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@@ -12,25 +12,25 @@ using InteractiveUtils
# Plots and Data Visualization in Julia: _Plots.jl_
There are numerous graphics packages for Julia. Two frequently used ones are [Makie.jl](https://docs.makie.org/stable/) and
[Plots.jl](https://docs.juliaplots.org/latest/). Before presenting these in more detail, some other packages are listed.
Julia has numerous graphics packages. Two frequently used ones are [Makie.jl](https://docs.makie.org/stable/) and
[Plots.jl](https://docs.juliaplots.org/latest/). Before presenting `Plots.jl` in detail, we list some others.
## Brief Overview: Some Graphics Packages
| Package/Documentation | Tutorial | Examples | Remarks |
|:----|:--|:--|:--------|
|[Plots.jl](https://docs.juliaplots.org/latest/) | [Tutorial](https://docs.juliaplots.org/latest/tutorial/) | [Gallery](https://goropikari.github.io/PlotsGallery.jl/) | designed as a unified interface to various _backends_ (graphics libraries) |
|[Plots.jl](https://docs.juliaplots.org/latest/) | [Tutorial](https://docs.juliaplots.org/latest/tutorial/) | [Gallery](https://docs.juliaplots.org/latest/gallery/gr/) | designed as a unified interface to various _backends_ (graphics libraries) |
| [Makie.jl](https://docs.makie.org/stable/) | [Basic tutorial](https://docs.makie.org/v0.21/tutorials/basic-tutorial) | [Beautiful Makie](https://beautiful.makie.org/) | "data visualization ecosystem for Julia", backends: Cairo (vector graphics), OpenGL, WebGL |
|[PlotlyJS.jl](http://juliaplots.org/PlotlyJS.jl/stable/) | [Getting started](https://plotly.com/julia/getting-started/)| [Examples](https://plotly.com/julia/plotly-fundamentals/)| Interface to the [Plotly](https://plotly.com/graphing-libraries/) JavaScript graphics library |
| [Gadfly.jl](https://gadflyjl.org/stable/)| [Tutorial](https://gadflyjl.org/stable/tutorial/) | [Gallery](https://github.com/GiovineItalia/Gadfly.jl?tab=readme-ov-file#gallery)| "a plotting and data visualization system written in Julia, influenced by R's [ggplot2](https://ggplot2.tidyverse.org/)" |
| [Bokeh.jl](https://cjdoris.github.io/Bokeh.jl/stable/) | | [Gallery](https://cjdoris.github.io/Bokeh.jl/stable/gallery/)| Julia frontend for [Bokeh](https://bokeh.org/) |
|[VegaLite.jl](https://www.queryverse.org/VegaLite.jl/stable/) | [Tutorial](https://www.queryverse.org/VegaLite.jl/stable/gettingstarted/tutorial/)| [Examples](https://www.queryverse.org/VegaLite.jl/stable/examples/examples_barcharts/)| Julia frontend for [Vega-Lite](https://vega.github.io/vega-lite/)|
| [Luxor.jl](http://juliagraphics.github.io/Luxor.jl/stable/) |[Tutorial](https://juliagraphics.github.io/Luxor.jl/stable/tutorial/helloworld/)|[Examples](https://juliagraphics.github.io/Luxor.jl/stable/example/moreexamples/)| General vector graphics/illustrations |
| [Luxor.jl](https://juliagraphics.github.io/LuxorManual/stable/) |[Tutorial](https://juliagraphics.github.io/LuxorManual/stable/tutorial/helloworld/)|[Examples](https://juliagraphics.github.io/LuxorManual/stable/example/moreexamples/)| General vector graphics/illustrations |
| [Javis.jl](https://juliaanimators.github.io/Javis.jl/stable/) |[Tutorials](https://juliaanimators.github.io/Javis.jl/stable/tutorials/)| [Examples](https://juliaanimators.github.io/Javis.jl/stable/examples/)| *Animated* vector graphics
| [TidierPlots.jl](https://github.com/TidierOrg/TidierPlots.jl)| [Reference](https://tidierorg.github.io/TidierPlots.jl/latest/) || "is a 100% Julia implementation of the R package ggplot2 powered by Makie.jl"|
|[PythonPlot.jl](https://github.com/JuliaPy/PythonPlot.jl)| |[Examples (in Python)](https://matplotlib.org/stable/gallery/index.html)| Interface to Matplotlib (Python), 1:1 transfer of the Python API, therefore see [Matplotlib documentation](https://matplotlib.org/stable/api/pyplot_summary.html)
{: .striped .hover}
: {.striped .hover}
<!--
| [PyPlot.jl](https://github.com/JuliaPy/PyPlot.jl) | | [Examples](https://gist.github.com/gizmaa/7214002)| Interface to Matplotlib (Python), 1:1 transfer of the Python API, therefore see [Matplotlib documentation](https://matplotlib.org/stable/) |
@@ -44,7 +44,7 @@ The `plot()` function expects, in the simplest case:
- as the first argument a vector of $x$-values of length $n$ and
- as the second argument a vector of the same length with the corresponding $y$-values.
- The second argument can also be an $n\times m$-matrix. Then each column vector is regarded as a separate graph (called `series` in the documentation) and $m$ curves are plotted:
- The second argument can also be an $n\times m$ matrix. Each column is treated as a separate graph (called a `series` in the documentation), plotting $m$ curves:
```{julia}
@@ -57,8 +57,8 @@ cx = @. cos(2x^(1/2))
plot(x, [sx cx])
```
- The functions of the _Plots.jl_ package such as `plot(), scatter(), contour(), heatmap(), histogram(), bar(),...` etc. all start a new plot.
- The versions `plot!(), scatter!(), contour!(), heatmap!(), histogram!(), bar!(),...` extend an existing plot:
- Functions like `plot()`, `scatter()`, `contour()`, `heatmap()`, `histogram()`, `bar()`, etc. from _Plots.jl_ all start a new plot.
- The versions `plot!()`, `scatter!()`, `contour!()`, `heatmap!()`, `histogram!()`, `bar!()`, etc. extend an existing plot:
```{julia}
plot(x, sx) # plot only sin(x)
@@ -74,7 +74,7 @@ plot1a = deepcopy(plot1) # plot objects are quite deep structures
scatter!(plot1, x, sx) # add scatter plot, i.e. unconnected data points
```
The copied version `plot1a` has not been modified by the `scatter!` statement and can be used independently:
The copied version `plot1a` remains unchanged by the `scatter!` call and can be used independently:
```{julia}
plot!(plot1a, x, 2 .* sx)
@@ -82,7 +82,7 @@ plot!(plot1a, x, 2 .* sx)
Plot objects can be saved as graphics files (PDF, SVG, PNG,...):
Plot objects can be saved as graphics files (PDF, SVG, PNG, etc.):
```{julia}
@@ -111,7 +111,7 @@ plot(f, 0:0.01:3)
```
The parametric form $x = x(t),\ y = y(t)$ can be drawn by passing two functions and a vector of $t$-values to `plot()`.
The parametric form $x = x(t),\ y = y(t)$ is plotted by passing two functions and a vector of $t$-values to `plot()`.
```{julia}
# https://en.wikipedia.org/wiki/Butterfly_curve_(transcendental)
@@ -125,10 +125,9 @@ plot(xt, yt, 0:0.01:12π)
### Plot Themes
> "PlotThemes is a package to spice up the plots made with Plots.jl."\
Here is the illustrated [list of themes](https://docs.juliaplots.org/stable/generated/plotthemes/)
> "PlotThemes is a package to spice up plots made with Plots.jl."\
See the illustrated [list of themes](https://docs.juliaplots.org/stable/generated/plotthemes/)
or:
```{julia}
using PlotThemes
@@ -151,8 +150,8 @@ plot(x, [sx cx 1 ./ (1 .+ x)])
### Plot Attributes
The functions of the `Plots.jl` package have a large number of options.
`Plots.jl` divides the attributes into 4 groups:
The `Plots.jl` functions have numerous options.
Attributes are divided into 4 groups:
::::{.cell}
```{julia}
@@ -182,7 +181,7 @@ plotattr(:Series) # attributes for a series, e.g., a line in the plot
```
::::
One can also ask what the individual attributes mean and which values are allowed:
You can also query what individual attributes mean and which values are allowed:
```{julia}
plotattr("linestyle")
```
@@ -200,7 +199,7 @@ plot(x, y, seriestype = :sticks, linewidth = 4, seriescolor = "#00b300",
)
```
Many specifications can also be abbreviated significantly, see e.g. the `Aliases:` in the above output of the command `plotattr("linestyle")`.
Many specifications can be abbreviated significantly; see, e.g., the `Aliases:` in the output of `plotattr("linestyle")`.
The following `plot()` command is equivalent to the previous one:
@@ -213,13 +212,13 @@ plot(x, y, t = :sticks, w = 4, c = "#00b300", m = (:circle, 8, :green ))
### Additional Extras
```{julia}
using Plots # repetition does not hurt
using Plots # no harm in repeating
using Plots.PlotMeasures # for measurements in mm, cm,...
using LaTeXStrings # for LaTeX constructs in plot labels
using PlotThemes # predefined themes
```
The `LaTeXStrings.jl` package provides a string constructor `L"..."`. These strings can contain LaTeX constructs, especially formulas. If the string does not contain explicit dollar signs, it is automatically interpreted in LaTeX math mode.
The `LaTeXStrings.jl` package provides the `L"..."` string constructor. These strings can contain LaTeX constructs, especially formulas. Without explicit dollar signs, they are automatically interpreted in LaTeX math mode.
```{julia}
xs = range(0, 2π, length = 100)
@@ -232,9 +231,9 @@ plot10 = plot(xs, data,
fontfamily="Computer Modern",
# LaTeX string L"..."
title = L"Winkelfunktionen $\sin(\alpha), \cos(\alpha), 2\sin(\alpha), \sin(\alpha^2)$",
xlabel = L"Winkel $\alpha$",
ylabel = "Funktionswert",
title = L"Trigonometric functions $\sin(\alpha), \cos(\alpha), 2\sin(\alpha), \sin(\alpha^2)$",
xlabel = L"angle $\alpha$",
ylabel = "value",
# 1x4-matrices with colors, markers,... for the 4 'series'
color=[:black :green RGB(0.3, 0.8, 0.2) :blue ],
@@ -255,14 +254,14 @@ plot10 = plot(xs, data,
top_margin = 5mm, # here Plots.PlotMeasures is needed
)
# additional text: annotate!(x-pos, y-pos, text("...", font, fontsize))
# additional text: annotate!(x-pos, y-pos, text("...", font, fontsize))
annotate!(plot10, 4.1, 1.8, text("nicht schön, aber viel","Computer Modern", 10) )
annotate!(plot10, 4.1, 1.8, text("Some remark","Computer Modern", 10) )
```
### Other Plot Functions
So far, we have plotted mainly lines. There are many other types such as _scatter plot, contour, heatmap, histogram, stick,..._
So far, we have mainly plotted lines. Many other types exist, such as _scatter plots, contours, heatmaps, histograms, sticks_, etc.
This can be controlled with the `seriestype` attribute:
@@ -283,7 +282,7 @@ scatter(x, sin.(x))
### Subplots and Layout {#sec-subplot}
Multiple plots can be combined into one figure. The arrangement is determined by the `layout` parameter. `layout=(m,n)` means that the plots are arranged in an $m\times n$ scheme:
Multiple plots can be combined into one figure. The arrangement is determined by the `layout` parameter: `layout=(m,n)` arranges plots in an $m\times n$ grid:
```{julia}
x = range(0, 2π; length = 100)
@@ -317,11 +316,11 @@ plot(plots..., layout=mylayout, legend=false, title=["sin" "cos" "tan" "sinc"])
### Backends
`Plots.jl` is designed as a unified interface to various _backends_ (graphics engines). One can switch to another backend and use the same plot commands and attributes.
`Plots.jl` provides a unified interface to various _backends_ (graphics engines). You can switch backends and use the same plot commands and attributes.
However, not all _backends_ support all plot types and attributes. An overview is available [here](https://docs.juliaplots.org/stable/generated/supported/).
So far, the default backend has been used. It is called [GR](https://gr-framework.org/about.html) and is a graphics engine developed at the Jülich Research Center and written primarily in C.
So far, we have used the default backend: [GR](https://gr-framework.org/about.html), a graphics engine developed at the Jülich Research Center, primarily in C.
```{julia}
@@ -330,22 +329,22 @@ backend() # display the selected backend, GR is the default
```
Another example
Another example:
```{julia}
x = 1:30
y = rand(30)
plot(x, y, linecolor =:green, bg_inside =:lightblue1, line =:solid, label = "Wasserstand")
plot(x, y, linecolor =:green, bg_inside =:lightblue1, line =:solid, label = "Water level")
```
and here the same plot with the `PlotlyJS` backend.
The same plot with the `PlotlyJS` backend:
```{julia}
plotlyjs() # change plots backend
plot(x, y, linecolor =:green, bg_inside =:lightblue1, line =:solid, label = "Wasserstand")
plot(x, y, linecolor =:green, bg_inside =:lightblue1, line =:solid, label = "Water level")
```
This backend enables a certain interactivity using JavaScript. When moving the mouse into the image, one can zoom and pan with the mouse, and 3D plots can also be rotated.
This backend enables interactivity via JavaScript. Hovering over the image allows zooming and panning; 3D plots can also be rotated.
```{julia}
@@ -356,7 +355,7 @@ gr() # return to GR as backend
### 3D Plots
The functions `surface()` and `contour()` allow plotting of a function $f(x,y)$. The required arguments are:
The `surface()` and `contour()` functions plot a function $f(x,y)$. Required arguments are:
- a set (vector) $X$ of $x$-values,
- a set (vector) $Y$ of $y$-values and
@@ -372,8 +371,8 @@ surface( -3:0.02:3, -3:0.02:3, f)
contour( -3:0.02:3, -3:0.02:3, f, fill=true, colormap=:summer, levels=20, contour_labels=false)
```
Curves (or simply point sets) in three dimensions can be plotted by calling `plot()` with 3 vectors
containing the $x$, $y$ and $z$-coordinates of the data points, respectively.
Curves (or point sets) in three dimensions are plotted by calling `plot()` with three vectors
containing the $x$, $y$, and $z$ coordinates of the data points.
```{julia}
plotlyjs()
@@ -387,25 +386,23 @@ plot(x, y, z, zcolor=reverse(z), markersize=3, markershape= :circle,
```
> We use the `plotlyjs` backend here, so the plot is interactive and can be rotated and zoomed with the mouse.
> The `plotlyjs` backend makes the plot interactive: it can be rotated and zoomed with the mouse.
### Plots.jl and _recipes_
Other packages can extend the capabilities of `Plots.jl` by defining so-called _recipes_ for special plots and data structures, see [https://docs.juliaplots.org/latest/ecosystem/](https://docs.juliaplots.org/latest/ecosystem/), e.g.:
Other packages can extend `Plots.jl` by defining so-called _recipes_ for special plots and data structures (see [https://docs.juliaplots.org/latest/ecosystem/](https://docs.juliaplots.org/latest/ecosystem/)), e.g.:
- `StatsPlots.jl` direct plotting of _Dataframes_, special statistical plots, etc. or
- `GraphRecipes.jl` [Plotting of graph structures](https://docs.juliaplots.org/latest/GraphRecipes/examples/)
- `StatsPlots.jl`: direct plotting of _DataFrames_, special statistical plots, etc.
- `GraphRecipes.jl`: [plotting of graph structures](https://docs.juliaplots.org/latest/GraphRecipes/examples/)
### A Bar Chart
For the last example, we load a package that provides over 700 free (_"public domain"_) datasets, including, for example:
For the last example, we load a package providing over 700 free (_"public domain"_) datasets, including:
- the passenger list of the _Titanic_,
- fuel consumption data of American cars from the 70s or
- historical exchange rates
provides:
- the _Titanic_ passenger list,
- fuel consumption data for American cars from the 70s, or
- historical exchange rates:
```{julia}
using RDatasets
@@ -425,7 +422,7 @@ The dataset ["Motor Trend Car Road Tests"](https://rdrr.io/r/datasets/mtcars.htm
cars = dataset("datasets", "mtcars")
```
We only need the two columns `cars.Model` and `cars.MPG` for the plot, the fuel consumption in _miles per gallon_ (more means more economical!)
We only need two columns for the plot: `cars.Model` and `cars.MPG`, the fuel consumption in _miles per gallon_ (higher is more economical).
```{julia}
theme(:bright)
@@ -445,8 +442,8 @@ bar(cars.Model, cars.MPG,
### What is Missing: Animation
Please refer to the [documentation](https://docs.juliaplots.org/latest/animations/) and only an example
(from <https://www.juliafordatascience.com/animations-with-plots-jl/>) is given:
See the [documentation](https://docs.juliaplots.org/latest/animations/); here is an example
(from <https://www.juliafordatascience.com/animations-with-plots-jl/>):
```{julia}
#| error: false
@@ -465,7 +462,7 @@ gif(anim, fps=50)
```
:::: {.content-visible when-format="pdf"}
:::: {.content-visible when-format="typst"}
```{julia}
#| echo: false
Random.seed!(123)