The avatar for @observablehq
Observable
The end-to-end solution for building and hosting better data apps, dashboards, and reports.
Published
Curated Datasets
Edited
Jun 28, 2022
Fork of StreetEasy Data
•
15 forks
Importers
3 stars
12
Curated Datasets
Pizza ParadiseUSAFacts Higher Education DataStack Overflow Developer Survey DataMonthly Retail Trade Report DataCurated datasets
StreetEasy Real Estate Data
FAA Laser Reports DataVisualization Publication DatasetDarkCideS: Bats in Caves DataThe MoMA Collection DataU.S. Geographic DataPlot Test DataNOAA Hourly Weather DataEIA Electricity DataBandcamp Sales DataGoogle Merchandise Sales Data
Insert cell
Insert cell
Insert cell
Insert cell
Insert cell
salesSpreadsheet = FileAttachment("sales.xlsx").xlsx()
Insert cell
sales = salesSpreadsheet.sheet(0, { headers: true })
Insert cell
Inputs.table(sales)
Insert cell
Plot.plot({
marks: [
Plot.ruleY([0]),
Plot.lineY(sales, {x: "date", y: "medianAskingPrice", stroke: "neighborhood"})
]
})
Insert cell
sales
Insert cell
Insert cell
rentalsSpreadsheet = FileAttachment("rentals.xlsx").xlsx()
Insert cell
rentals = rentalsSpreadsheet.sheet(0, { headers: true })
Insert cell
Inputs.table(rentals)
Insert cell
Plot.plot({
marks: [
Plot.ruleY([0]),
Plot.lineY(rentals, {x: "date", y: "medianAskingRent", stroke: "neighborhood"})
]
})
Insert cell
rentals
Insert cell
Insert cell
Insert cell
tidyData = {
// Sales
const tidySalesMedianDaysOnMarket = pivotData(salesMedianDaysOnMarket, 'medianDaysOnMarket','sales');
const tidySalesMedianAskingPrice = pivotData(salesMedianAskingPrice, 'medianAskingPrice','sales');
const tidySalesMedianRecordedSalesPrice = pivotData(salesMedianRecordedSalesPrice, 'medianRecordedSalesPrice','sales');
const tidySalesPriceCutShare = pivotData(salesPriceCutShare, 'priceCutShare','sales');
// const tidySalesPriceIndex = pivotData(salesPriceIndex, 'priceIndex','sales');
const tidySalesRecordedSalesVolume = pivotData(salesRecordedSalesVolume , 'recordedSalesVolume','sales');
const tidySalesSaleListRatio = pivotData(salesSaleListRatio, 'saleListRatio','sales');
const tidySalesTotalInventory = pivotData(salesTotalInventory, 'totalInventory','sales');

// Rental

const tidyRentalsDiscountShare = pivotData(rentalsDiscountShare, 'discountShare','rentals');
// const tidyRentalsRentIndex = pivotData(rentalsRentIndex, 'rentIndex','rentals');
const tidyRentalsMedianAskingRent = pivotData(rentalsMedianAskingRent, 'medianAskingRent','rentals');
const tidyRentalsTotalInventory = pivotData(rentalsTotalInventory, 'totalInventory','rentals');
return d3.merge([tidySalesMedianDaysOnMarket, tidySalesMedianAskingPrice, tidySalesMedianRecordedSalesPrice, tidySalesPriceCutShare, tidySalesRecordedSalesVolume, tidySalesSaleListRatio, tidySalesTotalInventory, tidyRentalsDiscountShare, tidyRentalsMedianAskingRent, tidyRentalsTotalInventory])
}
Insert cell
pivotData = (streetEasyData, metric, type) => { // StreetEasy's data is very wide, it has column for each month + year. This function tidies the data
const returnArray = []; // the array we return
streetEasyData.forEach(record => {
dates.forEach(date => {
returnArray.push({
areaName: record['areaName'],
borough: record['Borough'],
areaType: record['areaType'],
date: date,
metric: metric,
value: +record[date],
type: type
})
})
})
return returnArray;
}
Insert cell
dates = ["2019-01","2019-02","2019-03","2019-04","2019-05","2019-06","2019-07","2019-08","2019-09","2019-10","2019-11","2019-12","2020-01","2020-02","2020-03","2020-04","2020-05","2020-06","2020-07","2020-08","2020-09","2020-10","2020-11","2020-12","2021-01","2021-02","2021-03","2021-04","2021-05","2021-06","2021-07","2021-08","2021-09","2021-10","2021-11","2021-12","2022-01","2022-02","2022-03"]
Insert cell
rolledSales = d3.rollups(tidyData.filter(d => d.areaType == "neighborhood" && d.type == "sales"), v => {
let d = v[0]
let o = {
neighborhood: d.areaName,
borough: d.borough,
date: new Date(d.date + "-01")
}
v.forEach(d => {
o[d.metric] = d.value
})
return o
},
d => d.areaName,
d => d.date)
Insert cell
Insert cell
salesWide = rolledSales.flatMap(d => d[1].map(d => d[1]))
Insert cell
rolledRentals = d3.rollups(tidyData.filter(d => d.areaType == "neighborhood" && d.type == "rentals"), v => {
let d = v[0]
let o = {
neighborhood: d.areaName,
borough: d.borough,
date: new Date(d.date + "-01")
}
v.forEach(d => {
o[d.metric] = d.value
})
return o
},
d => d.areaName,
d => d.date)
Insert cell
Insert cell
rentalsWide = rolledRentals.flatMap(d => d[1].map(d => d[1]))
Insert cell
Insert cell
Insert cell
Insert cell
salesMedianDaysOnMarket = FileAttachment("daysOnMarket_All.csv").csv()
Insert cell
Inputs.table(salesMedianDaysOnMarket)
Insert cell
Insert cell
Insert cell
Inputs.table(salesMedianAskingPrice)
Insert cell
Insert cell
Insert cell
Inputs.table(salesMedianRecordedSalesPrice)
Insert cell
Insert cell
Insert cell
Inputs.table(salesPriceCutShare)
Insert cell
Insert cell
Insert cell
Inputs.table(salesRecordedSalesVolume)
Insert cell
Insert cell
Insert cell
Inputs.table(salesSaleListRatio)
Insert cell
Insert cell
Insert cell
Inputs.table(salesTotalInventory)
Insert cell
Insert cell
Insert cell
Insert cell
Inputs.table(rentalsDiscountShare)
Insert cell
Insert cell
Insert cell
Inputs.table(rentalsMedianAskingRent)
Insert cell
Insert cell
Insert cell
Inputs.table(rentalsTotalInventory)
Insert cell

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.
Learn more