Scatter plotsTensorflow (neural network)How to understand D3's paddingInnerIntroductionHow to use CSS style sheets in Observable NotebooksexerciseExplorable DataHow to differentiate between let, const, and var in JavaScript / ObservableDay 1 & Day 2, 25 Aug. & 26 Aug., 2020, Basic Commands
D3 practice, 2021-03-31
md`# D3 practice, 2021-03-31
Our task today:
`
barchart= {
const w = 900;
const h = 400;
const margin = ({top: 10, right: 10, bottom: 5, left: 20})
let dataset = [];
for (let i = 0; i < numBars; ++i) {
let randomNum = Math.random() * h;
dataset.push(randomNum)
};
let svg = d3.create("svg")
.attr("width", w)
.attr("height", h)
.style("background-color", colorBg)
let xScale = d3.scaleBand()
.domain(d3.range(dataset.length))
.range([margin.left, w - margin.right])
.paddingInner(0.05);
let yScale = d3.scaleLinear()
.domain([0, d3.max(dataset)])
.range([h - margin.bottom, margin.top]);
svg.selectAll("rect")
.data(dataset)
.join("rect")
.attr("x", (d, i) => xScale(i))
.attr("y", d => yScale(d))
.attr("width", xScale.bandwidth()) // Not xScale().bandwidth()
.attr("height", d => h - margin.bottom - yScale(d))
.style("fill", colorBars);
d3.select(newData)
.on("click", function() {
dataset = [];
for (let i = 0; i < numBars; ++i) {
let randomNum = Math.random() * h;
dataset.push(randomNum)
};
xScale.domain(d3.range(dataset.length))
yScale.domain([0, d3.max(dataset)])
svg.selectAll("rect")
.data(dataset)
.transition()
.duration(1000)
.attr("x", (d, i) => xScale(i))
.attr("y", d => yScale(d))
.attr("width", xScale.bandwidth())
.attr("height", d => h - margin.bottom - yScale(d))
})
return svg.node()
}
w = 900
h = 400
margin = ({top: 10, right: 10, bottom: 10, left: 10})
svg = d3.select("#demo")
.append("svg")
.attr("width", w)
.attr("height", h)
.style("background-color", "pink")
dataset = {
let dataset = [];
for (let i = 0; i < numBar; i++) {
let randomNum = Math.random() * 1000;
dataset.push(randomNum)
}
return dataset;
}
xScale = d3.scaleBand()
.domain(d3.range(dataset.length))
.range([0, w])
.paddingInner(0.05)
yScale = d3.scaleLinear()
.domain([0, d3.max(dataset)])
.range([h, 0]);
svg.selectAll("rect")
.data(dataset)
.join("rect")
.attr("x", function(d, i) {return xScale(i)}) // (d, i) => xScale(i)
.attr("y", d => yScale(d))
.attr("width", xScale.bandwidth())
.attr("height", d => h - yScale(d))
.style("fill", colorBar)
md`
## Be a good (event) listener!
`
d3.select(button) // Don't use "button"
.on ("click", function(){
let dataset = [];
for (let i = 0; i < numBar; i++) {
let randomNum = Math.random() * 1000;
dataset.push(randomNum)
}
xScale.domain(d3.range(dataset.length));
yScale.domain([0, d3.max(dataset)]);
svg.selectAll("rect")
.data(dataset)
.transition()
.delay((d, i) => i / dataset.length * 1000)
.ease(d3.easeLinear)
.duration(1000) //miliseconds
.attr("x", function(d, i) {return xScale(i)}) // (d, i) => xScale(i)
.attr("y", d => yScale(d))
.attr("width", xScale.bandwidth())
.attr("height", d => h - yScale(d));
})
viewof numBar = Range([1, 25], {value: 18, step: 1, label: "number of bars:"})
button = html`<button>Click for new data</button>`
d3 = require("d3@6")
import {Range} from "@observablehq/inputs"
import {color} from "@jashkenas/inputs"
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.