data = d3.csv(
"https://raw.githubusercontent.com/info474-s17/m14-layouts/master/exercise-1/data/prepped_data.csv",
d3.autoType
)
render_data_table(data.slice(0, 10))
// Nest by region
nested = d3
.nest()
.key(d => d.region)
.entries(data)
// Transform into a hierarchy (but don't yet specify the sum() function)
hierarchy = d3.hierarchy({ key: "world", values: nested }, d => d.values)
// Write a function that can compute treemap layout
make_treemap_layout = d3
.treemap()
.round(true)
.tile(d3.treemapResquarify)
.size([width, height])
// Make a color scale for the regions
color = d3
.scaleOrdinal()
.domain(nested.map(d => d.key))
.range(d3.schemeBlues[7])
// Select metric to visualize in the treemap
viewof metric = select({
title: "Metric for visualization",
options: ["fertility_rate", "life_expectancy", "gdp"],
value: "gdp"
})
// Draw the visualization
chart = {
const svg = d3
.create("svg")
.attr("width", width)
.attr("height", height);
svg.node().update = current_metric => {
make_treemap_layout(hierarchy.sum(d => d[current_metric]));
svg
.selectAll("g")
.data(hierarchy.leaves())
.join(enter => {
const gs = enter
.append("g")
.attr("transform", d => `translate(${width / 2}, ${height / 2})`);
gs.append("text")
.text(d => d.data.country_code)
.style("font-size", "10px")
.attr("y", 10);
gs.append("rect")
.style("stroke", "white")
.style("fill", d => color(d.parent.data.key))
.style("fill-opacity", .5);
return gs;
})
.transition()
.ease(d3.easeBounceOut) //uncomment for a bouncing transition
.duration(1500)
.delay((d, i) => i * 20)
.attr("transform", d => `translate(${d.x0}, ${d.y0})`)
.selectAll("rect")
.attr("width", d => d.x1 - d.x0)
.attr("height", d => d.y1 - d.y0);
};
return svg.node();
}
chart.update(metric)
// Draw the visualization
basic_chart = {
make_treemap_layout(basic_data);
const svg = d3
.create("svg")
.attr("width", width)
.attr("height", height);
svg
.selectAll("rect")
.data(hierarchy.leaves())
.join("rect")
.attr("x", d => d.x0)
.attr("width", d => d.x1 - d.x0)
.attr("y", d => d.y0)
.attr("height", d => d.y1 - d.y0)
.style("stroke", "red");
return svg.node();
}
appendix = md`## Appendix`
_ = require("lodash")
d3 = require("d3")
import { select } from "@jashkenas/inputs"
height = 500
import {
displayCaution,
render_data_table,
table_styles
} from "@info474/utilities"
table_styles
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.