md`# Grouping data exercise (Oct. 1 2019)`
d3 = require("d3@5") // require the d3 data library
// load in the list of players in the csv
players = d3.tsv("https://gist.githubusercontent.com/lathropd/0d63302f6c3c1014f2cd3846a23967a8/raw/9e7f666b7a2a83fb6d8e444215d7c46a0d60a62b/baseball.tsv")
d3.nest()
.key(d => d.POS) // nest by player position
.entries(players)
positions = d3.nest()
.key(d => d.POS)
.rollup( summarize )
.entries(players)
.map(renameColumnHeadings)
function renameColumnHeadings(d) {
let position = d.key
let count = d.value.count
let average = d.value.meanPay
return {position, count, average}
}
function summarize(players) {
let pay = players.map(d => +d["Average Pay"])
let meanPay = d3.format("$,.2~s")(d3.mean(pay))
let medianPay = d3.median(pay)
let maximumPay = d3.max(pay)
let minimumPay = d3.min(pay)
let totalForPosition = d3.sum(pay)
let count = players.length
return { pay, meanPay, medianPay, maximumPay, minimumPay, totalForPosition, count }
}
import {table} from "@tmcw/tables"
table(positions)
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.