de44fdf92a
Change-Id: Ia23f528d28126d6a2d0dd2d7327ec686c7e90df4 Reviewed-on: https://cl.tvl.fyi/c/depot/+/2158 Reviewed-by: tazjin <mail@tazj.in> Tested-by: BuildkiteCI
36 lines
1.1 KiB
Text
36 lines
1.1 KiB
Text
# This turns the CDC mortality data[0] into a format useful for my
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# excess mortality spreadsheet. The US format is by far the worst one
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# I have dealt with, as expected.
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#
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# This requires miller for transforming the CSV appropriately.
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#
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# Params:
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# state: abbreviation of the state to extract ('US' for whole country)
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# period: time period (either "2020" for current data, or anything else
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# for historical averages)
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#
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# Call as:
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# mlr --icsv --ojson cat weekly.csv | \
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# jq -rsf us_mortality.jq --arg state US --arg period 2020
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#
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# [0]: https://www.cdc.gov/nchs/nvss/vsrr/covid19/excess_deaths.htm
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def filter_period(period):
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if period == "2020"
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then . | map(select(.["Time Period"] == 2020))
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else . | map(select(.["Time Period"] == "2015-2019"))
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end;
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def collate_weeks(period):
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(. | map(.["Number of Deaths"]) | add) as $count
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| {
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count: (if period == "2020" then $count else $count / 5 end),
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week: .[0].Week,
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};
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. | map(select(.Type == "Predicted (weighted)"))
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| map(select(.["State Abbreviation"] == $state))
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| filter_period($period)
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| group_by(.Week)
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| map(collate_weeks($period))
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| .[] | "week \(.week): \(.count)"
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