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Contents
# = Scatterplot
# Scatterplots can facilitate visual analysis along multiple dimensions, though care should be taken to avoid interference. In this example, we encode three dimensions: two are encoded using position, while the third is redundantly encoded as both area and color.
$:.unshift(File.dirname(__FILE__)+"/../../lib")
require 'rubyvis'
data = pv.range(100).map {|x|
OpenStruct.new({x: x, y: rand(), z: 10**(2*rand)})
}
w = 400
h = 400
x = pv.Scale.linear(0, 99).range(0, w)
y = pv.Scale.linear(0, 1).range(0, h)
c = pv.Scale.log(1, 100).range("orange", "brown")
# The root panel.
vis = pv.Panel.new()
.width(w)
.height(h)
.bottom(20)
.left(20)
.right(10)
.top(5);
# Y-axis and ticks.
vis.add(pv.Rule)
.data(y.ticks())
.bottom(y)
.strokeStyle(lambda {|d| d!=0 ? "#eee" : "#000"})
.anchor("left").add(pv.Label)
.visible(lambda {|d| d > 0 and d < 1})
.text(y.tick_format)
# X-axis and ticks.
vis.add(pv.Rule)
.data(x.ticks())
.left(x)
.stroke_style(lambda {|d| d!=0 ? "#eee" : "#000"})
.anchor("bottom").add(pv.Label)
.visible(lambda {|d| d > 0 and d < 100})
.text(x.tick_format);
#/* The dot plot! */
vis.add(pv.Panel)
.data(data)
.add(pv.Dot)
.left(lambda {|d| x.scale(d.x)})
.bottom(lambda {|d| y.scale(d.y)})
.stroke_style(lambda {|d| c.scale(d.z)})
.fill_style(lambda {|d| c.scale(d.z).alpha(0.2)})
.shape_size(lambda {|d| d.z})
.title(lambda {|d| "%0.1f" % d.z})
vis.render()
puts vis.to_svg
Version data entries
11 entries across 11 versions & 1 rubygems