Jmr/v5 docs - #1747
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…es to signal and v5 pages.
dsweber2
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I also hacked together something that would let us live-fill the temporal scope start/latest data available into the docs using the metadata endpoint, but I'll put that in a PR on this
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In general this maybe centers the old API a little bit to much for a longterm version of this, but for now this works fine
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We can remove the second paragraph if preferred. I just wanted to give a little context.
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I think it's worth talking about, I'm not sure having it be the ~first thing you see in the docs is right. Seems like something for a migration section (which we may also want w/a description of the key differences (all dates vs ideosyncratic ints, different endpoint structure, a couple of translation of typical calls).
I don't think it's make or break but at some point we should probably make it like that.
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Agreed, I've moved it to a section below to allow for a more detailed discussion of the migration later
Co-authored-by: David Weber <david.weber2@pm.me>
Co-authored-by: Javier {∅, Rdz} <39629918+JavierMtzRdz@users.noreply.github.com>
JavierMtzRdz
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I have addressed previous comments.
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Agreed, I've moved it to a section below to allow for a more detailed discussion of the migration later
| | **Data Source** | [Epic Cosmos](https://cosmos.epic.com/) via [PopHIVE](https://github.com/PopHIVE/Ingest) | | ||
| | **Geographic Levels** | HHS Regions (`hhs`), nation, state | | ||
| | **Temporal Granularity** | Weekly (epiweeks; dates are Saturdays, the last day of the week) | | ||
| | **Reporting Cadence** | Weekly | |
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Reporting cadence is spotty at best. I think it's supposed to be biweekly, but we often get a whole month of lag. Not sure how to put that all into nice documentation words though
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biweekly-monthly maybe? not ideal, probably best to elaborate in text
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I selected Irregular and included a note because it varies significantly.
library(epidatr)
library(dplyr)
archive_df <- epidata_archive(
source = "pophive",
signal = "covid_pct_ed",
geo_type = "state"
)
archive_df %>%
distinct(report_time) %>%
arrange(report_time) %>%
mutate(days_since_last = as.numeric(difftime(report_time, lag(report_time), units = "days"))) %>%
count(days_since_last)
# A tibble: 13 × 2
days_since_last n
<dbl> <int>
1 1 2
2 7 1
3 8 1
4 13 1
5 14 3
6 15 2
7 16 2
8 17 1
9 19 1
10 20 1
11 27 1
12 37 1
13 NA 1
|



addresses issue(s) #ISSUE
Summary:
Adds and updates documentation for the Delphi V5 API, covering two new data sources and improvements to the V5 reference pages.
v5-signals/folder and the "Sources and Signals" subpage.TODO:
Prerequisites:
devbranchdev