README.md

In BigelowLab/stcovid: stcovid - state and county centric data from COVID19 data hub

stcovid: COVID-19 cases viewer at county level by US state.

Data from COVID19 data hub provides historical cumulative counts if COIVID-19 cases.

Data from US Census accessed using tidycensus.

While this has been developed for Computational Oceanography Lab coding ecosystem and Bigelow Lab it would be easy to adapt to other settings.

Requirements

• R

• tidycensus

• sf

• rlang

• dplyr

• readr

• ggplot2

• patchwork

Installation

remotes::install_github("BigelowLab/stcovid"")

Usage

Read a daily update of cumulative cases in Maine. If you don't have the data yet then first download them with fetch_datahub.

library(stcovid)
x <- stcovid::read_datahub(state = "Maine", date = as.Date("2020-04-20"))
tail(x)
# # A tibble: 6 x 7
#   date       State County      Confirmed Recovered Hospitalizations Deaths
#   <date>     <chr> <chr>           <dbl>     <dbl>            <dbl>  <dbl>
# 1 2020-04-20 Maine Piscataquis         1         0                0      0
# 2 2020-04-20 Maine Sagadahoc          16         0                0      0
# 3 2020-04-20 Maine Somerset           16         0                0      0
# 4 2020-04-20 Maine Waldo              43         0                0      8
# 5 2020-04-20 Maine Washington          2         0                0      0
# 6 2020-04-20 Maine York              181         0                0      6

Merge it with census data. If you dont have the census data yet, you can use fetch_census_estimates to collect them.

pop <- stcovid::read_census("Maine")
pop
# Simple feature collection with 16 features and 4 fields
# geometry type:  MULTIPOLYGON
# dimension:      XY
# bbox:           xmin: -71.08392 ymin: 42.97776 xmax: -66.9499 ymax: 47.45969
# CRS:            4269
# # A tibble: 16 x 5
#    geoid County          pop density                                                                               geometry
#    <chr> <chr>         <dbl>   <dbl>                                                                     <MULTIPOLYGON [°]>
#  1 23001 Androscoggin 107679  230.   (((-70.48529 44.0604, ...
#  2 23003 Aroostook     67111   10.1  (((-70.01975 46.59217,...
#  3 23005 Cumberland   293557  351.   (((-69.94153 43.73007,...
#  4 23007 Franklin      29897   17.6  (((-70.83554 45.2938, ...
#  5 23009 Hancock       54811   34.5  (((-68.03522 44.33274,...
#  6 23011 Kennebec     122083  141.   (((-70.13259 44.37138,...
#  7 23013 Knox          39771  109.   (((-68.55926 44.04502,...
#  8 23015 Lincoln       34342   75.3  (((-69.32357 43.75899,...
#  9 23017 Oxford        57618   27.7  (((-71.08    45.30699,...
# 10 23019 Penobscot    151096   44.5  (((-69.35567 45.07347,...
# 11 23021 Piscataquis   16800    4.24 (((-69.83118 45.7386, ...
# 12 23023 Sagadahoc     35634  140.   (((-69.76031 43.71044,...
# 13 23025 Somerset      50592   12.9  (((-70.55279 45.66784,...
# 14 23027 Waldo         39694   54.4  (((-68.94229 44.28436,...
# 15 23029 Washington    31490   12.3  (((-67.3226  44.6116, ...
# 16 23031 York         206229  208.   (((-70.61725 42.99202,...

x <- stcovid::merge_census(x, pop)
x
# Simple feature collection with 16 features and 9 fields
# geometry type:  MULTIPOLYGON
# dimension:      XY
# bbox:           xmin: -71.08392 ymin: 42.97776 xmax: -66.9499 ymax: 47.45969
# CRS:            4269
# # A tibble: 16 x 10
#    date       geoid County     pop density Confirmed Recovered Hospitalizations Deaths                  geometry
#    <date>     <chr> <chr>    <dbl>   <dbl>     <dbl>     <dbl>            <dbl>  <dbl>        <MULTIPOLYGON [°]>
#  1 2020-04-20 23001 Andros… 107679  230.          35         0                0      1 (((-70.48529 44.0604, ...
#  2 2020-04-20 23003 Aroost…  67111   10.1          2         0                0      0 (((-70.01975 46.59217,...
#  3 2020-04-20 23005 Cumber… 293557  351.         380         0                0     16 (((-69.94153 43.73007,...
#  4 2020-04-20 23007 Frankl…  29897   17.6         13         0                0      0 (((-70.83554 45.2938, ...
#  5 2020-04-20 23009 Hancock  54811   34.5          6         0                0      0 (((-68.03522 44.33274,...
#  6 2020-04-20 23011 Kenneb… 122083  141.          97         0                0      4 (((-70.13259 44.37138,...
#  7 2020-04-20 23013 Knox     39771  109.          12         0                0      0 (((-68.55926 44.04502,...
#  8 2020-04-20 23015 Lincoln  34342   75.3         12         0                0      0 (((-69.32357 43.75899,...
#  9 2020-04-20 23017 Oxford   57618   27.7         14         0                0      0 (((-71.08    45.30699,...
# 10 2020-04-20 23019 Penobs… 151096   44.5         44         0                0      0 (((-69.35567 45.07347,...
# 11 2020-04-20 23021 Piscat…  16800    4.24         1         0                0      0 (((-69.83118 45.7386, ...
# 12 2020-04-20 23023 Sagada…  35634  140.          16         0                0      0 (((-69.76031 43.71044,...
# 13 2020-04-20 23025 Somers…  50592   12.9         16         0                0      0 (((-70.55279 45.66784,...
# 14 2020-04-20 23027 Waldo    39694   54.4         43         0                0      8 (((-68.94229 44.28436,...
# 15 2020-04-20 23029 Washin…  31490   12.3          2         0                0      0 (((-67.3226  44.6116, ...
# 16 2020-04-20 23031 York    206229  208.         181         0                0      6 (((-70.61725 42.99202,...

Make a graphic that shows cumulative counts and density by county.

library(patchwork)
gg <- stcovid::draw_statemap(x)
print(gg[[1]] + gg[[2]])

BigelowLab/stcovid documentation built on May 1, 2020, 3:59 p.m.