This post describes how to build a very basic
connected scatter plot
with d3.js. Note that it is basically a
line chart with data points represented as
well. Learn more about the theory of connected scatter plot in
data-to-viz.com. This example works with d3.js v4
and v6
div
that will be modified by d3 later on.
svg
area. It specify the chart size and its
margin. Read more.
path
, and using the
d3.line
utility.
circle
, like for a
basic scatterplot.
<!DOCTYPE html>
<meta charset="utf-8">
<!-- Load d3.js -->
<script src="https://d3js.org/d3.v4.js"></script>
<!-- Create a div where the graph will take place -->
<div id="my_dataviz"></div>
<!DOCTYPE html>
<meta charset="utf-8">
<!-- Load d3.js -->
<script src="https://d3js.org/d3.v6.js"></script>
<!-- Create a div where the graph will take place -->
<div id="my_dataviz"></div>
<script>
// set the dimensions and margins of the graph
var margin = {top: 10, right: 30, bottom: 30, left: 60},
width = 460 - margin.left - margin.right,
height = 400 - margin.top - margin.bottom;
// append the svg object to the body of the page
var svg = d3.select("#my_dataviz")
.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 + ")");
//Read the data
d3.csv("https://raw.githubusercontent.com/holtzy/D3-graph-gallery/master/DATA/connectedscatter.csv",
// When reading the csv, I must format variables:
function(d){
return { date : d3.timeParse("%Y-%m-%d")(d.date), value : d.value }
},
// Now I can use this dataset:
function(data) {
// Add X axis --> it is a date format
var x = d3.scaleTime()
.domain(d3.extent(data, function(d) { return d.date; }))
.range([ 0, width ]);
svg.append("g")
.attr("transform", "translate(0," + height + ")")
.call(d3.axisBottom(x));
// Add Y axis
var y = d3.scaleLinear()
.domain( [8000, 9200])
.range([ height, 0 ]);
svg.append("g")
.call(d3.axisLeft(y));
// Add the line
svg.append("path")
.datum(data)
.attr("fill", "none")
.attr("stroke", "#69b3a2")
.attr("stroke-width", 1.5)
.attr("d", d3.line()
.x(function(d) { return x(d.date) })
.y(function(d) { return y(d.value) })
)
// Add the points
svg
.append("g")
.selectAll("dot")
.data(data)
.enter()
.append("circle")
.attr("cx", function(d) { return x(d.date) } )
.attr("cy", function(d) { return y(d.value) } )
.attr("r", 5)
.attr("fill", "#69b3a2")
})
</script>
<script>
// set the dimensions and margins of the graph
const margin = {top: 10, right: 30, bottom: 30, left: 60},
width = 460 - margin.left - margin.right,
height = 400 - margin.top - margin.bottom;
// append the svg object to the body of the page
const svg = d3.select("#my_dataviz")
.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})`);
//Read the data
d3.csv("https://raw.githubusercontent.com/holtzy/D3-graph-gallery/master/DATA/connectedscatter.csv",
// When reading the csv, I must format variables:
function(d){
return { date : d3.timeParse("%Y-%m-%d")(d.date), value : d.value }
}).then(
// Now I can use this dataset:
function(data) {
// Add X axis --> it is a date format
const x = d3.scaleTime()
.domain(d3.extent(data, d => d.date))
.range([ 0, width ]);
svg.append("g")
.attr("transform", "translate(0," + height + ")")
.call(d3.axisBottom(x));
// Add Y axis
const y = d3.scaleLinear()
.domain( [8000, 9200])
.range([ height, 0 ]);
svg.append("g")
.call(d3.axisLeft(y));
// Add the line
svg.append("path")
.datum(data)
.attr("fill", "none")
.attr("stroke", "#69b3a2")
.attr("stroke-width", 1.5)
.attr("d", d3.line()
.x(d => x(d.date))
.y(d => y(d.value))
)
// Add the points
svg
.append("g")
.selectAll("dot")
.data(data)
.join("circle")
.attr("cx", d => x(d.date))
.attr("cy", d => y(d.value))
.attr("r", 5)
.attr("fill", "#69b3a2")
})
</script>
Wondering what chart type you should use? Check my
Data To Viz project! It is a
comprehensive classification of chart types organized by data
input format. Get a high-resolution version of the decision tree in your
inbox now!