Densely Packed Data: Hexagon Plot
Daniel Carr from George Mason University developed a type of plot called a hexagon plot that helps address the overplotting problem in Scatter 2D plots. The plane is divided into regular hexagons (or squares), counts the number of cases in each hexagon (or square), and then (by default) maps the number of cases to the a color.
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var data = [
[ "Id", "carat", "price", "y", "z", "color", "depth", "clarity", "x", "table", "cut"],
[ "1", 1.04, 6908, 6.51, 4.04, "F", 61.9, "VS2", 6.55, 58, "Premium"],
[ "2", 0.5, 1654, 5.1, 3.12, "F", 61.1, "VS2", 5.12, 60, "Premium"],
[ "3", 1.14, 4916, 6.67, 4.17, "H", 62.2, "SI1", 6.73, 57, "Ideal"],
[ "4", 0.57, 1746, 5.29, 3.3, "F", 62.1, "SI2", 5.33, 59, "Premium"],
[ "5", 0.3, 524, 4.37, 2.67, "F", 61.4, "VS2", 4.32, 55, "Very Good"],
[ "6", 2, 15053, 8.17, 4.87, "I", 59.8, "SI1", 8.12, 59, "Very Good"],
[ "7", 1.86, 14383, 7.82, 4.88, "H", 62.6, "SI1", 7.77, 58, "Very Good"],
[ "8", 0.3, 608, 4.29, 2.69, "I", 62.5, "VS1", 4.32, 54, "Ideal"],
[ "9", 0.5, 1271, 4.94, 3.22, "G", 64.9, "VS1", 4.98, 57, "Fair"],
[ "10", 2.02, 17153, 8.05, 4.95, "I", 61.2, "VS1", 8.13, 59, "Premium"],
[ "11", 0.31, 749, 4.31, 2.74, "F", 63.5, "VS2", 4.32, 55, "Very Good"],
[ "12", 1.19, 5497, 6.84, 4.22, "E", 61.9, "SI2", 6.8, 56, "Ideal"],
[ "13", 0.36, 726, 4.59, 2.82, "G", 61.7, "VS1", 4.55, 59, "Premium"],
[ "14", 0.7, 2142, 5.59, 3.57, "I", 63.6, "SI1", 5.63, 61, "Good"],
[ "15", 2, 15330, 7.8, 5.09, "E", 65.7, "SI1", 7.7, 60, "Fair"],
[ "16", 1.38, 5641, 7.24, 4.37, "H", 60.2, "SI2", 7.31, 60, "Premium"],
[ "17", 0.41, 875, 4.76, 2.93, "I", 61.7, "VVS1", 4.73, 57, "Ideal"],
[ "18", 1.03, 4407, 6.48, 4.01, "G", 62.1, "SI2", 6.44, 56, "Ideal"],
[ "19", 1.02, 4818, 6.43, 4, "D", 62.5, "SI2", 6.38, 59, "Premium"],
[ "20", 0.33, 1040, 4.44, 2.73, "F", 61.3, "VVS2", 4.46, 56, "Ideal"],
[ "21", 1.01, 5411, 6.35, 3.99, "E", 63.2, "SI1", 6.27, 59, "Good"],
[ "22", 1.19, 10079, 6.87, 4.19, "D", 61.1, "VS1", 6.84, 57, "Ideal"],
[ "23", 1.1, 5476, 6.7, 4.07, "H", 60.7, "SI1", 6.72, 59, "Premium"],
[ "24", 1.2, 11182, 6.71, 4.28, "F", 63.6, "VVS1", 6.74, 57, "Good"],
[ "25", 1.22, 6323, 6.82, 4.24, "H", 62.4, "SI1", 6.78, 57, "Very Good"],
[ "26", 1.01, 5544, 6.32, 4.11, "D", 65.1, "SI1", 6.3, 57, "Good"],
[ "27", 1.09, 4871, 6.61, 4.1, "H", 61.9, "SI1", 6.64, 56, "Ideal"],
[ "28", 1.2, 7294, 6.76, 4.17, "H", 62, "VS1", 6.69, 57, "Ideal"],
// ... (data truncated after 29 records for clarity)
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"showScatterDensity" : true,
"xAxis" : ["carat"],
"yAxis" : ["price"]
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}
}
// Create the configuration for the graph
var config = {
"binplotBins" : [100,100],
"binplotShape" : "hexagon",
"graphType" : "Scatter2D",
"scatterType" : "bin2d",
"showScatterDensity" : true,
"xAxis" : ["carat"],
"yAxis" : ["price"]
}
// Event used to create graph (optional)
var events = false
// Call the CanvasXpress function to create the graph
var cX = new CanvasXpress("canvasId", data, config, events);
</script>
</body>
</html>
library(canvasXpress)
y=read.table("https://www.canvasxpress.org/data/r/cX-dsmall-dat.txt", header=TRUE, sep="\t", quote="", row.names=1, fill=TRUE, check.names=FALSE, stringsAsFactors=FALSE)
z=read.table("https://www.canvasxpress.org/data/r/cX-dsmall-var.txt", header=TRUE, sep="\t", quote="", row.names=1, fill=TRUE, check.names=FALSE, stringsAsFactors=FALSE)
canvasXpress(
data=y,
varAnnot=z,
binplotBins=list(100, 100),
binplotShape="hexagon",
graphType="Scatter2D",
scatterType="bin2d",
showScatterDensity=TRUE,
xAxis=list("carat"),
yAxis=list("price")
)
from canvasxpress.canvas import CanvasXpress
from canvasxpress.plot import show_in_browser
if __name__ == "__main__":
# Define the chart with its data and configuration
chart: CanvasXpress = CanvasXpress(
render_to = "hexplotbinplot2",
data = {
"y": {
"data": [
[1.04, 6908],
[0.5, 1654],
[1.14, 4916],
[0.57, 1746],
[0.3, 524],
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[2.02, 17153],
[0.31, 749],
[1.19, 5497],
[0.36, 726],
[0.7, 2142],
[2, 15330],
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}
},
config = {
"binplotBins": [100, 100],
"binplotShape": "hexagon",
"graphType": "Scatter2D",
"scatterType": "bin2d",
"showScatterDensity": True,
"xAxis": ["carat"],
"yAxis": ["price"]
},
width = 600,
height = 600
)
# Display the chart in its own Web page
show_in_browser(chart)
Run this notebook:
from canvasxpress.canvas import CanvasXpress
from canvasxpress.plot import graph
graph(
CanvasXpress(
render_to = "hexplotbinplot2",
data = {
"y": {
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