The avatar for @jzavala-gonzalez
José M. Zavala González
Public
Edited
Dec 16, 2023
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median_ratio_scaler(3.3012)
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d3.extent(regional_dailies, d => d.median_ratio)
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median_ratio_scaler.domain()
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median_ratio_scaler =
d3.scaleLog([0, 1])
// .domain(d3.extent(regional_dailies, d => d.median_ratio))
.domain([0.0625, d3.max(regional_dailies, d => d.median_ratio)])
.base(2) // TODO: Add clamp left al range para que no se vaya negativo
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Plot.plot({
height: 500,
marginRight: 100,
x: {
'type': 'log',
base: 2,
domain: d3.extent(regional_dailies, d => d.median_ratio),
},
marks: [
// Plot.axisX({type: 'log'}),
Plot.rectY(regional_dailies, Plot.binX({y: "count"}, {x: "median_ratio", fy: 'region_name', tip:true})),
Plot.ruleY([0])
]
})
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regional_dailies
select region_name,
median(clientes_afectados) as day_median,
mean(clientes_afectados) as day_mean,
from regional_dailies
group by region_name
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datadb
with initial_table as (
select
strftime("timestamp", '%Y-%m-%d')::DATE as timestamp_date,
region_name,
max(totalClientsWithoutService) as clientes_afectados,
from regionsWithoutService
where "type" = 'region'
group by timestamp_date, region_name
)

select *,
median(clientes_afectados) over (partition by (region_name)) as day_median,
clientes_afectados / day_median as median_ratio
from initial_table
order by timestamp_date, region_name

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datadb
select *
from regionsWithoutService
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<style>
@import url('https://fonts.googleapis.com/css2?family=B612+Mono&display=swap');
</style>
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import {m4} from "@uwdata/m4-scalable-time-series-visualization"
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import {datadb} from "3bd1064313a4d2c3"
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