rainclouds
rainclouds!(ax, category_labels, data_array; plot_boxplots=true, plot_clouds=true, kwargs...)
Plot a violin (/histogram), boxplot and individual data points with appropriate spacing between each.
Arguments
ax
: Axis used to place all these plots onto.category_labels
: TypicallyVector{String}
with a label for each element indata_array
data_array
: TypicallyVector{Float64}
used for to represent the datapoints to plot.
Keywords
Violin/Histogram Plot Specific Keywords
Scatter Plot Specific Keywords
side_nudge
: Default value is 0.02 ifplot_boxplots
is true, otherwise0.075
default.jitter_width=0.05
: Determines the width of the scatter-plot bar in category x-axis absolute terms.
Plot type
The plot type alias for the rainclouds
function is RainClouds
.
"Raincloud" plots are a combination of a (half) violin plot, box plot and scatter plots. The three together can make an appealing and informative visual, particularly for large N datasets.
using CairoMakie
using Random
using Makie: rand_localized
####
#### Below is used for testing the plotting functionality.
####
function mockup_distribution(N)
all_possible_labels = ["Single Mode", "Double Mode", "Random Exp", "Uniform"]
category_type = rand(all_possible_labels)
if category_type == "Single Mode"
random_mean = rand_localized(0, 8)
random_spread_coef = rand_localized(0.3, 1)
data_points = random_spread_coef*randn(N) .+ random_mean
elseif category_type == "Double Mode"
random_mean = rand_localized(0, 8)
random_spread_coef = rand_localized(0.3, 1)
data_points = random_spread_coef*randn(Int(round(N/2.0))) .+ random_mean
random_mean = rand_localized(0, 8)
random_spread_coef = rand_localized(0.3, 1)
data_points = vcat(data_points, random_spread_coef*randn(Int(round(N/2.0))) .+ random_mean)
elseif category_type == "Random Exp"
data_points = randexp(N)
elseif category_type == "Uniform"
min = rand_localized(0, 4)
max = min + rand_localized(0.5, 4)
data_points = [rand_localized(min, max) for _ in 1:N]
else
error("Unidentified category.")
end
return data_points
end
function mockup_categories_and_data_array(num_categories; N = 500)
category_labels = String[]
data_array = Float64[]
for category_label in string.(('A':'Z')[1:min(num_categories, end)])
data_points = mockup_distribution(N)
append!(category_labels, fill(category_label, N))
append!(data_array, data_points)
end
return category_labels, data_array
end
category_labels, data_array = mockup_categories_and_data_array(3)
colors = Makie.wong_colors()
rainclouds(category_labels, data_array;
axis = (; xlabel = "Categories of Distributions", ylabel = "Samples", title = "My Title"),
plot_boxplots = false, cloud_width=0.5, clouds=hist, hist_bins=50,
color = colors[indexin(category_labels, unique(category_labels))])
![](/v0.21/assets/3e6e727.ADGKmF6H.png)
rainclouds(category_labels, data_array;
axis = (; ylabel = "Categories of Distributions",
xlabel = "Samples", title = "My Title"),
orientation = :horizontal,
plot_boxplots = true, cloud_width=0.5, clouds=hist,
color = colors[indexin(category_labels, unique(category_labels))])
![](/v0.21/assets/274bd75.D1JMX81w.png)
rainclouds(category_labels, data_array;
axis = (;
xlabel = "Categories of Distributions",
ylabel = "Samples",
title = "My Title"
),
plot_boxplots = true, cloud_width=0.5, clouds=hist,
color = colors[indexin(category_labels, unique(category_labels))])
![](/v0.21/assets/cdf6075.DEGr4-to.png)
rainclouds(category_labels, data_array;
axis = (;
xlabel = "Categories of Distributions",
ylabel = "Samples",
title = "My Title"
),
plot_boxplots = true, cloud_width=0.5, side = :right,
violin_limits = extrema, color = colors[indexin(category_labels, unique(category_labels))])
![](/v0.21/assets/ccf01dd.CADjQOI_.png)
rainclouds(category_labels, data_array;
axis = (;
xlabel = "Categories of Distributions",
ylabel = "Samples",
title = "My Title",
),
plot_boxplots = true, cloud_width=0.5, side = :right,
color = colors[indexin(category_labels, unique(category_labels))])
![](/v0.21/assets/587df86.BXq7-x6a.png)
more_category_labels, more_data_array = mockup_categories_and_data_array(6)
rainclouds(more_category_labels, more_data_array;
axis = (;
xlabel = "Categories of Distributions",
ylabel = "Samples",
title = "My Title",
),
plot_boxplots = true, cloud_width=0.5,
color = colors[indexin(more_category_labels, unique(more_category_labels))])
![](/v0.21/assets/b040aee.CD6lEh5r.png)
category_labels, data_array = mockup_categories_and_data_array(6)
rainclouds(category_labels, data_array;
axis = (;
xlabel = "Categories of Distributions",
ylabel = "Samples",
title = "My Title",
),
plot_boxplots = true, cloud_width=0.5,
color = colors[indexin(category_labels, unique(category_labels))])
![](/v0.21/assets/ea9f16e.BHYyzXlO.png)
4 of these, between 3 distributions Left and Right example With and Without Box Plot
fig = Figure(size = (800*2, 600*5))
colors = [Makie.wong_colors(); Makie.wong_colors()]
category_labels, data_array = mockup_categories_and_data_array(3)
rainclouds!(
Axis(fig[1, 1], title = "Left Side, with Box Plot"),
category_labels, data_array;
side = :left,
plot_boxplots = true,
color = colors[indexin(category_labels, unique(category_labels))])
rainclouds!(
Axis(fig[2, 1], title = "Left Side, without Box Plot"),
category_labels, data_array;
side = :left,
plot_boxplots = false,
color = colors[indexin(category_labels, unique(category_labels))])
rainclouds!(
Axis(fig[1, 2], title = "Right Side, with Box Plot"),
category_labels, data_array;
side = :right,
plot_boxplots = true,
color = colors[indexin(category_labels, unique(category_labels))])
rainclouds!(
Axis(fig[2, 2], title = "Right Side, without Box Plot"),
category_labels, data_array;
side = :right,
plot_boxplots = false,
color = colors[indexin(category_labels, unique(category_labels))])
# Plots wiht more categories
# dist_between_categories (0.6, 1.0)
# with and without clouds
category_labels, data_array = mockup_categories_and_data_array(12)
rainclouds!(
Axis(fig[3, 1:2], title = "More categories. Default spacing."),
category_labels, data_array;
plot_boxplots = true,
gap = 1.0,
color = colors[indexin(category_labels, unique(category_labels))])
rainclouds!(
Axis(fig[4, 1:2], title = "More categories. Adjust space. (smaller cloud widths and smaller category distances)"),
category_labels, data_array;
plot_boxplots = true,
cloud_width = 0.3,
gap = 0.5,
color = colors[indexin(category_labels, unique(category_labels))])
rainclouds!(
Axis(fig[5, 1:2], title = "More categories. Adjust space. No clouds."),
category_labels, data_array;
plot_boxplots = true,
clouds = nothing,
gap = 0.5,
color = colors[indexin(category_labels, unique(category_labels))])
supertitle = Label(fig[0, :], "Cloud Plot Testing (Scatter, Violin, Boxplot)", fontsize=30)
fig
![](/v0.21/assets/ceac5ef.DgSWGtyD.png)
Attributes
boxplot_nudge
Defaults to 0.075
Determines the distance away the boxplot should be placed from the center line when center_boxplot
is false
. This is the value used to recentering the boxplot.
boxplot_width
Defaults to 0.1
Width of the boxplot on the category axis.
center_boxplot
Defaults to true
Whether or not to center the boxplot on the category.
cloud_width
Defaults to 0.75
Determines size of violin plot. Corresponds to width
keyword arg in violin
.
clouds
Defaults to violin
[violin
, hist
, nothing
] how to show cloud plots, either as violin or histogram plots, or not at all.
color
Defaults to @inherit patchcolor
A single color, or a vector of colors, one for each point.
cycle
Defaults to [:color => :patchcolor]
No docs available.
dodge
Defaults to automatic
Vector of Integer
(length of data) of grouping variable to create multiple side-by-side boxes at the same x position
dodge_gap
Defaults to 0.01
Spacing between dodged boxes.
gap
Defaults to 0.2
Distance between elements on the main axis (depending on orientation
).
hist_bins
Defaults to 30
If clouds=hist
, this passes down the number of bins to the histogram call.
markersize
Defaults to 2.0
Size of marker used for the scatter plot.
n_dodge
Defaults to automatic
The number of categories to dodge (defaults to maximum(dodge)
)
orientation
Defaults to :vertical
Orientation of rainclouds (:vertical
or :horizontal
)
plot_boxplots
Defaults to true
Whether to show boxplots to summarize distribution of data.
show_boxplot_outliers
Defaults to false
Show outliers in the boxplot as points (usually confusing when paired with the scatter plot so the default is to not show them)
show_median
Defaults to true
Determines whether or not to have a line for the median value in the boxplot.
side
Defaults to :left
Can take values of :left
, :right
, determines where the violin plot will be, relative to the scatter points
strokewidth
Defaults to 1.0
Determines the stroke width for the outline of the boxplot.
violin_limits
Defaults to (-Inf, Inf)
Specify values to trim the violin
. Can be a Tuple
or a Function
(e.g. datalimits=extrema
)
whiskerwidth
Defaults to 0.5
The width of the Q1, Q3 whisker in the boxplot. Value as a portion of the boxplot_width
.