This is scatter plot with a regression line fit to the data. When clicking the New Data! button the plot will update with newly genated data. The underlying template for the scatter plot is based on an example from Scott Murray's book Interactive Data Visualization.
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June 28, 2017 00:58
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Interactive Scatterplot with Regression Line
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license: gpl-3.0 | |
height: 500 | |
scrolling: no | |
border: no |
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<!DOCTYPE html> | |
<html lang="en"> | |
<head> | |
<meta charset="utf-8"> | |
<title>D3 Scatterplot with Regression Line</title> | |
<script src="https://d3js.org/d3.v4.min.js"></script> | |
<link rel="stylesheet" type="text/css" href="main.css"> | |
</head> | |
<body> | |
<button type="button">New Data!</button> | |
<p></p> | |
<script> | |
var w = 960; | |
var h = 500; | |
var padding = 30; | |
var numDataPoints = 1000; | |
//create data points | |
var dataset = create_data(numDataPoints); | |
// function for creation of line | |
var newline = d3.line() | |
.x(function(d) { | |
return xScale(d.x); | |
}) | |
.y(function(d) { | |
return yScale(d.yhat); | |
}); | |
////// Define Scales ///////////////// | |
var xScale = d3.scaleLinear() | |
.domain([0,d3.max(dataset, function(d){ | |
return d.x; | |
})]) | |
.range([padding,w - padding*2]); | |
var yScale = d3.scaleLinear() | |
.domain([ | |
d3.min(dataset, function(d){ | |
return(d.y); | |
}), | |
d3.max(dataset, function(d){ | |
return d.y; | |
})]) //y range is reversed because svg | |
.range([h-padding, padding]); | |
/////// Define Axis ////////////////////////////// | |
var xAxis = d3.axisBottom() | |
.scale(xScale); | |
var yAxis = d3.axisLeft() | |
.scale(yScale) | |
.ticks(5); | |
// create svg | |
var svg = d3.select("body") | |
.append("svg") | |
.attr("width",w) | |
.attr("height", h); | |
// cut off datapoints that are outside the axis | |
svg.append("clipPath") | |
.attr("id", "chart-area") | |
.append("rect") | |
.attr("x", padding) | |
.attr("y", padding) | |
.attr("width", w-padding * 3) | |
.attr("height", h-padding *2); | |
// append data points | |
svg.append("g") | |
.attr("id", "circles") | |
.attr("clip-path", "url(#chart-area)") | |
.selectAll("circle") | |
.data(dataset) | |
.enter() | |
.append("circle") | |
.attr("class", "dot") | |
.attr("cx", function(d){ | |
return xScale(d.x); | |
}) | |
.attr("cy", function(d){ | |
return yScale(d.y); | |
}) | |
.attr("r", 3.5); | |
// append regression line | |
svg.append("path") | |
.datum(dataset) | |
.attr("clip-path", "url(#chart-area)") | |
.attr("class", "line") | |
.attr("d", newline); | |
// append Axes /////////////////////////// | |
svg.append("g") | |
.attr("class", "x axis") | |
.attr("transform", "translate(0," + (h-padding) + ")") | |
.call(xAxis); | |
svg.append("g") | |
.attr("class", "y axis") | |
.attr("transform", "translate(" + padding + ",0)") | |
.call(yAxis); | |
d3.select("button") | |
.on("click", function(){ | |
// create new data | |
dataset = create_data(numDataPoints); | |
//Update scale domains | |
xScale.domain([0, d3.max(dataset, function(d) { return d.x; })]); | |
yScale.domain([ | |
d3.min(dataset, function(d) { return d.y; }), | |
d3.max(dataset, function(d) { return d.y; }) | |
]); | |
// update data points | |
svg.selectAll("circle") | |
.data(dataset) | |
.transition() | |
.duration(1000) | |
.attr("cx", function(d){ | |
return xScale(d.x); | |
}) | |
.attr("cy", function(d){ | |
return yScale(d.y); | |
}); | |
// update and transition regression line | |
svg.select("path") | |
.datum(dataset) | |
.transition() | |
.duration(1000) | |
.attr("d", newline);; | |
// update axis | |
svg.select(".x.axis") | |
.transition() | |
.duration(1000) | |
.call(xAxis); | |
svg.select(".y.axis") | |
.transition() | |
.duration(1000) | |
.call(yAxis); | |
}); | |
function create_data(nsamples) { | |
var x = []; | |
var y = []; | |
var n = nsamples; | |
var x_mean = 0; | |
var y_mean = 0; | |
var term1 = 0; | |
var term2 = 0; | |
var noise_factor = 2 + Math.random()*100; | |
var noise = 0; | |
var slope = (Math.random()*10) * | |
(Math.round(Math.random()) == 1 ? 1 : -1); | |
// create x and y values | |
for (var i = 0; i < n; i++) { | |
noise = noise_factor * Math.random(); | |
noise *= Math.round(Math.random()) == 1 ? 1 : -1; | |
y.push(i / slope + noise); | |
x.push(i + 1); | |
x_mean += x[i] | |
y_mean += y[i] | |
} | |
// calculate mean x and y | |
x_mean /= n; | |
y_mean /= n; | |
// calculate coefficients | |
var xr = 0; | |
var yr = 0; | |
for (i = 0; i < x.length; i++) { | |
xr = x[i] - x_mean; | |
yr = y[i] - y_mean; | |
term1 += xr * yr; | |
term2 += xr * xr; | |
} | |
var b1 = term1 / term2; | |
var b0 = y_mean - (b1 * x_mean); | |
// perform regression | |
yhat = []; | |
// fit line using coeffs | |
for (i = 0; i < x.length; i++) { | |
yhat.push(b0 + (x[i] * b1)); | |
} | |
var data = []; | |
for (i = 0; i < y.length; i++) { | |
data.push({ | |
"yhat": yhat[i], | |
"y": y[i], | |
"x": x[i] | |
}) | |
} | |
return (data); | |
} | |
</script> | |
</body> | |
</html> |
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.line { | |
stroke: #E4002B; | |
fill: none; | |
stroke-width: 3; | |
} | |
.axis path, | |
.axis line { | |
fill: none; | |
stroke: black; | |
shape-rendering: crispEdges; | |
} | |
.axis text { | |
font-size: 10px; | |
font-family: sans-serif; | |
} | |
.text-label { | |
font-size: 10px; | |
font-family: sans-serif; | |
} | |
.dot { | |
stroke: #293b47; | |
fill: #7A99AC | |
} |
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