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@ctufts /.block
Last active Jun 27, 2017

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D3 Scatterplot with Regression Line
license: gpl-3.0
height: 500
scrolling: no
border: no

D3 Scatterplot with a Regression Line

This block is an extension of the Scatterplot Block with a regression line fit to the data. The data is randomly generated using the create_data function.

<!DOCTYPE html>
<meta charset="utf-8">
<title>D3 Scatterplot with Regression Line</title>
<style>
.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
}
</style>
<body>
<script src="//cdnjs.cloudflare.com/ajax/libs/d3/3.5.6/d3.min.js"></script>
<script>
var margin = {
top: 20,
right: 20,
bottom: 30,
left: 40
},
width = 960 - margin.left - margin.right,
height = 500 - margin.top - margin.bottom;
var x = d3.scale.linear()
.range([0, width]);
var y = d3.scale.linear()
.range([height, 0]);
var xAxis = d3.svg.axis()
.scale(x)
.orient("bottom");
var yAxis = d3.svg.axis()
.scale(y)
.orient("left");
var svg = d3.select("body").append("svg")
.attr("width", width + margin.left + margin.right)
.attr("height", height + margin.top + margin.bottom)
.append("g")
.attr("transform", "translate(" + margin.left + "," + margin.top + ")");
var data = create_data(1000);
data.forEach(function(d) {
d.x = +d.x;
d.y = +d.y;
d.yhat = +d.yhat;
});
var line = d3.svg.line()
.x(function(d) {
return x(d.x);
})
.y(function(d) {
return y(d.yhat);
});
x.domain(d3.extent(data, function(d) {
return d.x;
}));
y.domain(d3.extent(data, function(d) {
return d.y;
}));
svg.append("g")
.attr("class", "x axis")
.attr("transform", "translate(0," + height + ")")
.call(xAxis)
.append("text")
.attr("class", "label")
.attr("x", width)
.attr("y", -6)
.style("text-anchor", "end")
.text("X-Value");
svg.append("g")
.attr("class", "y axis")
.call(yAxis)
.append("text")
.attr("class", "label")
.attr("transform", "rotate(-90)")
.attr("y", 6)
.attr("dy", ".71em")
.style("text-anchor", "end")
.text("Y-Value")
svg.selectAll(".dot")
.data(data)
.enter().append("circle")
.attr("class", "dot")
.attr("r", 3.5)
.attr("cx", function(d) {
return x(d.x);
})
.attr("cy", function(d) {
return y(d.y);
});
svg.append("path")
.datum(data)
.attr("class", "line")
.attr("d", line);
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 = 100;
var noise = 0;
// 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 / 5 + 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>
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