README.md
In vqv/stat5730covid19: COVID-19 Data and Tools for STAT 5730
stat5730covid19
This is an rough and unpolished R data package for the students of STAT
5730. It contains data COVID-19 data from the Johns Hopkins CSSE group,
The COVID Tracking Project, the New York Times, the state executive
orders shared Google sheet collected by the Spring 2020 STAT 5730 class,
the Google Mobility Report, and other related data. The data will be
updated regularly for now, so please reinstall the package to get new
data.
Where possible, I have included FIPS codes for the geographic regions of
the United States. This is a standardized code used by the U.S. federal
government to identify states (with 2 digits) and counties (with 5
digits). In many cases, you can use the FIPS code as a key for joining
tables.
Also included with the package are county- and state-level estimates of
population, density, and median income from the U.S. Census Bureau for
2018. The GEOID variable in the data can be treated as a FIPS code.
Installation
You can install stat5730covid19 from Github with:
remotes::install_github("vqv/stat5730covid19")
Note that you will need to have the remotes
package installed in order to use
remotes::install_github().
Examples
library(stat5730covid19)
JHU CSSE
Source: https://github.com/CSSEGISandData/COVID-19
The CSSE group at Johns Hopkins has been collecting data on the global
COVID-19 pandemic. Their data includes number of cases and deaths in
different regions globally.
csse_global
#> # A tibble: 25,662 x 8
#> date province_state country_region cases deaths lat long recovered
#> <date> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 2020-01-22 <NA> Afghanistan 0 0 33 65 0
#> 2 2020-01-23 <NA> Afghanistan 0 0 33 65 0
#> 3 2020-01-24 <NA> Afghanistan 0 0 33 65 0
#> 4 2020-01-25 <NA> Afghanistan 0 0 33 65 0
#> 5 2020-01-26 <NA> Afghanistan 0 0 33 65 0
#> 6 2020-01-27 <NA> Afghanistan 0 0 33 65 0
#> 7 2020-01-28 <NA> Afghanistan 0 0 33 65 0
#> 8 2020-01-29 <NA> Afghanistan 0 0 33 65 0
#> 9 2020-01-30 <NA> Afghanistan 0 0 33 65 0
#> 10 2020-01-31 <NA> Afghanistan 0 0 33 65 0
#> # … with 25,652 more rows
csse_us
#> # A tibble: 306,628 x 14
#> date state fips cases deaths uid iso2 iso3 code3 admin2
#> <date> <chr> <chr> <dbl> <dbl> <chr> <chr> <chr> <chr> <chr>
#> 1 2020-01-22 Amer… <NA> 0 0 16.0 AS ASM 16 <NA>
#> 2 2020-01-23 Amer… <NA> 0 0 16.0 AS ASM 16 <NA>
#> 3 2020-01-24 Amer… <NA> 0 0 16.0 AS ASM 16 <NA>
#> 4 2020-01-25 Amer… <NA> 0 0 16.0 AS ASM 16 <NA>
#> 5 2020-01-26 Amer… <NA> 0 0 16.0 AS ASM 16 <NA>
#> 6 2020-01-27 Amer… <NA> 0 0 16.0 AS ASM 16 <NA>
#> 7 2020-01-28 Amer… <NA> 0 0 16.0 AS ASM 16 <NA>
#> 8 2020-01-29 Amer… <NA> 0 0 16.0 AS ASM 16 <NA>
#> 9 2020-01-30 Amer… <NA> 0 0 16.0 AS ASM 16 <NA>
#> 10 2020-01-31 Amer… <NA> 0 0 16.0 AS ASM 16 <NA>
#> # … with 306,618 more rows, and 4 more variables: country_region <chr>,
#> # lat <dbl>, long <dbl>, combined_key <chr>
csse_lookup
#> # A tibble: 3,590 x 12
#> uid iso2 iso3 code3 fips admin2 province_state country_region lat
#> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <dbl>
#> 1 4 AF AFG 4 <NA> <NA> <NA> Afghanistan 33.9
#> 2 8 AL ALB 8 <NA> <NA> <NA> Albania 41.2
#> 3 12 DZ DZA 12 <NA> <NA> <NA> Algeria 28.0
#> 4 20 AD AND 20 <NA> <NA> <NA> Andorra 42.5
#> 5 24 AO AGO 24 <NA> <NA> <NA> Angola -11.2
#> 6 28 AG ATG 28 <NA> <NA> <NA> Antigua and B… 17.1
#> 7 32 AR ARG 32 <NA> <NA> <NA> Argentina -38.4
#> 8 51 AM ARM 51 <NA> <NA> <NA> Armenia 40.1
#> 9 40 AT AUT 40 <NA> <NA> <NA> Austria 47.5
#> 10 31 AZ AZE 31 <NA> <NA> <NA> Azerbaijan 40.1
#> # … with 3,580 more rows, and 3 more variables: long <dbl>, combined_key <chr>,
#> # population <dbl>
COVID Tracking Project
Source: https://covidtracking.com
The COVID Tracking Project was initiated by journalists at The Atlantic
magazine. The data provides daily case and death counts at the state
level, as well as numbers of tests and negative results, and hospital
data.
covidtracking
#> # A tibble: 2,881 x 26
#> date state_abbr state fips cases deaths negative pending
#> <date> <chr> <chr> <chr> <dbl> <dbl> <dbl> <dbl>
#> 1 2020-04-25 AK Alas… 02 339 9 15393 NA
#> 2 2020-04-25 AL Alab… 01 6137 212 65207 NA
#> 3 2020-04-25 AR Arka… 05 2829 47 35224 NA
#> 4 2020-04-25 AS Amer… 60 0 0 3 17
#> 5 2020-04-25 AZ Ariz… 04 6280 266 56228 NA
#> 6 2020-04-25 CA Cali… 06 41137 1651 464898 NA
#> 7 2020-04-25 CO Colo… 08 12256 674 44533 NA
#> 8 2020-04-25 CT Conn… 09 24582 1862 53020 NA
#> 9 2020-04-25 DC Dist… 11 3699 165 13603 NA
#> 10 2020-04-25 DE Dela… 10 3576 112 14122 NA
#> # … with 2,871 more rows, and 18 more variables: hospitalized_currently <dbl>,
#> # hospitalized_cumulative <dbl>, in_icu_currently <dbl>,
#> # in_icu_cumulative <dbl>, on_ventilator_currently <dbl>,
#> # on_ventilator_cumulative <dbl>, recovered <dbl>, hash <chr>,
#> # date_checked <dttm>, hospitalized <dbl>, total <dbl>,
#> # total_test_results <dbl>, pos_neg <dbl>, death_increase <dbl>,
#> # hospitalized_increase <dbl>, negative_increase <dbl>,
#> # positive_increase <dbl>, total_test_results_increase <dbl>
New York Times
Source: https://github.com/nytimes/covid-19-data
Journalists and data scientists at the New York Times have been working
on collecting daily case and death counts at the U.S. county level.
nyt_county
#> # A tibble: 86,952 x 6
#> date state county fips cases deaths
#> <date> <chr> <chr> <chr> <dbl> <dbl>
#> 1 2020-01-21 Washington Snohomish 53061 1 0
#> 2 2020-01-22 Washington Snohomish 53061 1 0
#> 3 2020-01-23 Washington Snohomish 53061 1 0
#> 4 2020-01-24 Illinois Cook 17031 1 0
#> 5 2020-01-24 Washington Snohomish 53061 1 0
#> 6 2020-01-25 California Orange 06059 1 0
#> 7 2020-01-25 Illinois Cook 17031 1 0
#> 8 2020-01-25 Washington Snohomish 53061 1 0
#> 9 2020-01-26 Arizona Maricopa 04013 1 0
#> 10 2020-01-26 California Los Angeles 06037 1 0
#> # … with 86,942 more rows
STAT 5730
The students of Spring 2020 STAT 5730 collected data on the executive
orders made by U.S. state governments mandating school closures, shelter
in place (stay at home), and travel restrictions.
state_orders
#> # A tibble: 220 x 4
#> date state fips order_type
#> <date> <chr> <chr> <chr>
#> 1 2020-03-12 Indiana 18 schools closed
#> 2 2020-03-12 Maryland 24 schools closed
#> 3 2020-03-13 Louisiana 22 schools closed
#> 4 2020-03-13 Rhode Island 44 schools closed
#> 5 2020-03-13 Rhode Island 44 travel restriction
#> 6 2020-03-13 South Dakota 46 schools closed
#> 7 2020-03-13 Utah 49 schools closed
#> 8 2020-03-13 Washington 53 schools closed
#> 9 2020-03-13 West Virginia 54 schools closed
#> 10 2020-03-15 Iowa 19 schools closed
#> # … with 210 more rows
Google Mobility
Source: https://www.google.com/covid19/mobility/index.html?hl=en
Google is temporarily providing estimates of mobility in different
locations that are based on anonymized data collected from users who
have enabled location history. The values are percent change from a
baseline median value on the same day of the week during a 5 week period
from the beginning of the year.
Notes from Google:
Location accuracy and the understanding of categorized places varies
from region to region, so we don’t recommend using this data to
compare changes between countries, or between regions with different
characteristics (e.g. rural versus urban areas).
google_mobility
#> # A tibble: 1,384,968 x 9
#> country_region_… country_region sub_region_1 sub_region_2 date abbr
#> <chr> <chr> <chr> <chr> <date> <chr>
#> 1 AE United Arab E… <NA> <NA> 2020-02-15 <NA>
#> 2 AE United Arab E… <NA> <NA> 2020-02-15 <NA>
#> 3 AE United Arab E… <NA> <NA> 2020-02-15 <NA>
#> 4 AE United Arab E… <NA> <NA> 2020-02-15 <NA>
#> 5 AE United Arab E… <NA> <NA> 2020-02-15 <NA>
#> 6 AE United Arab E… <NA> <NA> 2020-02-15 <NA>
#> 7 AE United Arab E… <NA> <NA> 2020-02-16 <NA>
#> 8 AE United Arab E… <NA> <NA> 2020-02-16 <NA>
#> 9 AE United Arab E… <NA> <NA> 2020-02-16 <NA>
#> 10 AE United Arab E… <NA> <NA> 2020-02-16 <NA>
#> # … with 1,384,958 more rows, and 3 more variables: fips <chr>, type <chr>,
#> # percent_change <dbl>
U.S. Census data
census_state_pop
#> # A tibble: 52 x 4
#> state GEOID POP DENSITY
#> <chr> <chr> <dbl> <dbl>
#> 1 Alabama 01 4887871 96.5
#> 2 Alaska 02 737438 1.29
#> 3 Arizona 04 7171646 63.1
#> 4 Arkansas 05 3013825 57.9
#> 5 California 06 39557045 254.
#> 6 Colorado 08 5695564 55.0
#> 7 Connecticut 09 3572665 738.
#> 8 Delaware 10 967171 496.
#> 9 District of Columbia 11 702455 11490.
#> 10 Florida 12 21299325 397.
#> # … with 42 more rows
census_county_pop
#> # A tibble: 3,220 x 6
#> NAME GEOID county state POP DENSITY
#> <chr> <chr> <chr> <chr> <dbl> <dbl>
#> 1 Autauga County, Alabama 01001 Autauga County Alabama 55601 93.5
#> 2 Baldwin County, Alabama 01003 Baldwin County Alabama 218022 137.
#> 3 Barbour County, Alabama 01005 Barbour County Alabama 24881 28.1
#> 4 Bibb County, Alabama 01007 Bibb County Alabama 22400 36.0
#> 5 Blount County, Alabama 01009 Blount County Alabama 57840 89.7
#> 6 Bullock County, Alabama 01011 Bullock County Alabama 10138 16.3
#> 7 Butler County, Alabama 01013 Butler County Alabama 19680 25.3
#> 8 Calhoun County, Alabama 01015 Calhoun County Alabama 114277 189.
#> 9 Chambers County, Alabama 01017 Chambers County Alabama 33615 56.3
#> 10 Cherokee County, Alabama 01019 Cherokee County Alabama 26032 47.0
#> # … with 3,210 more rows
census_county_income
#> # A tibble: 3,220 x 6
#> GEOID NAME moe county state medincome
#> <chr> <chr> <dbl> <chr> <chr> <dbl>
#> 1 01001 Autauga County, Alabama 2972 Autauga County Alabama 58786
#> 2 01003 Baldwin County, Alabama 1204 Baldwin County Alabama 55962
#> 3 01005 Barbour County, Alabama 2552 Barbour County Alabama 34186
#> 4 01007 Bibb County, Alabama 5618 Bibb County Alabama 45340
#> 5 01009 Blount County, Alabama 2703 Blount County Alabama 48695
#> 6 01011 Bullock County, Alabama 10285 Bullock County Alabama 32152
#> 7 01013 Butler County, Alabama 2026 Butler County Alabama 39109
#> 8 01015 Calhoun County, Alabama 1578 Calhoun County Alabama 45197
#> 9 01017 Chambers County, Alabama 2347 Chambers County Alabama 39872
#> 10 01019 Cherokee County, Alabama 1931 Cherokee County Alabama 41014
#> # … with 3,210 more rows
vqv/stat5730covid19 documentation built on May 12, 2020, 12:39 p.m.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
stat5730covid19
This is an rough and unpolished R data package for the students of STAT 5730. It contains data COVID-19 data from the Johns Hopkins CSSE group, The COVID Tracking Project, the New York Times, the state executive orders shared Google sheet collected by the Spring 2020 STAT 5730 class, the Google Mobility Report, and other related data. The data will be updated regularly for now, so please reinstall the package to get new data.
Where possible, I have included FIPS codes for the geographic regions of the United States. This is a standardized code used by the U.S. federal government to identify states (with 2 digits) and counties (with 5 digits). In many cases, you can use the FIPS code as a key for joining tables.
Also included with the package are county- and state-level estimates of
population, density, and median income from the U.S. Census Bureau for
2018. The GEOID variable in the data can be treated as a FIPS code.
Installation
You can install stat5730covid19 from Github with:
remotes::install_github("vqv/stat5730covid19")
Note that you will need to have the remotes
package installed in order to use
remotes::install_github().
Examples
library(stat5730covid19)
JHU CSSE
Source: https://github.com/CSSEGISandData/COVID-19
The CSSE group at Johns Hopkins has been collecting data on the global COVID-19 pandemic. Their data includes number of cases and deaths in different regions globally.
csse_global
#> # A tibble: 25,662 x 8
#> date province_state country_region cases deaths lat long recovered
#> <date> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 2020-01-22 <NA> Afghanistan 0 0 33 65 0
#> 2 2020-01-23 <NA> Afghanistan 0 0 33 65 0
#> 3 2020-01-24 <NA> Afghanistan 0 0 33 65 0
#> 4 2020-01-25 <NA> Afghanistan 0 0 33 65 0
#> 5 2020-01-26 <NA> Afghanistan 0 0 33 65 0
#> 6 2020-01-27 <NA> Afghanistan 0 0 33 65 0
#> 7 2020-01-28 <NA> Afghanistan 0 0 33 65 0
#> 8 2020-01-29 <NA> Afghanistan 0 0 33 65 0
#> 9 2020-01-30 <NA> Afghanistan 0 0 33 65 0
#> 10 2020-01-31 <NA> Afghanistan 0 0 33 65 0
#> # … with 25,652 more rows
csse_us
#> # A tibble: 306,628 x 14
#> date state fips cases deaths uid iso2 iso3 code3 admin2
#> <date> <chr> <chr> <dbl> <dbl> <chr> <chr> <chr> <chr> <chr>
#> 1 2020-01-22 Amer… <NA> 0 0 16.0 AS ASM 16 <NA>
#> 2 2020-01-23 Amer… <NA> 0 0 16.0 AS ASM 16 <NA>
#> 3 2020-01-24 Amer… <NA> 0 0 16.0 AS ASM 16 <NA>
#> 4 2020-01-25 Amer… <NA> 0 0 16.0 AS ASM 16 <NA>
#> 5 2020-01-26 Amer… <NA> 0 0 16.0 AS ASM 16 <NA>
#> 6 2020-01-27 Amer… <NA> 0 0 16.0 AS ASM 16 <NA>
#> 7 2020-01-28 Amer… <NA> 0 0 16.0 AS ASM 16 <NA>
#> 8 2020-01-29 Amer… <NA> 0 0 16.0 AS ASM 16 <NA>
#> 9 2020-01-30 Amer… <NA> 0 0 16.0 AS ASM 16 <NA>
#> 10 2020-01-31 Amer… <NA> 0 0 16.0 AS ASM 16 <NA>
#> # … with 306,618 more rows, and 4 more variables: country_region <chr>,
#> # lat <dbl>, long <dbl>, combined_key <chr>
csse_lookup
#> # A tibble: 3,590 x 12
#> uid iso2 iso3 code3 fips admin2 province_state country_region lat
#> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <dbl>
#> 1 4 AF AFG 4 <NA> <NA> <NA> Afghanistan 33.9
#> 2 8 AL ALB 8 <NA> <NA> <NA> Albania 41.2
#> 3 12 DZ DZA 12 <NA> <NA> <NA> Algeria 28.0
#> 4 20 AD AND 20 <NA> <NA> <NA> Andorra 42.5
#> 5 24 AO AGO 24 <NA> <NA> <NA> Angola -11.2
#> 6 28 AG ATG 28 <NA> <NA> <NA> Antigua and B… 17.1
#> 7 32 AR ARG 32 <NA> <NA> <NA> Argentina -38.4
#> 8 51 AM ARM 51 <NA> <NA> <NA> Armenia 40.1
#> 9 40 AT AUT 40 <NA> <NA> <NA> Austria 47.5
#> 10 31 AZ AZE 31 <NA> <NA> <NA> Azerbaijan 40.1
#> # … with 3,580 more rows, and 3 more variables: long <dbl>, combined_key <chr>,
#> # population <dbl>
COVID Tracking Project
Source: https://covidtracking.com
The COVID Tracking Project was initiated by journalists at The Atlantic magazine. The data provides daily case and death counts at the state level, as well as numbers of tests and negative results, and hospital data.
covidtracking
#> # A tibble: 2,881 x 26
#> date state_abbr state fips cases deaths negative pending
#> <date> <chr> <chr> <chr> <dbl> <dbl> <dbl> <dbl>
#> 1 2020-04-25 AK Alas… 02 339 9 15393 NA
#> 2 2020-04-25 AL Alab… 01 6137 212 65207 NA
#> 3 2020-04-25 AR Arka… 05 2829 47 35224 NA
#> 4 2020-04-25 AS Amer… 60 0 0 3 17
#> 5 2020-04-25 AZ Ariz… 04 6280 266 56228 NA
#> 6 2020-04-25 CA Cali… 06 41137 1651 464898 NA
#> 7 2020-04-25 CO Colo… 08 12256 674 44533 NA
#> 8 2020-04-25 CT Conn… 09 24582 1862 53020 NA
#> 9 2020-04-25 DC Dist… 11 3699 165 13603 NA
#> 10 2020-04-25 DE Dela… 10 3576 112 14122 NA
#> # … with 2,871 more rows, and 18 more variables: hospitalized_currently <dbl>,
#> # hospitalized_cumulative <dbl>, in_icu_currently <dbl>,
#> # in_icu_cumulative <dbl>, on_ventilator_currently <dbl>,
#> # on_ventilator_cumulative <dbl>, recovered <dbl>, hash <chr>,
#> # date_checked <dttm>, hospitalized <dbl>, total <dbl>,
#> # total_test_results <dbl>, pos_neg <dbl>, death_increase <dbl>,
#> # hospitalized_increase <dbl>, negative_increase <dbl>,
#> # positive_increase <dbl>, total_test_results_increase <dbl>
New York Times
Source: https://github.com/nytimes/covid-19-data
Journalists and data scientists at the New York Times have been working on collecting daily case and death counts at the U.S. county level.
nyt_county
#> # A tibble: 86,952 x 6
#> date state county fips cases deaths
#> <date> <chr> <chr> <chr> <dbl> <dbl>
#> 1 2020-01-21 Washington Snohomish 53061 1 0
#> 2 2020-01-22 Washington Snohomish 53061 1 0
#> 3 2020-01-23 Washington Snohomish 53061 1 0
#> 4 2020-01-24 Illinois Cook 17031 1 0
#> 5 2020-01-24 Washington Snohomish 53061 1 0
#> 6 2020-01-25 California Orange 06059 1 0
#> 7 2020-01-25 Illinois Cook 17031 1 0
#> 8 2020-01-25 Washington Snohomish 53061 1 0
#> 9 2020-01-26 Arizona Maricopa 04013 1 0
#> 10 2020-01-26 California Los Angeles 06037 1 0
#> # … with 86,942 more rows
STAT 5730
The students of Spring 2020 STAT 5730 collected data on the executive orders made by U.S. state governments mandating school closures, shelter in place (stay at home), and travel restrictions.
state_orders
#> # A tibble: 220 x 4
#> date state fips order_type
#> <date> <chr> <chr> <chr>
#> 1 2020-03-12 Indiana 18 schools closed
#> 2 2020-03-12 Maryland 24 schools closed
#> 3 2020-03-13 Louisiana 22 schools closed
#> 4 2020-03-13 Rhode Island 44 schools closed
#> 5 2020-03-13 Rhode Island 44 travel restriction
#> 6 2020-03-13 South Dakota 46 schools closed
#> 7 2020-03-13 Utah 49 schools closed
#> 8 2020-03-13 Washington 53 schools closed
#> 9 2020-03-13 West Virginia 54 schools closed
#> 10 2020-03-15 Iowa 19 schools closed
#> # … with 210 more rows
Google Mobility
Source: https://www.google.com/covid19/mobility/index.html?hl=en
Google is temporarily providing estimates of mobility in different locations that are based on anonymized data collected from users who have enabled location history. The values are percent change from a baseline median value on the same day of the week during a 5 week period from the beginning of the year.
Notes from Google:
Location accuracy and the understanding of categorized places varies from region to region, so we don’t recommend using this data to compare changes between countries, or between regions with different characteristics (e.g. rural versus urban areas).
google_mobility
#> # A tibble: 1,384,968 x 9
#> country_region_… country_region sub_region_1 sub_region_2 date abbr
#> <chr> <chr> <chr> <chr> <date> <chr>
#> 1 AE United Arab E… <NA> <NA> 2020-02-15 <NA>
#> 2 AE United Arab E… <NA> <NA> 2020-02-15 <NA>
#> 3 AE United Arab E… <NA> <NA> 2020-02-15 <NA>
#> 4 AE United Arab E… <NA> <NA> 2020-02-15 <NA>
#> 5 AE United Arab E… <NA> <NA> 2020-02-15 <NA>
#> 6 AE United Arab E… <NA> <NA> 2020-02-15 <NA>
#> 7 AE United Arab E… <NA> <NA> 2020-02-16 <NA>
#> 8 AE United Arab E… <NA> <NA> 2020-02-16 <NA>
#> 9 AE United Arab E… <NA> <NA> 2020-02-16 <NA>
#> 10 AE United Arab E… <NA> <NA> 2020-02-16 <NA>
#> # … with 1,384,958 more rows, and 3 more variables: fips <chr>, type <chr>,
#> # percent_change <dbl>
U.S. Census data
census_state_pop
#> # A tibble: 52 x 4
#> state GEOID POP DENSITY
#> <chr> <chr> <dbl> <dbl>
#> 1 Alabama 01 4887871 96.5
#> 2 Alaska 02 737438 1.29
#> 3 Arizona 04 7171646 63.1
#> 4 Arkansas 05 3013825 57.9
#> 5 California 06 39557045 254.
#> 6 Colorado 08 5695564 55.0
#> 7 Connecticut 09 3572665 738.
#> 8 Delaware 10 967171 496.
#> 9 District of Columbia 11 702455 11490.
#> 10 Florida 12 21299325 397.
#> # … with 42 more rows
census_county_pop
#> # A tibble: 3,220 x 6
#> NAME GEOID county state POP DENSITY
#> <chr> <chr> <chr> <chr> <dbl> <dbl>
#> 1 Autauga County, Alabama 01001 Autauga County Alabama 55601 93.5
#> 2 Baldwin County, Alabama 01003 Baldwin County Alabama 218022 137.
#> 3 Barbour County, Alabama 01005 Barbour County Alabama 24881 28.1
#> 4 Bibb County, Alabama 01007 Bibb County Alabama 22400 36.0
#> 5 Blount County, Alabama 01009 Blount County Alabama 57840 89.7
#> 6 Bullock County, Alabama 01011 Bullock County Alabama 10138 16.3
#> 7 Butler County, Alabama 01013 Butler County Alabama 19680 25.3
#> 8 Calhoun County, Alabama 01015 Calhoun County Alabama 114277 189.
#> 9 Chambers County, Alabama 01017 Chambers County Alabama 33615 56.3
#> 10 Cherokee County, Alabama 01019 Cherokee County Alabama 26032 47.0
#> # … with 3,210 more rows
census_county_income
#> # A tibble: 3,220 x 6
#> GEOID NAME moe county state medincome
#> <chr> <chr> <dbl> <chr> <chr> <dbl>
#> 1 01001 Autauga County, Alabama 2972 Autauga County Alabama 58786
#> 2 01003 Baldwin County, Alabama 1204 Baldwin County Alabama 55962
#> 3 01005 Barbour County, Alabama 2552 Barbour County Alabama 34186
#> 4 01007 Bibb County, Alabama 5618 Bibb County Alabama 45340
#> 5 01009 Blount County, Alabama 2703 Blount County Alabama 48695
#> 6 01011 Bullock County, Alabama 10285 Bullock County Alabama 32152
#> 7 01013 Butler County, Alabama 2026 Butler County Alabama 39109
#> 8 01015 Calhoun County, Alabama 1578 Calhoun County Alabama 45197
#> 9 01017 Chambers County, Alabama 2347 Chambers County Alabama 39872
#> 10 01019 Cherokee County, Alabama 1931 Cherokee County Alabama 41014
#> # … with 3,210 more rows
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