Plotting in Julia

Biostat/Biomath M257

System information (for reproducibility):

versioninfo()

Julia Version 1.8.5
Commit 17cfb8e65ea (2023-01-08 06:45 UTC)
Platform Info:
  OS: macOS (arm64-apple-darwin21.5.0)
  CPU: 12 × Apple M2 Max
  WORD_SIZE: 64
  LIBM: libopenlibm
  LLVM: libLLVM-13.0.1 (ORCJIT, apple-m1)
  Threads: 1 on 8 virtual cores
Environment:
  JULIA_EDITOR = code

Load packages:

using Pkg
Pkg.activate(pwd())
Pkg.instantiate()
Pkg.status()

  Activating project at `~/Documents/github.com/ucla-biostat-257/2023spring/slides/04-juliaplot`

Status `~/Documents/github.com/ucla-biostat-257/2023spring/slides/04-juliaplot/Project.toml`
  [c91e804a] Gadfly v1.3.4
  [f0f68f2c] PlotlyJS v0.18.10
⌃ [91a5bcdd] Plots v1.38.8
  [d330b81b] PyPlot v2.11.1
  [6f49c342] RCall v0.13.14
  [9a3f8284] Random
Info Packages marked with ⌃ have new versions available and may be upgradable.

using Gadfly, Plots, PlotlyJS, PyPlot, Random, RCall

The WebIO Jupyter extension was not detected. See the WebIO Jupyter integration documentation for more information.

1 Plotting in Julia

Warning: The time to first plot can be frustratingly long in Julia because of the JIT compilation.

The five most popular options (currently) for plotting in Julia are

• Plots.jl

  • Defines an unified interface for plotting
  • Maps arguments to different plotting “backends”
    • PyPlot, GR, PlotlyJS, and many more
      
  • For a complete list of backends: http://docs.juliaplots.org/latest/backends/
    
  • Mapping of attributes to backends: http://docs.juliaplots.org/latest/supported/
    
  • First runs can be slowish. I found the GR backend fastest and most stable.

• Makie.jl

  • A pure Julia solution. Very extensible. Check GeneticsMakie.jl.

• R users can just use RCall.jl package to plot using ggplot2 in R.

• Gadfly.jl

  • Julia equivalent of ggplot2 in R

• PyPlot.jl

  • Wrapper for Python’s matplotlib

1.1 Gadfly.jl

To demonstrate Gadfly, we will go through an example and compare it to ggplot2.

using RCall

R"""
library(ggplot2)
library(dplyr)

df <- ToothGrowth %>%
  group_by(supp, dose) %>%
  summarise(se = sd(len) / n(), len = mean(len), n = n())

ggplot(df, aes(x = dose, y = len, group = supp, color = supp)) + 
  geom_line() +
  geom_point() +
  geom_errorbar(aes(ymin = len - se, ymax = len + se), width = 0.1, alpha = 0.5,
  position = position_dodge(0.005)) +  
  scale_color_manual(values = c(VC = "skyblue", OJ = "orange")) + 
  labs(x = "Dose", y = "Tooth Length")
"""

┌ Warning: RCall.jl: 
│ Attaching package: ‘dplyr’
│ 
│ The following objects are masked from ‘package:stats’:
│ 
│     filter, lag
│ 
│ The following objects are masked from ‘package:base’:
│ 
│     intersect, setdiff, setequal, union
│ 
└ @ RCall ~/.julia/packages/RCall/Wyd74/src/io.jl:172
┌ Warning: RCall.jl: `summarise()` has grouped output by 'supp'. You can override using the
│ `.groups` argument.
└ @ RCall ~/.julia/packages/RCall/Wyd74/src/io.jl:172

RObject{VecSxp}

@rget df # retrieve dataframe from R to Julia workspace
using Gadfly
df[!, :ymin] = df[!, :len] - df[!, :se]
df[!, :ymax] = df[!, :len] + df[!, :se]
Gadfly.plot(df, 
    x = :dose, 
    y = :len, 
    color = :supp, 
    Geom.point,
    Guide.xlabel("Dose"), 
    Guide.ylabel("Tooth Length"), 
    Guide.xticks(ticks = [0.5, 1.0, 1.5, 2.0]),
    Geom.line, 
    Geom.errorbar, 
    ymin = :ymin, 
    ymax = :ymax, 
    Scale.color_discrete_manual("orange", "skyblue"))

Both offer more customized options

R"""
ggplot(df, aes(x = dose, y = len, group = supp, color = supp)) + 
  geom_line() +
  geom_point() +
  geom_errorbar(aes(ymin = len - se, ymax = len + se), width = 0.1, alpha = 0.5, 
    position = position_dodge(0.005)) + 
    theme(legend.position = c(0.8,0.1), 
    legend.key = element_blank(), 
    axis.text.x = element_text(angle = 0, size = 11), 
    axis.ticks = element_blank(), 
    panel.grid.major = element_blank(), 
    legend.text=element_text(size = 11),
    panel.border = element_blank(), 
    panel.grid.minor = element_blank(), 
    panel.background = element_blank(), 
    axis.line = element_line(color = 'black',size = 0.3), 
  plot.title = element_text(hjust = 0.5)) + 
  scale_color_manual(values = c(VC = "skyblue", OJ = "orange")) + 
  labs(x = "Dose", y = "Tooth Length")
"""

┌ Warning: RCall.jl: Warning: The `size` argument of `element_line()` is deprecated as of ggplot2 3.4.0.
│ ℹ Please use the `linewidth` argument instead.
└ @ RCall ~/.julia/packages/RCall/Wyd74/src/io.jl:172

RObject{VecSxp}

Gadfly.plot(df, x = :dose, y = :len, color = :supp, Geom.point,
    Guide.xlabel("Dose"), Guide.ylabel("Tooth Length"), 
    Guide.xticks(ticks = [0.5, 1.0, 1.5, 2.0]),
    Theme(panel_fill = nothing, highlight_width = 0mm, point_size = 0.5mm,
    key_position = :inside, 
    grid_line_width = 0mm, panel_stroke = colorant"black"),
    Geom.line, Geom.errorbar, ymin = :ymin, ymax = :ymax, 
    Scale.color_discrete_manual("orange", "skyblue"))

1.2 Plots.jl

We demonstrate Plots.jl below:

# Pkg.add("Plots")
using Plots, Random

Random.seed!(123) # set seed
x = cumsum(randn(50, 2), dims=1);

# Pkg.add("PyPlot")
pyplot()  # set the backend to PyPlot
Plots.plot(x, title="Random walk", xlab="time")

gr()   # change backend to GR
Plots.plot(x, title="Random walk", xlab="time")

gr()
@gif for i in 1:20
    Plots.plot(x -> sin(x) / (.2i), 0, i, xlim=(0, 20), ylim=(-.75, .75))
    scatter!(x -> cos(x) * .01 * i, 0, i, m=1)
end;

produces following animation.

# Pkg.add("PlotlyJS")
plotlyjs()  # change backend to PlotlyJS
Plots.plot(x, title="Random walk", xlab="time")