"034e36f9-7f12-46b5-aee5-4bdd6cb42d26.csv"
// use this for the "other_region" list
import {target_list} from "05afa9b80ca6b800"
Inputs.table(min_max_month)
top_campys = ["BDR", "CLE", "ICT", "SLC", "STL", "CAE", "DSM", "HVN", "SGF"]
viewof weekly = max_weekly_by_quarter
.groupby('year','week_date')
.rollup({
spend: d => op.sum(d.spend),
leads: d => op.sum(d.leads)
})
.derive({
cpl: d => d.spend / d.leads
})
.view()
viewof weekly_by_campy = max_weekly_by_quarter
.groupby('year','week_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)
})
// .select('title')
// .dedupe()
// .count()
.view()
viewof clean_max = max_weekly_by_quarter
.groupby('year','quarter', 'month','week', 'title')
.rollup({
spend: d => op.sum(d.spend),
leads: d => op.sum(d.leads)
})
.orderby('year','quarter','week',aq.desc('spend'))
// .select('title')
// .dedupe()
// .count()
.view()
spend_range = d3.extent(clean_max, d => d.spend)
lead_range = d3.extent(clean_max, d => d.leads)
viewof file1 = Inputs.file({label: "timeline"})
raw_timeline = file1.xlsx()
timeline = raw_timeline.sheet("SC2 Timeline", {headers: true})
viewof clean = aq.from(raw_data)
.filter(d => d.spend > 0)
// this target list is the download from their csv
.derive({
campaign_category: aq.escape (d => target_list.includes(d.title) == true ? d.title : 'other_region' )
})
.filter(d => d.campaign_category == 'other_region')
.filter(d => d.master_publisher_aggregated == 'Appcast Exchange')
.filter(d => d.title != 'All Other')
.filter(d => d.title != 'Min Bid')
.filter(aq.escape(d => active_campaigns.includes(d.title) == true))
// .select('title')
// .dedupe()
// .count()
.view()
max_weekly_by_quarter
.filter(d => d.year == 2023)
.filter(d => d.week > 7)
.view()
// what is the max weekly yield every quarter by campaign?
// this could potentially give us an idea of seasonality instead of an overall max, median, min view
viewof max_weekly_by_quarter = clean
.derive({
quarter: d => op.utcquarter(d.date) + 1,
week: d => op.utcweek(d.date),
month: aq.escape( d => d3.utcMonth(d.date) ),
week_date: aq.escape(d => d3.utcMonday(d.date)),
year: d => op.utcyear(d.date)
})
.view()
raw_data = file.csv({typed: true})
raw_active_campys = FileAttachment("lyft-by_campaign-2023-03-15-2023-03-15 (1).csv").csv({typed: true})
active_campaigns = aq.from(raw_active_campys)
.filter(d => d.Status == 'Active')
.select('Campaign')
.filter(d => op.includes(d.Campaign, 'Craig') == false)
// .array('Campaign')
.array('Campaign')
// 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"
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.
