An initial repo to test spatio-temporal modeling of covid-19.
There are now 4 files under data/
-
2020-03-30-covid19-nyt.csv
: a csv of the latest pull of the NYT county level data with some additional formating. Most states should be fine, but a couple of notes.- New York reports
New York City
with ageoid = NA
instead of the 5 boroughs. Here are the county fips if you want to do anything with themnyc_county_fips <- c("36061", "36047", "36081", "36005", "36085")
Unknown
county data is reported for almost all states.Kansas City, Missouri
data is reported seperately from the counties it is apart of. I wrote some code to distribute its counts into the counties using multinomial based on county population. I didn’t include it here. Will clean it up and add later if wanted
- New York reports
-
us-acs.RData
: data.frame includingsf
geometries for the whole US. Contains example variables from ACS (now including median age) as well as county membership of MSAs
washington-acs.RData
: data.frame includingsf
geometries. Contains example variables from ACS as well as county membership of MSAswashington-covid19.csv
: csv of daily county level covid19 cases and deaths.
Both of these files can be recreated with the scripts in data-raw/
I didn’t pull it into this but you might be interested in using Descartes Labs Mobility Data
mobility_df <- readr::read_csv("https://github.com/descarteslabs/DL-COVID-19/raw/master/DL-us-mobility-daterow.csv")
#> Parsed with column specification:
#> cols(
#> date = col_date(format = ""),
#> country_code = col_character(),
#> admin_level = col_double(),
#> admin1 = col_character(),
#> admin2 = col_character(),
#> fips = col_character(),
#> samples = col_double(),
#> m50 = col_double(),
#> m50_index = col_double()
#> )
knitr::kable(head(mobility_df))
date | country_code | admin_level | admin1 | admin2 | fips | samples | m50 | m50_index |
---|---|---|---|---|---|---|---|---|
2020-03-01 | US | 1 | Alabama | NA | 01 | 133826 | 8.331 | 79 |
2020-03-02 | US | 1 | Alabama | NA | 01 | 143632 | 10.398 | 98 |
2020-03-03 | US | 1 | Alabama | NA | 01 | 146009 | 10.538 | 100 |
2020-03-04 | US | 1 | Alabama | NA | 01 | 149352 | 10.144 | 96 |
2020-03-05 | US | 1 | Alabama | NA | 01 | 144109 | 10.982 | 104 |
2020-03-06 | US | 1 | Alabama | NA | 01 | 141491 | 13.024 | 123 |