I’m a graphic designer and data visualiser. Colours, patterns, and data get my pulse racing!
years = Array.from({length:17},(d,i) => new Date(0).getFullYear()+36+i)
yearsKey = years.map(y => `year_${y}`)
data = new Object // you have to define data as an object first
yearsKey.map(k => data[k]= []) // you have to define data.year as an array first
fragileStatesIndex0 = await FileAttachment("2006-fragile states index.xlsx").xlsx()
flags = await FileAttachment("Flags_colors-7.csv").csv()
raw_data = [year_2006, year_2007, year_2008, year_2009, year_2010, year_2011, year_2012, year_2013, year_2014, year_2015, year_2016, year_2017, year_2018, year_2019, year_2020, year_2021, year_2022]
raw_data.map((yearData,y) => yearData.slice(1).map((obj,i) => data[yearsKey[y]][i] = new Object))
raw_data.map((yearData,y) => yearData.slice(1).map((d,i) => Object.keys(d).map((key,j) => data[yearsKey[y]][i][keys[j]] = d[key])))
data
flag_colours = new Object
flags.filter(d => d.value !== "").map(d => flag_colours[d.source.match(/\w+/)] = new Set) // I cant see any benefit from Wrangler. Why the wrangling is not stable and usable on other parts?!?!?! So I'm using the flags database
flags.filter(d => d.value !== "").map(d => flag_colours[d.source.match(/\w+/)].add(d.target)) // use .match to get rid of the flag icon in keys
Object.keys(flag_colours).map(country => flag_colours[country] = [...flag_colours[country]])// to convert each Set to an Array
flag_colours
data
tables_aq = new Object
yearsKey.map(y => tables_aq[y] = aq.from(data[y])) // generate aquero table data for each year and hold it together in tables_aq
tables_aq // aquero table data for all years
viewof yearInput = Inputs.select(years, {label: "Select the year"})
tables_aq[`year_${yearInput}`].view() // visualise the selected year's tabular data with aquero .view()
table_plot_input = data[`year_${yearInput}`] // for using Plot table you should have year_data as a separate database ready first.
table_plot_input
Type Table, then Shift-Enter. Ctrl-space for more options.
data_aq = new Object
yearsKey.map(year => data_aq[year] = tables_aq[year].objects()) // access the wrangled data by aquero via .objects()
data_aq // whatever wrangling you do with aquero will be reflected in this dataset. use this for d3. // NO you cannot use this. the wrangling stuff does not store in the table_aq and therfore not reflected in data_aq either. you have to store it while doing wranglings...
tables_aq[`year_${yearInput}`]
.derive({Year: aq.escape(d => d.Year.getFullYear())})
.derive({Rank: aq.escape(d => d.Rank.match(/\d+/).toString())}) // use aq.escape( d => ...) wherever you need to use other parameters or functions not supported by arquero
.view()
aq_wrangled_data = new Object
aq_wrangled_data[`year_${yearInput}`] = tables_aq[`year_${yearInput}`]
.derive({Year: aq.escape(d => d.Year.getFullYear())})
.derive({Rank: aq.escape(d => d.Rank.match(/\d+/).toString())})
.objects() // STORE data from aquero table wrangling. the wrangling is not reflected in the main tables_aq database automatically.
aq_wrangled_data // as you select different years in the INPUT, you generate the whole database for aq_wrangled_data for al years.
Wrangler(data[`year_${yearInput}`]) // Create an interface to wrangle your data
viewof wrangled = Wrangler(data[`year_${yearInput}`]) // Create a view of the wrangled data
wrangled.data // returns an arquero table, you can call .view() or .objects()
wrangled.code // access the code as a string
wrangled.inputs // access the current inputs configuration
// flags_colours = // To use copied code replace "data" with your own variable
// aq.from(flags)
// .fold('target')
// .groupby('source')
// .filter(d => d["value"] !== " ")
// .select('source','value')
// .orderby("source")
// .objects() // Uncomment to return an array of objects
Flags_colors-7.csv
Type Table, then Shift-Enter. Ctrl-space for more options.
country_group = d3.group(flag_file, c => c.Country)
country_group.get("🇦🇩 Andorra")
import {aq, op} from "@uwdata/arquero"
import {Wrangler, op} from "@observablehq/data-wrangler"
d3 = require("d3")
Purpose-built for displays of data
Observable is your go-to platform for exploring data and creating expressive data visualizations. Use reactive JavaScript notebooks for prototyping and a collaborative canvas for visual data exploration and dashboard creation.
