UW iSchool Course INFO 474: focused on designing and building visualizations to better understand and communicate about pressing issues.
df = FileAttachment('heart.csv').csv()
x_scale = d3
.scaleLinear()
.domain([d3.min(chart_data, d => d["x"]), d3.max(chart_data, d => d["x"])])
.range([margin.left, width - margin.right])
y_scale = d3
.scaleLinear()
.domain([d3.min(chart_data, d => d["y"]), d3.max(chart_data, d => d["y"])])
.range([height - margin.bottom, margin.top])
color_scale = d3
.scaleLinear()
.domain(_.uniqBy(chart_data.map(d => d["color"])))
.range([margin.left, width - margin.right])
x_scale(77)
chart = {
const svg = d3
.create("svg")
.attr("width", width)
.attr("height", height);
const y_axis = d3
.axisLeft()
.scale(y_scale)
.ticks(4, "f");
svg
.append('g')
.call(y_axis)
.attr('transform', 'translate(' + margin.left + ', 0)');
const x_axis = d3.axisBottom().scale(x_scale);
svg
.append('g')
.call(x_axis)
.attr('transform', 'translate(0, ' + (height - margin.bottom) + ')');
svg
.append("text")
.attr("text-anchor", "end")
.attr("x", width - margin.right)
.attr("y", height - margin.top)
.text("Age");
svg
.append("text")
.attr("text-anchor", "middle")
.attr("x", width / 2)
.attr("y", margin.top - 20)
.text("Resting Blood Pressure by Age & Gender");
svg
.append("text")
.attr("text-anchor", "end")
.attr("transform", "rotate(-90)")
.attr("y", margin.left - 30)
.attr("x", -margin.left)
.text("Resting Blood Pressure");
const dots = svg
.append('g')
.selectAll('dot')
.data(chart_data)
.enter()
.append('circle')
.attr('cx', d => x_scale(0))
.attr('cy', d => y_scale(0))
.attr('r', 4)
.style('fill', d => {
if (d["color"] === "male") {
return "blue";
} else {
return "orange";
}
})
.style('opacity', 0.4)
.transition()
.duration(500)
.attr('cx', d => x_scale(d['x']))
.attr('cy', d => y_scale(d['y']));
return svg.node();
}
width = 600
height = 400
margin = ({ top: 50, right: 10, bottom: 100, left: 50 })
import {
assignment_instructions,
observable_challenges
} from "@uw-info474/utilities"
One platform to build and deploy the best data apps
Experiment and prototype by building visualizations in live JavaScript notebooks. Collaborate with your team and decide which concepts to build out.
Use Observable Framework to build data apps locally. Use data loaders to build in any language or library, including Python, SQL, and R.
Seamlessly deploy to Observable. Test before you ship, use automatic deploy-on-commit, and ensure your projects are always up-to-date.