WIP: Beeswarm plot

README.md

@@ -14,6 +14,7 @@ This package contains many statistical recipes for concepts and types introduced
     - histogram/histogram2d
     - boxplot
     - violin
+    - beeswarm
     - marginalhist
     - corrplot/cornerplot
 
@@ -25,18 +26,24 @@ using StatPlots
 gr(size=(400,300))
 ```
 
-The `DataFrames` support allows passing `DataFrame` columns as symbols. Operations on DataFrame column can be specified using quoted expressions, e.g.
+The `DataFrames` support allows passing `DataFrame` columns as
+symbols. Operations on `DataFrame` column can be specified using quoted
+expressions, e.g.
+
 ```julia
 using DataFrames
 df = DataFrame(a = 1:10, b = 10*rand(10), c = 10 * rand(10))
 plot(df, :a, [:b :c])
 scatter(df, :a, :b, markersize = :(4 * log(:c + 0.1)))
 ```
+
 If you find an operation not supported by DataFrames, please open an issue. An alternative approach to the `StatPlots` syntax is to use the [DataFramesMeta](https://github.com/JuliaStats/DataFramesMeta.jl) macro `@with`. Symbols not referring to DataFrame columns must be escaped by `^()` e.g.
+
 ```julia
 using DataFramesMeta
 @with(df, plot(:a, [:b :c], colour = ^([:red :blue])))
 ```
+
 ---
 
 ## marginalhist with DataFrames

src/StatPlots.jl

@@ -1,4 +1,3 @@
-
 module StatPlots
 
 using Reexport
@@ -24,12 +23,11 @@ include("cornerplot.jl")
 include("distributions.jl")
 include("boxplot.jl")
 include("violin.jl")
+include("beeswarm.jl")
 include("hist.jl")
 include("marginalhist.jl")
 include("bar.jl")
 include("shadederror.jl")
 include("groupederror.jl")
 
-
-
 end # module

src/beeswarm.jl

@@ -0,0 +1,50 @@
+# ---------------------------------------------------------------------------
+# Beeswarm plot
+@recipe function f(::Type{Val{:beeswarm}}, x, y, z; trim::Bool=false, side::Symbol=:both)
+    if !(side in [:both :left :right])
+        warn("side (you gave :$side) must be one of :both, :left, or :right")
+        side = :both
+        info("side set to :$side")
+    end
+    x, y = Float64[], Float64[]
+    glabels = sort(collect(unique(x)))
+    bw = d[:bar_width]
+    bw == nothing && (bw = 0.8)
+    for (i,glabel) in enumerate(glabels)
+
+        # We get the values for this label
+        lab_y = y[filter(i -> _cycle(x,i) == glabel, 1:length(y))]
+        lab_x = zeros(lab_y)
+
+        # Then we apply Sturge's rule to get the number of bins
+        n = convert(Int64, ceil(1+log2(length(lab_y))))
+
+        # Get the widths and the coordinates
+        widths, centers = violin_coords(lab_y, trim=trim, n=n)
+        isempty(widths) && continue
+
+        # normalize
+        hw = 0.5_cycle(bw, i)
+        widths = hw * widths / Plots.ignorenan_maximum(widths)
+
+        # make the violin
+        xcenter = Plots.discrete_value!(d[:subplot][:xaxis], glabel)[1]
+        if (side==:right)
+          xcoords = vcat(widths, zeros(length(widths))) + xcenter
+        elseif (side==:left)
+          xcoords = vcat(zeros(length(widths)), -reverse(widths)) + xcenter
+        else
+          xcoords = vcat(widths, -reverse(widths)) + xcenter
+        end
+        ycoords = vcat(centers, reverse(centers))
+
+        push!(xsegs, xcoords)
+        push!(ysegs, ycoords)
+    end
+
+    seriestype := :scatter
+    x := xsegs.pts
+    y := ysegs.pts
+    ()
+end
+Plots.@deps beeswarm scatter

src/hist.jl

@@ -18,9 +18,9 @@ Plots.@deps density path
 # ---------------------------------------------------------------------------
 # cumulative density
 
-@recipe function f(::Type{Val{:cdensity}}, x, y, z; trim=false,
-                   npoints = 200)
-    newx, newy = violin_coords(y, trim=trim)
+@recipe function f(::Type{Val{:cdensity}}, x, y, z; trim::Bool=false,
+                   n::Int64=200)
+    newx, newy = violin_coords(y, trim=trim, n=n)
 
     if Plots.isvertical(d)
         newx, newy = newy, newx

src/violin.jl

@@ -1,11 +1,22 @@
-
 # ---------------------------------------------------------------------------
-# Violin Plot
+# Violin plot utility functions
 
 const _violin_warned = [false]
 
-function violin_coords(y; trim::Bool=false)
-    kd = KernelDensity.kde(y, npoints = 200)
+"""
+**Use kde to return an enveloppe for the violin and beeswarm plots**
+
+~~~
+violin_coords(y; trim::Bool=false, n::Int64=200)
+~~~
+
+- `y`: points to estimate the distribution from
+- `trim`: whether to remove the extreme values
+- `n`: number of points to use in kde (defaults to 200)
+
+"""
+function violin_coords(y; trim::Bool=false, n::Int64=200)
+    kd = KernelDensity.kde(y, npoints = n)
     if trim
         xmin, xmax = Plots.ignorenan_extrema(y)
         inside = Bool[ xmin <= x <= xmax for x in kd.x]
@@ -15,7 +26,9 @@ function violin_coords(y; trim::Bool=false)
 end
 
 
-@recipe function f(::Type{Val{:violin}}, x, y, z; trim=true, side=:both)
+# ---------------------------------------------------------------------------
+# Violin plot recipe
+@recipe function f(::Type{Val{:violin}}, x, y, z; trim=false, side=:both)
     xsegs, ysegs = Segments(), Segments()
     glabels = sort(collect(unique(x)))
     bw = d[:bar_width]
@@ -49,3 +62,4 @@ end
     ()
 end
 Plots.@deps violin shape
+