Chapter 6. Data Visualization in Julia

import Pkg;
Pkg.add(["Tidier", "TidierData", "TidierText", "TidierStrings", "TidierPlots", "DataFrames", "CSV", "Pipelines", "Makie", "StatsPlots", "RDatasets"]);
using Tidier, TidierData, TidierText, TidierStrings, TidierPlots, DataFrames, CSV, Pipelines, Makie, StatsPlots, RDatasets
import Makie.IntervalsBetween, Makie.Attributes

TidierPlots_set("plot_log", false) # I don't not want to see many logs
TidierPlots_set("plot_show", false) # and repetitive plots in VSCode

   Resolving package versions...
  No Changes to `~/.julia/environments/v1.11/Project.toml`
  No Changes to `~/.julia/environments/v1.11/Manifest.toml`





false

Load Data

df = dataset("datasets", "iris")
first(df, 5)

5×5 DataFrame

Row	SepalLength	SepalWidth	PetalLength	PetalWidth	Species
	Float64	Float64	Float64	Float64	Cat…
1	5.1	3.5	1.4	0.2	setosa
2	4.9	3.0	1.4	0.2	setosa
3	4.7	3.2	1.3	0.2	setosa
4	4.6	3.1	1.5	0.2	setosa
5	5.0	3.6	1.4	0.2	setosa

Basic Plots

1. Scatter Plot

ggplot(data=df) +
geom_point(@aes(x = PetalLength, y = PetalWidth)) +
labs(x="Petal Length", y="Petal Width") +
theme_minimal()

2. Bar Plot

ggplot(data=df) +
geom_bar(@aes(x = PetalWidth)) +
labs(x="Petal Width") +
theme_minimal()

3. Line Plot

ggplot(data=df, @aes(x = PetalLength, y = PetalWidth)) +
geom_line() +
labs(x="Petal Length", y="Petal Width") +
theme_minimal()

4. Histogram

ggplot(data=df, @aes(x = PetalWidth)) +
geom_histogram() +
labs(x="Petal Width") +
theme_minimal()

Color and themes

1. Color

ggplot(data=df, @aes(x = PetalLength, y = PetalWidth, color = SepalWidth)) +
geom_point() +
labs(x="Petal Length", y="Petal Width") +
theme_minimal()

2. Themes

ggplot(data=df, @aes(x = PetalLength, y = PetalWidth, color = SepalWidth)) +
geom_point() +
labs(x="Petal Length", y="Petal Width") +
theme_latexfonts()

Facet

ggplot(data=df, @aes(x = PetalLength, y = PetalWidth, color = SepalWidth)) +
geom_point() +
facet_wrap("Species") +
labs(x="Petal Length", y="Petal Width") +
theme_latexfonts()

However, the supports of ggplot-like visualization cannot be identical to that of ggplot2 in R.

If you want to use R packages or Python packages for certain purposes, we can also simply use them in Julia