By: DSB
Re-posted from: https://davisbarreira.medium.com/the-chart-missing-from-all-spreadsheet-software-b7fede90d634?source=rss-8bd6ec95ab58------2
And how to implement it in Julia
This chart has been discussed in a recent video on the Minute Physics YouTube Channel. It is actually quite simple, consisting of a floating stacked bar chart. The idea of such plot is that we can use it to visualize range values, while assigning a color to represent such range.
Despite its simplicity, one might be surprised to find out that this chart is not readily available in many of the spreadsheet softwares. It is possible to draw it, yet, one needs to resort to some ad-hoc methods. This and more are discussed in the Minute Physics video.
The Challenge
We want to implement this visualization using the Julia programming language; but we want to be able to do this without resorting to a bunch of ad-hoc methods, and without having to write a humongous amount of code.
One possible solution would be to search for a charting library that covers such example. Yet, as in the case with spreadsheets, this search might be futile. Another possible approach would be to use a more generic visualization library that enables us to “directly” specify such graphic.
The Graphic Description
Before we can implement a solution, we must first get a clear picture of how this graphic is constructed. Consider the following dataset:
In order to reproduce the desired chart, we start by rearranging the dataset. We transform the locations (“Ontario”, “England” and “Kentucky”) into row values, and we pair together the temperature labels, for example, “winter mean low | annual mean low”.
If the dataset organized as such, we can more easily describe how to construct our graphic. Here is the construction logic. For each row, draw a “bar” with the horizontal position according to the location column, the lower end of the bar according to the low_temp column, the higher end of the bar according to the high_temp column, and the bar color is based on the label_temp column.
That’s it! With the dataset structured in such a way, the procedure to construct our graphic is fairly intuitive. We are now ready to implement our solution.
The Implementation
In order to draw our graphic, we need a visualization library that allows us to implement the construction logic we just described. This can be done using the package called Vizagrams.jl.
Vizagrams is a visualization grammar with a syntax very similar to VegaLite. If you have used packages such as Altair or ggplot, you probably know what I am talking about. The difference compared to these grammars is that Vizagrams implements what is called “graphic expression”.
A graphic expression is a constructive description of a graphic. In other words, we can use graphic expressions to encode our constructive logic without having to resort to ad-hoc methods, or writing a bunch of code.
This tutorial does not aim to thoroughly explain Vizagrams or graphic expressions. So let us cut short our explanation here, and move on to the solution.
In the code below, we start by importing Vizagrams. We assume that df holds the dataframe already properly transformed, and we use my-colors to specify the colors to be used (one could also just pick an existing colorscheme). We then specify the plot, which is stored into the plt variable.
The plot specification contains the data, the encoding variables (x, y, color, low_y) and the graphic, which is where we write the graphic expression. Our graphic expression simply iterates over each row in the dataset, and draws a trail having width 20 and with the respective color, lower point and higher point. The trail mark is simpler to use (in this example) and delivers the same result as using the bar mark.
using Vizagrams
df # Our transformed dataframe
my_colors = ["#F28E2B","#4E79A7","#FFBE7D","#A0CBE8"] # Our bar colors
plt = Plot(
data=df,
x = :location,
y = (field=:high_temp,scale_domain=(-10,40), scale_range=(0,200)),
low_y = (field=:low_temp,scale_domain=(-10,40), scale_range=(0,200)),
color = (field=:label_temp,scale_range=my_colors),
graphic =
∑() do row
S(:fill=>row.color)*Trail([[row.x,row.low_y],[row.x,row.y]],20)
end
)
draw(plt)
Conclusion
And there we have it! We implemented the desired chart. The example with all the code and data necessary can be found in Vizagrams’ gallery under the “Floating Stacked Bar” section.
To get a better understanding of what is going on, you should hop on a notebook and try the code yourself. Try changing the graphic expression using different graphical marks, or changing how the encoding variables are used.
