41071 0.700917 20 │ pidigits Julia 1.73214 0.46422 21 │ pidigits Python 2.07143 0.520183 22 │ regex-redux Java 6.675 0.664996 23 │ regex-redux Julia 2.175 0.543307 24 │ regex-redux Python 1.675 1.00429 25 │ reverse-complement Java 3.82927 1.11094 26 │ reverse-complement Julia 3.5122 0.265649 27 │ reverse-complement Python 16.1463 0.414249 28 │ spectral norm Java 3.78049 0.631579 29 │ spectral norm Julia 2.70732 0.358396 30 │ spectral norm Python 275.537 0.340017
Drawing a plot
Start with a complete code producing the plot:
fig = Figure(resolution = (900, 600)) aog_plt = data(aog_df) * mapping(:problem, [:time, :size]; marker=:language, color=:language, col = dims(1) => renamer(["", ""])) * visual(Scatter; markersize=15, strokewidth=1) grid = draw!(fig, aog_plt, axis=(; xticklabelrotation=pi/6), palettes=(; marker=[:rect, :circle, :diamond], color=["lightgray", "gold", "lightblue"])) grid.axis.yscale = log10 hlines!(grid.axis, 1.0; color="red") hlines!(grid.axis, 1.0; color="red") lg = AlgebraOfGraphics.compute_legend(grid) push!(lg, [LineElement(color="red")]) push!(lg, "C") Legend(fig[1, end+1], lg...) fig
It produces the following figure:
On the figure we can see that Julia performs quite well in both time and code size in comparison to other programming languages.
However, today I want to focus on plotting.
The first thing is displaying the plot.
If you use Jupyter Notebook or VS Code the plot will be just shown.
However, if you work in the terminal automatic displaying is turned
off by default. You need to run the
to turn it on. After issuing it each time you try to display the
figure it will be opened in your default image viewer.
Now let me comment on the plotting code. If you have never used AlgebraOfGraphics.jl I recommend you first read the AlgebraOfGraphics.jl manual as the code I used is relatively advanced.
The line creating the
aog_plt object is using AlgebraOfGraphics.jl
commands. As you can see using
* we can link data, with mapping operations
and visualization commands. What is interesting in the presented case
is the fact that my source data contains two columns
that I want plotted on separate subplots. This is not a problem in
AlgebraOfGraphics.jl. I just needed to pass a vector
to make it work. Later the
col = dims(1) => renamer(["", ""]) instruction
signals that I want to have them plotted in separate subplots. The
renamer(["", ""]) disables display of subplot titles as I have
series names shown as y-axis labels anyway.
Now notice the
draw! command. It inserts the plot specification
created in AlgebraOfGraphics.jl into a Makie figure. The nice thing is
that it is possible to customize the plot by passing
The code that was really interesting for me comes next. The
is bound to a standard Makie object. Therefore I can later tweak as I wish
using Makie primitives. In this case what I wanted to do was:
- Changing the y-axis of the first of the plots to logarithmic scale.
I could easily achieve this by writing
grid.axis.yscale = log10.
- Adding two custom horizontal lines (representing the reference C language)
to both plots which I achieved using the
- Manually adding the C language entry to the legend of the plot which
I achieved by manipulating the
What is important is that even for someone who is not a Makie expert it was possible to work out how the underlying objects should be mutated to get what I wanted without a significant effort.
Of course one might want AlgebraOfGraphics.jl to be a complete plotting system like ggplot2. However, I think that the choice made in it is correct. I appreciate that the API of AlgebraOfGraphics.jl is relatively small. In this way you can easily learn to do simple and standard things. It also means that is is convenient to do exploratory data analysis (when you often need to do a lot of simple plots). Later, if I am to prepare a publication quality plot in which I need to perform some non-standard customizations I can use the Makie functionality to achieve this.
Here are the key things I learned during MakieCon 2023:
- Makie ecosystem provides a lot of advanced functionalities that are hard to find in other plotting packages. I typically need to prepare high-quality plots for publications. In this aspect a particular value of Makie is that it allows you to tweak every detail of the plot the way you want.
- AlgebraOfGraphics.jl is not an equivalent of ggplot2 (which is a complete stand-alone plotting system). I find it easier to think about AlgebraOfGraphics.jl as a set of extra functions that build on top of Makie and provide a convenient way to create visualizations of tabular data. However, you can later fall-back to Makie primitives to further customize the plots if needed.
Additionally, if you want to start working with Makie ecosystem you need to know that:
- It is essential that you read the Makie tutorial first.
To ensure the full flexibility of how your plots are styled Makie.jl introduces
several concepts (like
Axisobjects) that you need to learn if you want to confidently work with it.
- Makie ecosystem provides several plotting backends that differ in functionality.
I use CairoMakie.jl as I typically need non-interactive 2D plots that have
publication quality. As a particular thing in this case you need to know
that if you are working in a terminal you need to use the
Makie.inline!(false)command to turn-on automatic rendering of a plot (this is not needed in Jupyter Notebook and VS Code) if you want it (alternatively you can explicitly save your plot to a file).
Before I finish I would like to thank Dr. Lazaro Alonso Silva for inviting me to the event. The conference was an exceptional experience. Makie community is amazing and full of brilliant members.