The goal of covidmodeldata is to download and format data
remotes::install_github("nset-ornl/covidmodeldata")The fields ending in _mdl are the modeled data. The fields not ending
with mdl match the originally reported values. If a state is skiped
such as Rhode Island, the _mdl values for that state will match the
original values while values for other states will be the modeled
values.
library(covidmodeldata)
library(tidyverse)
df <- get_nyt() %>%
format_nyt(
skip_assignment = c("44") # don't assign Rhode Island cases
)
glimpse(df)#> Observations: 50,671
#> Variables: 13
#> $ geoid <chr> "01001", "01001", "01001", "01001", "01001", "0100...
#> $ state_fips <chr> "01", "01", "01", "01", "01", "01", "01", "01", "0...
#> $ state_name <chr> "Alabama", "Alabama", "Alabama", "Alabama", "Alaba...
#> $ county_name <chr> "Autauga", "Autauga", "Autauga", "Autauga", "Autau...
#> $ date <date> 2020-03-24, 2020-03-25, 2020-03-26, 2020-03-27, 2...
#> $ total_cases <dbl> 1, 4, 6, 6, 6, 6, 7, 7, 10, 10, 12, 12, 12, 12, 12...
#> $ total_deaths <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1,...
#> $ new_cases <dbl> 0, 3, 2, 0, 0, 0, 1, 0, 3, 0, 2, 0, 0, 0, 0, 0, 5,...
#> $ new_deaths <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0,...
#> $ total_cases_mdl <dbl> 1, 4, 6, 6, 6, 6, 7, 7, 10, 10, 12, 12, 12, 12, 12...
#> $ total_deaths_mdl <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1,...
#> $ new_cases_mdl <dbl> 1, 3, 2, 0, 0, 0, 1, 0, 3, 0, 2, 0, 0, 0, 0, 0, 5,...
#> $ new_deaths_mdl <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0,...
glimpse(acs_data)
#> Observations: 3,138
#> Variables: 21
#> $ geoid <chr> "01001", "01003", "01005...
#> $ state_fips <chr> "01", "01", "01", "01", ...
#> $ state_name <chr> "Alabama", "Alabama", "A...
#> $ county_name <chr> "Autauga", "Baldwin", "B...
#> $ county_name_long <chr> "Autauga County, Alabama...
#> $ acs_total_pop_e <dbl> 54654, 205093, 22882, 20...
#> $ acs_total_pop_m <dbl> 161, 459, 275, 202, 117,...
#> $ acs_median_income_e <dbl> 29667, 28632, 18138, 222...
#> $ acs_median_income_m <dbl> 1608, 904, 1210, 1330, 1...
#> $ acs_median_age_e <dbl> 37.8, 42.8, 39.9, 39.9, ...
#> $ acs_median_age_m <dbl> 0.4, 0.3, 0.5, 1.1, 0.4,...
#> $ cbsa_code <chr> "33860", "19300", "21640...
#> $ metropolitan_division_code <chr> NA, NA, NA, NA, NA, NA, ...
#> $ csa_code <chr> "388", "380", NA, "142",...
#> $ cbsa_title <chr> "Montgomery, AL", "Daphn...
#> $ metropolitan_micropolitan_statistical_area <chr> "Metropolitan Statistica...
#> $ metropolitan_division_title <chr> NA, NA, NA, NA, NA, NA, ...
#> $ csa_title <chr> "Montgomery-Selma-Alexan...
#> $ fips_state_code <chr> "01", "01", "01", "01", ...
#> $ central_outlying_county <chr> "Central", "Central", "C...
#> $ geometry <list> [1269841, 1248372, 1240...glimpse(landscan_usa)
#> Observations: 3,138
#> Variables: 7
#> $ geoid <chr> "01001", "01003", "01005", "01007", "01009", "01011"...
#> $ night_pop <dbl> 55601, 218022, 24881, 22400, 57840, 10138, 19680, 11...
#> $ night_pop_1m <dbl> 0.05560, 0.21802, 0.02488, 0.02240, 0.05784, 0.01014...
#> $ night_pop_100k <dbl> 0.5560, 2.1802, 0.2488, 0.2240, 0.5784, 0.1014, 0.19...
#> $ day_pop <dbl> 41423, 206846, 25987, 18295, 42508, 8857, 18774, 121...
#> $ day_pop_1m <dbl> 0.04142, 0.20685, 0.02599, 0.01829, 0.04251, 0.00886...
#> $ day_pop_100k <dbl> 0.4142, 2.0685, 0.2599, 0.1830, 0.4251, 0.0886, 0.18...