Visualizing Data with Plots.jl: An Overview

Data visualization is a common practice for understanding information. Seeing data in a graphical form helps in understanding patterns, trends, and outliers much more effectively than looking at raw numbers or tables. Plots.jl is one of the most popular and versatile tools for this purpose within the Julia ecosystem. An overview of how to start creating visualizations with this package is presented here.

Plots.jl is not a plotting library itself, but rather a plotting metapackage or API. It provides a common interface to many different underlying plotting libraries, known as "backends." This means you can learn one set of commands and then choose which backend actually generates the plot, perhaps GR for speed in generating static plots, or PlotlyJS for interactive web-based plots. This flexibility is a significant advantage, allowing you to switch between plotting engines without having to rewrite your plotting code significantly.

Getting Started with Plots.jl

Before you can create any plots, you'll need to add the Plots.jl package to your Julia environment if you haven't already. You can do this using Julia's built-in package manager, Pkg. Open your Julia REPL and type:

import Pkg
Pkg.add("Plots")

Once the installation is complete, you can load the package into your current Julia session using the using keyword:

using Plots

By default, Plots.jl will try to use a suitable backend, often GR, which is a fast and lightweight option good for quick visualizations. You don't usually need to install backends separately for basic usage with GR, as Plots.jl often handles it.

Creating Your First Plot

Let's start with a simple line plot. Suppose you have some data representing points on a curve, for instance, the values of $y = x^2$ for $x$ ranging from -5 to 5.

First, let's generate our data:

x = -5:0.5:5 # Creates a range of numbers from -5 to 5, with a step of 0.5
y = x.^2     # Squares each element of x. Note the dot for element-wise operation.

Now, plotting this is straightforward:

plot(x, y)

When you execute this command, a plot window should appear showing a parabola. This basic plot command is quite powerful. If you provide two vectors, Plots.jl assumes the first is for the x-axis and the second for the y-axis, and by default, it creates a line plot.

Enhancing Your Plots

A raw plot is often not enough. You'll want to add titles, label your axes, and perhaps add a legend if you're plotting multiple series. Plots.jl allows you to do this using various keyword arguments or by calling "mutating" functions (functions that end with !).

Let's enhance our previous plot:

plot(x, y,
    label="y = x^2",
    linewidth=2,
    color=:blue
)
title!("My First Julia Plot")
xlabel!("X-axis values")
ylabel!("Y-axis values (x squared)")

Here:

• label assigns a name to our data series, which will be used in the legend.
• linewidth adjusts the thickness of the line.
• color sets the color of the line. Plots.jl understands many color names as symbols (e.g., :blue, :red, :green).
• title!, xlabel!, and ylabel! are functions that modify the current plot to add a title and axis labels, respectively.

Exploring Different Plot Types

Plots.jl supports a wide variety of plot types. Here are a few common ones:

• Scatter Plots: Useful for showing the relationship between two variables where individual data points are important. Use scatter(x, y).
• Bar Charts: Good for comparing quantities across different categories. Use bar(categories, heights).
• Histograms: Ideal for visualizing the distribution of a single variable. Use histogram(data).

Let's try a simple scatter plot.

# Generate some slightly noisy data
x_scatter = 1:10
y_scatter = x_scatter .+ randn(10) # y = x + some random noise

scatter(x_scatter, y_scatter,
    label="Noisy Data Points",
    color=:red,
    markershape=:circle,
    title="Simple Scatter Plot",
    xlabel="X Data",
    ylabel="Y Data"
)

This will create a plot with individual circular markers for each data point. The markershape argument allows you to control the appearance of these markers.

A Quick Look at Backends

As mentioned, Plots.jl uses backends to render plots. While the default (often GR) is great for many purposes, you might want to use a different one, for example, to create interactive plots. PlotlyJS is a popular backend for this.

To use a specific backend, you typically call its name as a function, like plotlyjs(), before making your plots. Plots.jl will then attempt to use that backend for subsequent plots in the session. You might need to add the backend package first (e.g., Pkg.add("PlotlyJS")).

# Pkg.add("PlotlyJS") # If not already added
using Plots
plotlyjs() # Switch to the PlotlyJS backend

# Example data for a bar chart
categories = ["A", "B", "C", "D"]
heights = [10, 24, 15, 7]

bar(categories, heights,
    title="Sample Bar Chart (PlotlyJS)",
    label="Values",
    color=:teal
)

If you execute this, you might get an interactive plot in your browser or IDE's plot pane, where you can hover over bars to see values, zoom, and pan.

Below is an example of what a simple bar chart might look like, represented in a format that could be generated by a backend like PlotlyJS.

A bar chart comparing quantities for four distinct categories.

Where to Go From Here

This has been a brief introduction to Plots.jl. There's a rich set of features available, including:

• Plotting multiple series on the same graph.
• Creating subplots (layouts with multiple plots in one figure).
• Extensive customization options for colors, markers, line styles, annotations, and more.
• Specialized plot types for statistics, finance, and other domains.
• Saving plots to files in various formats (PNG, PDF, SVG, etc.) using the savefig("filename.png") command.

The official Plots.jl documentation is an excellent resource for exploring its full capabilities. As you work on the small project suggested at the end of this course, consider how visualizations could help you understand your data or present your results. Being able to quickly plot data is an invaluable skill in any programming or data analysis endeavor.