R/cdc_social_vulnerability_index.R
cdc_social_vulnerability_index.RdThe CDC's Social Vulnerability Index (SVI), created and maintained by the Geospatial Research, Analysis, and Services Program (GRASP), uses US Census data to determine the social vulnerability of every county and tract. This index ranks each county and tract based upon 15 social factors including poverty, lack of vehicle access, and crowded housing, and groups them into four related themes: 1) Socioeconomic, 2) Housing Composition and Disability, 3) Minority Status and Language, and 4) Housing and Transportation.
cdc_social_vulnerability_index()a data.frame
Theme rankings: For each of the four themes, we summed the percentiles for the variables comprising each theme. We ordered the summed percentiles for each theme to determine theme-specific percentile rankings. The four summary theme ranking variables, detailed in the Data Dictionary below, are:
Socioeconomic - RPL_THEME1
Household Composition & Disability - RPL_THEME2
Minority Status & Language - RPL_THEME3
Housing Type & Transportation - RPL_THEME4
Overall tract rankings: We summed the sums for each theme, ordered the tracts, and then calculated overall percentile rankings. Please note; taking the sum of the sums for each theme is the same as summing individual variable rankings. The overall tract summary ranking variable is RPL_THEM
For detailed documentation, see https://svi.cdc.gov/Documents/Data/2018_SVI_Data/SVI2018Documentation.pdf
Other data-import:
acaps_government_measures_data(),
acaps_secondary_impact_data(),
apple_mobility_data(),
beoutbreakprepared_data(),
cci_us_vaccine_data(),
cdc_aggregated_projections(),
cdc_excess_deaths(),
coronadatascraper_data(),
coronanet_government_response_data(),
cov_glue_lineage_data(),
cov_glue_newick_data(),
cov_glue_snp_lineage(),
covidtracker_data(),
descartes_mobility_data(),
ecdc_data(),
econ_tracker_consumer_spending,
econ_tracker_employment,
econ_tracker_unemp_data,
economist_excess_deaths(),
financial_times_excess_deaths(),
google_mobility_data(),
government_policy_timeline(),
jhu_data(),
jhu_us_data(),
kff_icu_beds(),
nytimes_county_data(),
oecd_unemployment_data(),
owid_data(),
param_estimates_published(),
test_and_trace_data(),
us_county_geo_details(),
us_county_health_rankings(),
us_healthcare_capacity(),
us_hospital_details(),
us_state_distancing_policy(),
usa_facts_data(),
who_cases()
res = cdc_social_vulnerability_index()
head(res)
#> # A tibble: 6 × 126
#> grasp_id state_fips state st_abbr county fips location area_sqmi e_totpop
#> <dbl> <chr> <chr> <chr> <chr> <chr> <chr> <dbl> <dbl>
#> 1 1 00001 Alabama AL Autauga 01001 Autauga … 594. 55200
#> 2 2 00001 Alabama AL Baldwin 01003 Baldwin … 1590. 208107
#> 3 3 00001 Alabama AL Barbour 01005 Barbour … 885. 25782
#> 4 4 00001 Alabama AL Bibb 01007 Bibb Cou… 622. 22527
#> 5 5 00001 Alabama AL Blount 01009 Blount C… 645. 57645
#> 6 6 00001 Alabama AL Bullock 01011 Bullock … 623. 10352
#> # … with 117 more variables: m_totpop <dbl>, e_hu <dbl>, m_hu <dbl>,
#> # e_hh <dbl>, m_hh <dbl>, e_pov <dbl>, m_pov <dbl>, e_unemp <dbl>,
#> # m_unemp <dbl>, e_pci <dbl>, m_pci <dbl>, e_nohsdp <dbl>, m_nohsdp <dbl>,
#> # e_age65 <dbl>, m_age65 <dbl>, e_age17 <dbl>, m_age17 <dbl>, e_disabl <dbl>,
#> # m_disabl <dbl>, e_sngpnt <dbl>, m_sngpnt <dbl>, e_minrty <dbl>,
#> # m_minrty <dbl>, e_limeng <dbl>, m_limeng <dbl>, e_munit <dbl>,
#> # m_munit <dbl>, e_mobile <dbl>, m_mobile <dbl>, e_crowd <dbl>, …
# limit to index columns only
res %>% dplyr::select(
state_fips:e_totpop,dplyr::starts_with('rpl_'))
#> # A tibble: 3,142 × 13
#> state_fips state st_abbr county fips location area_sqmi e_totpop rpl_theme1
#> <chr> <chr> <chr> <chr> <chr> <chr> <dbl> <dbl> <dbl>
#> 1 00001 Alaba… AL Autau… 01001 Autauga… 594. 55200 0.363
#> 2 00001 Alaba… AL Baldw… 01003 Baldwin… 1590. 208107 0.223
#> 3 00001 Alaba… AL Barbo… 01005 Barbour… 885. 25782 0.978
#> 4 00001 Alaba… AL Bibb 01007 Bibb Co… 622. 22527 0.769
#> 5 00001 Alaba… AL Blount 01009 Blount … 645. 57645 0.614
#> 6 00001 Alaba… AL Bullo… 01011 Bullock… 623. 10352 0.977
#> 7 00001 Alaba… AL Butler 01013 Butler … 777. 20025 0.846
#> 8 00001 Alaba… AL Calho… 01015 Calhoun… 606. 115098 0.787
#> 9 00001 Alaba… AL Chamb… 01017 Chamber… 597. 33826 0.690
#> 10 00001 Alaba… AL Chero… 01019 Cheroke… 554. 25853 0.688
#> # … with 3,132 more rows, and 4 more variables: rpl_theme2 <dbl>,
#> # rpl_theme3 <dbl>, rpl_theme4 <dbl>, rpl_themes <dbl>