Notebooks 2.0 is here.
Public
Edited
Feb 16, 2024
dataFile = await FileAttachment("fake_app_download_rating.csv").text()
dataset = d3.csvParse(dataFile, (row) => ({
app_name: row.app_name,
downloads: parseInt(row.downloads),
average_rating: parseFloat(row.average_rating),
thirty_day_keep: parseFloat(row.thirty_day_keep)
}))
w = 600
h = dataset.length * 24
// our range is limited from 0 to width - 100,
// which is for the 80 pixels on left for axis and
// 20 pixels on right for padding
xScale = d3.scaleLinear()
.domain([0, d3.max(dataset, (d) => d.downloads)])
.rangeRound([0, w - 80 - 20]);
datasetAppNames = dataset.map((d) => d.app_name)
// using scale band to work with nominal values
// the Array.map() call allows us to get a new array
// by calling a function on each item of the source array
// here it pulls out the app_name
yScale = d3.scaleBand()
.domain(datasetAppNames)
.rangeRound([20, h - 20]);
// d3 allows scaling between colors
colorScale = d3.scaleLinear()
.domain([0, d3.max(dataset, (d) => d.downloads)])
.range(['#ccf', '#88d']);
makeChart1(dataset)
makeChart1 = (dataset) => {
// sort the data by downloads
// uses built-in Array.sort() with comparator function
dataset.sort((a, b) => b.downloads - a.downloads);
const chart = d3.create("svg").attr("width", w).attr("height", h);
chart
.selectAll("rect")
.data(dataset)
.join("rect")
.attr("x", 80)
.attr("y", (d) => yScale(d.app_name))
.attr("width", 0)
.attr("height", 20)
.style("fill", (d) => colorScale(0))
.transition()
.attr("width", (d) => xScale(d.downloads))
.style("fill", (d) => colorScale(d.downloads))
.delay((d, i) => i * 35)
.duration(1500)
.transition()
.style("fill", "#88d")
.duration(1500)
.transition()
.style("fill", d => colorScale(d.downloads))
.duration(1500);
// AXES
chart
.append("g")
.classed("axis", true)
.attr("transform", `translate(80, ${h - 20})`)
.call(d3.axisBottom(xScale));
chart
.append("g")
.classed("axis", true)
.attr("transform", `translate(80,0)`)
.call(d3.axisLeft(yScale));
chart
;
return chart.node();
}
d3 = require("d3@7")
Purpose-built for displays of data
Observable is your go-to platform for exploring data and creating expressive data visualizations. Use reactive JavaScript notebooks for prototyping and a collaborative canvas for visual data exploration and dashboard creation.
