Inputs.table(final_zip_list)
Inputs.table(zip_list_summary)
act = Plot.plot({
width: 200,
marginLeft: 50,
y: {
label: null
},
marks: [
Plot.axisX({anchor: 'top', tickFormat: '%', label: 'Zip codes with Activations'}),
Plot.barX(final_zip_list.filter(d => d.status == 'active'), Plot.groupY({x: 'count'},{
y: 'title',
fill: 'has_activations',
offset: 'normalize'
}))
]
})
aq.from(zip_list_summary)
.view()
final_zip_list = clean
.filter(d => op.includes(op.lower(d.title), 'craigs') == false)
.derive({
has_activations: d => d.activations > 0 ? 'has_activations' : 'no_activations'
})
// .derive({
// has_activations: d => d.bgc > 0 ? 'has_bgc' : 'no_bgc'
// })
.select('title', 'job_postal_code', 'has_activations')
.dedupe('job_postal_code')
.derive({title: d => op.trim(d.title)})
.derive({
status: aq.escape( d => active_campys.includes(d.title) == true ? 'active' : 'paused' )
})
.orderby('title', 'status', 'has_activations')
.objects()
md `## active campaign list`
raw_active_campys = FileAttachment("lyft-by_campaign-2023-05-17-2023-05-17.csv").csv()
viewof cr = clean
.filter(d => op.includes(op.lower(d.title), 'craigs') == false)
.filter(aq.escape(d => active_campys.includes(d.title) == true))
.filter(d => d.title != 'All Other')
.groupby('title')
.rollup({
leads: d => op.sum(d.leads),
bgc: d => op.sum(d.bgc),
activations: d => op.sum(d.activations)
})
.derive({
LtoBGC: d => d.bgc / d.leads,
BGCtoA: d => d.activations / d.bgc
})
.orderby('title')
.view()
clean_campaign_filtered = clean.objects().filter(d => d.title == campaign_selector)
viewof clean = aq.from(raw_data)
.derive({date: d => op.parse_date(d.date)})
.derive({
month_date: aq.escape(d => d3.utcMonth(d.date))
})
.groupby('title', 'job_postal_code')
.rollup({
spend: d => op.sum(d.spend),
leads: d => op.sum(d.leads),
bgc: d => op.sum(d.bgc),
activations: d => op.sum(d.activations)
})
.join_left(aq.from(zips_clean), ['job_postal_code', 'zip_code'])
.derive({
latitude: d => +d.latitude,
longitude: d => +d.longitude
})
.view()
viewof clean_time = aq.from(raw_data)
.derive({date: d => op.parse_date(d.date)})
.derive({
month_date: aq.escape(d => d3.utcMonth(d.date))
})
.groupby('date', 'title')
.rollup({
spend: d => op.sum(d.spend),
leads: d => op.sum(d.leads),
bgc: d => op.sum(d.bgc),
activations: d => op.sum(d.activations)
})
.derive({
LtoBGC: d => d.bgc / d.leads,
BGCtoA: d => d.activations / d.bgc
})
.view()
raw_data = file.csv({typed: false})
zips_clean = d3.csv(zips)
// this is a really cool way of displaying tables. Much more aesthetically pleasing than the defaults of arquero and input.table
import { formatTable } from "@saneef/pretty-tables"
import {toc} from "@nebrius/indented-toc"
date_parser = d3.utcParse('%m/%d/%Y')
// sample of how to place html blocks in a grid
// html `<div style="display: grid; grid-template-columns: 1fr 1fr 1fr; column-gap: 0px; row-gap: 5px;">
// ${viewof spend}
// ${viewof cpl}
// ${viewof cpc}
// </div>`
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.
