34 Geospatial
Load Packages
library(tidygeocoder)
library(tigris)
library(rmapshaper)
library(sf)
options(tigris_use_cache = TRUE)Rural Health Clinics
req <- careGroup("rural_health")@groups |>
_[["rural_health_clinic_enrollments"]] |>
_[["endpoints"]] |>
_[["identifier"]] |>
request() |>
req_url_query(
"filter[STATE]" = "GA"
)
nres <- req |>
req_url_path_append("stats") |>
providertwo:::perform_simple() |>
_[["data"]] |>
_[["found_rows"]]
res <- req |>
req_url_query(offset = 0L, size = 5000L) |>
req_perform() |>
providertwo:::resp_simple_json() |>
get_elem("^data$|headers", regex = TRUE)
rhc_enr <- as_tbl(res$data) |>
set_names(res$meta) |>
providertwo:::map_na_if() |>
roworder(`ORGANIZATION NAME`) |>
janitor::clean_names() |>
mtt(incorporation_date = providertwo:::as_date(incorporation_date),
multiple_npi_flag = char_binary(multiple_npi_flag),
address = cheapr_if_else(
cheapr::is_na(address_line_2),
address_line_1,
paste(address_line_1, address_line_2))) |>
slt(
npi,
has_mult_npi = multiple_npi_flag,
ccn,
pac = associate_id,
enid = enrollment_id,
enid_state = enrollment_state,
org_name = organization_name,
dba_name = doing_business_as_name,
inc_date = incorporation_date,
inc_state = incorporation_state,
org_type = organization_type_structure,
org_type_2 = organization_other_type_text,
proprietary_nonprofit,
address,
city,
state,
zip = zip_code)
rhc_enr |>
purse()#> - $npi c85 1639774714 1174104657 ...
#> - $has_mult_npi i85 0 0 ...
#> - $ccn c85 118915 118917 ...
#> - $pac c85 2961814694 2961814694 ...
#> - $enid c85 O20210409001138 O20211123003 ...
#> - $enid_state c85 GA GA ...
#> - $org_name c85 ACCESS MEDICAL CLINIC GEORGIA LLC ACCESS MEDIC ...
#> - $dba_name c85 NA NA ...
#> - $inc_date D85 2020-07-29 2021-01-11 ...
#> - $inc_state c85 GA GA ...
#> - $org_type c85 LLC LLC ...
#> - $org_type_2 c85 NA NA ...
#> - $proprietary_nonprofit c85 P P ...
#> - $address c85 304 N WESTBERRY ST 37 341 BYP ...
#> - $city c85 SYLVESTER HAWKINSVILLE ...
#> - $state c85 GA GA ...
#> - $zip c85 317912125 310364839 ...req <- careGroup("rural_health")@groups |>
_[["rural_health_clinic_all_owners"]] |>
_[["endpoints"]] |>
_[["identifier"]] |>
request() |>
req_url_query("filter[STATE - OWNER]" = "GA")
nres <- req |>
req_url_path_append("stats") |>
providertwo:::perform_simple() |>
_[["data"]] |>
_[["found_rows"]]
res <- req |>
req_url_query(offset = 0L, size = 5000L) |>
req_perform() |>
providertwo:::resp_simple_json() |>
get_elem("^data$|headers", regex = TRUE)
rhc_owner <- as_tbl(res$data) |>
set_names(res$meta) |>
providertwo:::map_na_if() |>
janitor::clean_names() |>
rnm(
enid = enrollment_id,
pac = associate_id_owner,
org_name = organization_name) |>
mtt(
association_date = providertwo:::as_date(association_date_owner),
address = cheapr_if_else(cheapr::is_na(address_line_2_owner), address_line_1_owner, paste(address_line_1_owner, address_line_2_owner)),
percentage_ownership = as.numeric(percentage_ownership),
role_code = as.integer(role_code_owner)
) |>
slt(
pac,
enid,
org_name,
pac_owner = associate_id,
org_name_owner = organization_name_owner,
entity = type_owner,
role_code,
role_text = role_text_owner,
association_date_owner,
first_name_owner,
middle_name_owner,
last_name_owner,
title_owner,
dba_name = doing_business_as_name_owner,
address,
city_owner,
state_owner,
zip = zip_code_owner,
percentage_ownership,
is_for_acq = created_for_acquisition_owner,
is_corp = corporation_owner,
is_llc = llc_owner,
is_med_prov_supp = medical_provider_supplier_owner,
is_mgmt_serv_co = management_services_company_owner,
is_med_staff_co = medical_staffing_company_owner,
is_holding_co = holding_company_owner,
is_invest_firm = investment_firm_owner,
is_fin_inst = financial_institution_owner,
is_consult_firm = consulting_firm_owner,
is_for_profit = for_profit_owner,
is_non_profit = non_profit_owner,
is_other_type = other_type_owner,
other_type = other_type_text_owner
) |>
mtt(
is_for_acq = char_binary(is_for_acq),
is_corp = char_binary(is_corp),
is_llc = char_binary(is_llc),
is_med_prov_supp = char_binary(is_med_prov_supp),
is_mgmt_serv_co = char_binary(is_mgmt_serv_co),
is_med_staff_co = char_binary(is_med_staff_co),
is_holding_co = char_binary(is_holding_co),
is_invest_firm = char_binary(is_invest_firm),
is_fin_inst = char_binary(is_fin_inst),
is_consult_firm = char_binary(is_consult_firm),
is_for_profit = char_binary(is_for_profit),
is_non_profit = char_binary(is_non_profit),
is_other_type = char_binary(is_other_type)
)
rhc_owner#> # A tibble: 86 × 33
#> pac enid org_name pac_owner org_name_owner entity role_code role_text
#> <chr> <chr> <chr> <chr> <chr> <chr> <int> <chr>
#> 1 0547179665 O200… TMC HAR… 87299978… TMC/HIGGINS G… O 34 5% OR GR…
#> 2 1557271731 O200… HIAWASS… 15572717… HIAWASSEE FAM… O 43 OPERATIO…
#> 3 5991616039 O200… TMC WES… 59916160… TMC WEST CARR… O 34 5% OR GR…
#> 4 1850294034 O200… BACON C… 18502940… BACON COUNTY … O 34 5% OR GR…
#> 5 1850294034 O200… BACON C… 18502940… BACON COUNTY … O 43 OPERATIO…
#> 6 2264415538 O200… BOWDON-… 22644155… BOWDON- MT. Z… O 36 5% OR GR…
#> 7 2264415538 O200… BOWDON-… 22644155… BOWDON- MT. Z… O 43 OPERATIO…
#> 8 4981502622 O200… HOSPITA… 60027245… JOHN D. ARCHB… O 43 OPERATIO…
#> 9 4981502622 O200… HOSPITA… 60027245… JOHN D. ARCHB… O 43 OPERATIO…
#> 10 4183529183 O200… PHOEBE … 08401045… PHOEBE PUTNEY… O 34 5% OR GR…
#> # ℹ 76 more rows
#> # ℹ 25 more variables: association_date_owner <chr>, first_name_owner <chr>,
#> # middle_name_owner <chr>, last_name_owner <chr>, title_owner <chr>,
#> # dba_name <chr>, address <chr>, city_owner <chr>, state_owner <chr>,
#> # zip <chr>, percentage_ownership <dbl>, is_for_acq <int>, is_corp <int>,
#> # is_llc <int>, is_med_prov_supp <int>, is_mgmt_serv_co <int>,
#> # is_med_staff_co <int>, is_holding_co <int>, is_invest_firm <int>, …#> 1.13 sec elapsedrhc_nppes |>
slt(-endpoints, -enumeration_type) |>
print(n = 100)#> # A tibble: 85 × 7
#> number addresses practiceLocations basic taxonomies identifiers
#> <chr> <list> <list> <list> <list> <list>
#> 1 1639774714 <df> <NULL> <named list> <df> <NULL>
#> 2 1174104657 <df> <NULL> <named list> <df> <NULL>
#> 3 1568100519 <df> <NULL> <named list> <df> <NULL>
#> 4 1821017831 <df> <NULL> <named list> <df> <df>
#> 5 1558898882 <df> <NULL> <named list> <df> <NULL>
#> 6 1750339511 <df> <NULL> <named list> <df> <df>
#> 7 1417906348 <df> <NULL> <named list> <df> <df>
#> 8 1538117932 <df> <NULL> <named list> <df> <df>
#> 9 1851969372 <df [2 × 9]> <NULL> <named list> <df> <NULL>
#> 10 1639211956 <df> <NULL> <named list> <df> <df>
#> 11 1881758753 <df> <NULL> <named list> <df> <df>
#> 12 1790992352 <df> <NULL> <named list> <df> <df>
#> 13 1376750976 <df> <NULL> <named list> <df> <df>
#> 14 1427333095 <df> <NULL> <named list> <df> <df>
#> 15 1821662008 <df [2 × 9]> <NULL> <named list> <df> <NULL>
#> 16 1922672120 <df> <NULL> <named list> <df> <NULL>
#> 17 1255948717 <df> <NULL> <named list> <df> <df>
#> 18 1811020407 <df> <NULL> <named list> <df> <NULL>
#> 19 1750686382 <df> <NULL> <named list> <df> <NULL>
#> 20 1700237633 <df> <NULL> <named list> <df> <NULL>
#> 21 1063832202 <df> <NULL> <named list> <df> <NULL>
#> 22 1437577350 <df> <NULL> <named list> <df> <NULL>
#> 23 1366784415 <df> <NULL> <named list> <df> <NULL>
#> 24 1912319997 <df> <NULL> <named list> <df> <NULL>
#> 25 1538586243 <df> <NULL> <named list> <df> <NULL>
#> 26 1285706879 <df> <NULL> <named list> <df> <df>
#> 27 1790088169 <df> <NULL> <named list> <df> <NULL>
#> 28 1912379082 <df> <NULL> <named list> <df> <NULL>
#> 29 1699782037 <df> <NULL> <named list> <df> <df>
#> 30 1215943915 <df> <NULL> <named list> <df> <df>
#> 31 1801801543 <df> <NULL> <named list> <df> <df>
#> 32 1366472003 <df> <NULL> <named list> <df> <df>
#> 33 1922211515 <df> <NULL> <named list> <df> <df>
#> 34 1881960151 <df [2 × 9]> <NULL> <named list> <df> <NULL>
#> 35 1801128863 <df> <NULL> <named list> <df> <NULL>
#> 36 1023332970 <df> <NULL> <named list> <df> <df>
#> 37 1649670894 <df> <NULL> <named list> <df> <NULL>
#> 38 1912430224 <df> <NULL> <named list> <df> <NULL>
#> 39 1063459899 <df> <NULL> <named list> <df> <df>
#> 40 1851334015 <df> <NULL> <named list> <df> <df>
#> 41 1467602482 <df> <NULL> <named list> <df> <NULL>
#> 42 1760632780 <df> <NULL> <named list> <df> <NULL>
#> 43 1912564618 <df> <NULL> <named list> <df> <NULL>
#> 44 1518398049 <df> <NULL> <named list> <df> <df>
#> 45 1780695775 <df> <NULL> <named list> <df> <df>
#> 46 1275714438 <df> <NULL> <named list> <df> <df>
#> 47 1376778001 <df> <NULL> <named list> <df> <df>
#> 48 1891770764 <df> <NULL> <named list> <df> <df>
#> 49 1659700763 <df> <NULL> <named list> <df> <NULL>
#> 50 1053802553 <df> <NULL> <named list> <df> <NULL>
#> 51 1386339141 <df [2 × 9]> <NULL> <named list> <df> <NULL>
#> 52 1841432044 <df> <NULL> <named list> <df> <NULL>
#> 53 1033428214 <df> <NULL> <named list> <df> <NULL>
#> 54 1811233828 <df> <NULL> <named list> <df> <NULL>
#> 55 1386708154 <df> <NULL> <named list> <df> <NULL>
#> 56 1649853862 <df> <NULL> <named list> <df> <NULL>
#> 57 1285893818 <df> <NULL> <named list> <df> <NULL>
#> 58 1972762508 <df> <NULL> <named list> <df> <NULL>
#> 59 1285132415 <df> <NULL> <named list> <df> <NULL>
#> 60 1568713873 <df> <NULL> <named list> <df> <NULL>
#> 61 1720227895 <df> <NULL> <named list> <df> <NULL>
#> 62 1073752143 <df> <NULL> <named list> <df> <NULL>
#> 63 1255570321 <df> <NULL> <named list> <df> <NULL>
#> 64 1740778828 <df> <NULL> <named list> <df> <NULL>
#> 65 1528110103 <df> <NULL> <named list> <df> <df>
#> 66 1235227752 <df> <NULL> <named list> <df> <df>
#> 67 1083816128 <df> <NULL> <named list> <df> <df>
#> 68 1891863569 <df> <NULL> <named list> <df> <df>
#> 69 1275775314 <df> <NULL> <named list> <df> <NULL>
#> 70 1871816918 <df> <df [1 × 10]> <named list> <df> <df>
#> 71 1487284956 <df> <NULL> <named list> <df> <NULL>
#> 72 1861022329 <df> <NULL> <named list> <df> <NULL>
#> 73 1063042562 <df> <NULL> <named list> <df> <NULL>
#> 74 1588294045 <df> <NULL> <named list> <df> <NULL>
#> 75 1649800111 <df> <NULL> <named list> <df> <NULL>
#> 76 1508801416 <df> <NULL> <named list> <df> <NULL>
#> 77 1992710610 <df> <NULL> <named list> <df> <df>
#> 78 1497760243 <df> <NULL> <named list> <df> <df>
#> 79 1982631321 <df> <NULL> <named list> <df> <df>
#> 80 1578682696 <df [2 × 9]> <NULL> <named list> <df> <df>
#> 81 1427384353 <df> <NULL> <named list> <df> <NULL>
#> 82 1144276452 <df> <NULL> <named list> <df> <NULL>
#> 83 1700392438 <df> <NULL> <named list> <df> <df>
#> 84 1174024665 <df [2 × 9]> <NULL> <named list> <df> <NULL>
#> 85 1629531694 <df [2 × 9]> <NULL> <named list> <df> <NULL>
#> # ℹ 1 more variable: other_names <list>rhc_nppes |>
slt(number, addresses) |>
unnest(addresses) |>
mtt(address = cheapr_if_else(cheapr::is_na(address_2), address_1, paste(address_1, address_2))) |>
slt(-address_1, -address_2, -address_type, -country_code, -country_name)#> # A tibble: 170 × 8
#> number address_purpose city state postal_code telephone_number fax_number
#> <chr> <chr> <chr> <chr> <chr> <chr> <chr>
#> 1 16397747… LOCATION SYLV… GA 317912125 229-463-7071 833-606-0…
#> 2 16397747… MAILING HARDY AR 725428002 <NA> <NA>
#> 3 11741046… LOCATION HAWK… GA 310360000 478-845-4842 833-606-0…
#> 4 11741046… MAILING HARDY AR 725428002 <NA> 870-856-2…
#> 5 15681005… LOCATION BRUN… GA 315238285 870-856-1202 870-856-2…
#> 6 15681005… MAILING HARDY AR 725428002 870-856-1202 866-500-4…
#> 7 18210178… MAILING ALMA GA 315102908 912-632-2952 912-632-8…
#> 8 18210178… LOCATION ALMA GA 315102908 912-632-2952 912-632-8…
#> 9 15588988… LOCATION NICH… GA 31554 912-632-2952 <NA>
#> 10 15588988… MAILING ALMA GA 315102922 912-632-8961 912-632-5…
#> # ℹ 160 more rows
#> # ℹ 1 more variable: address <chr>rhc_nppes |>
slt(number, basic) |>
unnest_wider(basic)#> # A tibble: 85 × 16
#> number organization_name organizational_subpart enumeration_date last_updated
#> <chr> <chr> <chr> <chr> <chr>
#> 1 16397… ACCESS MEDICAL C… NO 2020-12-04 2021-10-06
#> 2 11741… ACCESS MEDICAL C… NO 2021-04-16 2022-09-13
#> 3 15681… ACCESS MEDICAL C… NO 2022-05-20 2024-02-27
#> 4 18210… BACON COUNTY HEA… NO 2006-07-19 2012-10-10
#> 5 15588… BACON COUNTY HEA… NO 2017-05-17 2018-05-22
#> 6 17503… BOWDON- MT. ZION… YES 2006-05-04 2022-02-21
#> 7 14179… BOWDENMTZION PRI… NO 2006-05-08 2022-03-21
#> 8 15381… BOWDON- MT. ZION… NO 2006-05-05 2023-02-28
#> 9 18519… BURKE HOSPITAL C… NO 2021-06-11 2022-10-03
#> 10 16392… COFFEE REGIONAL … NO 2007-02-13 2022-09-23
#> # ℹ 75 more rows
#> # ℹ 11 more variables: certification_date <chr>, status <chr>,
#> # authorized_official_first_name <chr>, authorized_official_last_name <chr>,
#> # authorized_official_telephone_number <chr>,
#> # authorized_official_title_or_position <chr>,
#> # authorized_official_name_prefix <chr>,
#> # authorized_official_name_suffix <chr>, …rhc_nppes |>
slt(number, taxonomies) |>
unnest(taxonomies)#> # A tibble: 156 × 7
#> number code taxonomy_group desc state license primary
#> <chr> <chr> <chr> <chr> <chr> <chr> <lgl>
#> 1 1639774714 207Q00000X "193200000X - Multi-Specia… Fami… <NA> <NA> FALSE
#> 2 1639774714 261QR1300X "" Clin… <NA> <NA> TRUE
#> 3 1174104657 207Q00000X "193200000X - Multi-Specia… Fami… <NA> <NA> FALSE
#> 4 1174104657 261QR1300X "" Clin… <NA> <NA> TRUE
#> 5 1568100519 207Q00000X "193200000X - Multi-Specia… Fami… <NA> <NA> FALSE
#> 6 1568100519 261QR1300X "" Clin… <NA> <NA> TRUE
#> 7 1821017831 282NR1301X "" Gene… <NA> <NA> TRUE
#> 8 1558898882 282NR1301X "" Gene… <NA> <NA> FALSE
#> 9 1558898882 363LF0000X "193200000X - Multi-Specia… Nurs… <NA> <NA> TRUE
#> 10 1750339511 261QR1300X "" Clin… <NA> <NA> TRUE
#> # ℹ 146 more rowsrhc_nppes |>
slt(number, identifiers) |>
unnest(identifiers) |>
print(n = 100)#> # A tibble: 47 × 6
#> number code desc issuer identifier state
#> <chr> <chr> <chr> <chr> <chr> <chr>
#> 1 1821017831 05 MEDICAID <NA> 000000118C GA
#> 2 1821017831 01 Other (non-Medicare) Oscar Number 113408 GA
#> 3 1750339511 05 MEDICAID <NA> 000254064A GA
#> 4 1417906348 05 MEDICAID <NA> 000213749B GA
#> 5 1417906348 05 MEDICAID <NA> 000573262A GA
#> 6 1538117932 05 MEDICAID <NA> 000228588A GA
#> 7 1639211956 05 MEDICAID <NA> 00000448D GA
#> 8 1881758753 05 MEDICAID <NA> 00002021D GA
#> 9 1881758753 05 MEDICAID <NA> 00002021E GA
#> 10 1790992352 05 MEDICAID <NA> 00000514F GA
#> 11 1376750976 01 Other (non-Medicare) MCR CCN 11-8907 GA
#> 12 1427333095 05 MEDICAID <NA> 000548369c GA
#> 13 1255948717 05 MEDICAID <NA> 003257063A GA
#> 14 1285706879 05 MEDICAID <NA> 000195665I GA
#> 15 1285706879 05 MEDICAID <NA> 89011JU NC
#> 16 1699782037 05 MEDICAID <NA> 000001031D GA
#> 17 1215943915 05 MEDICAID <NA> 000001031B GA
#> 18 1801801543 05 MEDICAID <NA> 000001031E GA
#> 19 1366472003 05 MEDICAID <NA> 000001339G GA
#> 20 1366472003 01 Other (non-Medicare) CLIA 11D0909917 <NA>
#> 21 1922211515 01 Other (non-Medicare) CLIA 11D1056726 GA
#> 22 1922211515 05 MEDICAID <NA> 841859123C GA
#> 23 1023332970 05 MEDICAID <NA> 003110986A GA
#> 24 1063459899 05 MEDICAID <NA> 00700917A GA
#> 25 1063459899 01 Other (non-Medicare) PALMETTO GBA RAILROAD CE5080 GA
#> 26 1851334015 05 MEDICAID <NA> 00679775A GA
#> 27 1851334015 01 Other (non-Medicare) PALMETTO GBA RAILROAD CA6260 GA
#> 28 1518398049 05 MEDICAID <NA> 003155331A GA
#> 29 1518398049 01 Other (non-Medicare) Medicare Part A 11-8912 GA
#> 30 1780695775 01 Other (non-Medicare) RAILROAD MEDICARE 080137303 GA
#> 31 1275714438 01 Other (non-Medicare) RAILROAD MEDICARE 080137303 GA
#> 32 1376778001 05 MEDICAID <NA> 003112355A GA
#> 33 1891770764 05 MEDICAID <NA> 00002109E GA
#> 34 1528110103 05 MEDICAID <NA> 0000014821 GA
#> 35 1235227752 05 MEDICAID <NA> 003140349A GA
#> 36 1083816128 05 MEDICAID <NA> 251066475A GA
#> 37 1891863569 05 MEDICAID <NA> 85000124G GA
#> 38 1871816918 05 MEDICAID <NA> 003109531A GA
#> 39 1992710610 05 MEDICAID <NA> 300033792A GA
#> 40 1992710610 01 Other (non-Medicare) clinic rendering 367607289A GA
#> 41 1992710610 01 Other (non-Medicare) Medicare ID DG2920 GA
#> 42 1497760243 01 Other (non-Medicare) Clinic rendering numb… 367607289B GA
#> 43 1497760243 05 MEDICAID <NA> 540960632A GA
#> 44 1497760243 01 Other (non-Medicare) Medicare ID CH7677 GA
#> 45 1982631321 05 MEDICAID <NA> 000750274A GA
#> 46 1578682696 01 Other (non-Medicare) RHC NUMBER 11-8516 GA
#> 47 1700392438 05 MEDICAID <NA> 003213614A GArhc_nppes |>
slt(number, other_names) |>
unnest(other_names) |>
print(n = 100)#> # A tibble: 76 × 4
#> number organization_name type code
#> <chr> <chr> <chr> <chr>
#> 1 1174104657 ACCESS MEDICAL CLINIC Doing B… 3
#> 2 1821017831 BACON COUNTY RURAL HEALTH CLINIC Doing B… 3
#> 3 1558898882 NICHOLLS FAMILY HEALTHCARE Doing B… 3
#> 4 1750339511 CARROLL CO PRIMARY HEALTH CARE Doing B… 3
#> 5 1417906348 FRANKLIN CLINIC Doing B… 3
#> 6 1538117932 CARROLL COUNTY PRIMARY HEALTH CARE Doing B… 3
#> 7 1639211956 COFFEE REGIONAL FIRST CARE Doing B… 3
#> 8 1881758753 COLQUITT REGIONAL PRIMARY CARE CLINIC Doing B… 3
#> 9 1790992352 WARWICK HEALTH CARE CLINIC Doing B… 3
#> 10 1376750976 CRISP REGIONAL CONVENIENT CARE Doing B… 3
#> 11 1427333095 DAHLONEGA PEDIATRICS Doing B… 3
#> 12 1821662008 DMC INTERNAL MEDICINE AND FAMILY HEALTH Doing B… 3
#> 13 1821662008 DMC INTERNAL AND FAMILY MEDICINE CLINIC Doing B… 3
#> 14 1922672120 DMC PRIMARY CARE Doing B… 3
#> 15 1255948717 EASTMAN FAMILY PRACTICE Doing B… 3
#> 16 1811020407 EFFINGHAM FAMILY MEDICINE AT GOSHEN Doing B… 3
#> 17 1750686382 EFFINGHAM FAMILY MEDICINE AT TWENTY-ONE Doing B… 3
#> 18 1700237633 ELLIJAY URGENT CARE & FAMILY PRACTICE Doing B… 3
#> 19 1063832202 RAY MEDICAL CLINIC Doing B… 3
#> 20 1437577350 EMANUEL FAMILY MEDICAL CLINIC Doing B… 3
#> 21 1366784415 SWAINSBORO INTERNAL MEDICINE Doing B… 3
#> 22 1912319997 TWIN CITY FAMILY MEDICAL CLINIC Doing B… 3
#> 23 1538586243 PORTER FAMILY MEDICAL CLINIC Doing B… 3
#> 24 1790088169 CANDLER MEDICAL GROUP RHC Doing B… 3
#> 25 1912379082 JEFF DAVIS FAMILY MEDICAL GROUP Doing B… 3
#> 26 1699782037 PHYSICIANS HEALTH GROUP WADLEY Doing B… 3
#> 27 1215943915 PHYSICIANS' HEALTH GROUP Doing B… 3
#> 28 1801801543 WRENS PHYSICIANS' HEALTH GROUP Doing B… 3
#> 29 1366472003 THE CAMILLA PEDIATRIC CENTER Doing B… 3
#> 30 1922211515 MEDICAL GROUP OF MITCHELL COUNTY Doing B… 3
#> 31 1881960151 ARCHBOLD - PELHAM PRIMARY CARE CLINIC Doing B… 3
#> 32 1801128863 COMMUNITY MEDICAL ASSOCIATES OF LINCOLNTON Doing B… 3
#> 33 1023332970 COMMUNITY MEDICAL ASSOCIATES OF WASHINGTON Doing B… 3
#> 34 1649670894 IRWIN COUNTY HOSPITAL AUTHORITY Former … 4
#> 35 1649670894 WILLACOOCHEE CLINIC Doing B… 3
#> 36 1912430224 LIFEBRITE FAMILY MEDICAL OF EARLY Doing B… 3
#> 37 1063459899 MEDICAL ASSOCIATES Doing B… 3
#> 38 1851334015 NORTH JEFFERSON MEDICAL CENTER Doing B… 3
#> 39 1467602482 CHI MEMORIAL FAMILY PRACTICE ASSOCIATES - TRENTON Doing B… 3
#> 40 1760632780 CHI MEMORIAL FAMILY PRACTICE ASSOCIATES - LAFAYETTE Doing B… 3
#> 41 1912564618 WARM SPRINGS MEDICAL CENTER Doing B… 3
#> 42 1376778001 PHOEBE SUMTER ELLAVILLE CLINIC Doing B… 3
#> 43 1891770764 PHOEBE WORTH FAMILY MEDICINE Doing B… 3
#> 44 1659700763 FLINT RIVER RURAL HEALTH CLINIC - OGLETHORPE Doing B… 3
#> 45 1053802553 PRESTON FAMILY MEDICINE Doing B… 3
#> 46 1386339141 MOUNTAIN LAKES PHYSICIANS GROUP Doing B… 3
#> 47 1841432044 OPTIM PRIMARY CARE - SYLVANIA Doing B… 3
#> 48 1033428214 OPTIM PRIMARY CARE MILLEN Doing B… 3
#> 49 1649853862 SCHPG-PRIMARY CARE Doing B… 3
#> 50 1285893818 OPTIM PRIMARY CARE-REIDSVILLE Doing B… 3
#> 51 1972762508 OPTIM PRIMARY CARE- GLENNVILLE Doing B… 3
#> 52 1285132415 OPTIM PRIMARY CARE METTER Doing B… 3
#> 53 1568713873 TAYLOR PHYSICIANS PRACTICE # 3 Doing B… 3
#> 54 1720227895 APPLING PEDIATRICS Doing B… 3
#> 55 1073752143 APPLING MEDICAL GROUP Doing B… 3
#> 56 1255570321 SOUTH GEORGIA MEDICAL ASSOCIATES Doing B… 3
#> 57 1740778828 INTERNAL MEDICINE AND GERIATRICS-RHC Doing B… 3
#> 58 1740778828 HABERSHAM MEDICAL GROUP Doing B… 3
#> 59 1528110103 MILLER COUNTY MEDICAL CENTER Doing B… 3
#> 60 1235227752 ROBERT E JENNINGS MEDICAL CLINIC Doing B… 3
#> 61 1275775314 ATRIUM HEALTH NAVICENT PRIMARY CARE FORT VALLEY Doing B… 3
#> 62 1275775314 VALLEY MEDICAL CENTER Doing B… 3
#> 63 1871816918 SOUTHWELL MEDICAL ADEL PRIMARY CARE Doing B… 3
#> 64 1871816918 COOK PRIMARY CARE Doing B… 3
#> 65 1487284956 SOUTHWELL MEDICAL SYLVESTER PRIMARY CARE Doing B… 3
#> 66 1861022329 SOUTHWELL MEDICAL CLINIC Doing B… 3
#> 67 1063042562 SOUTHWELL MEDICAL ASHBURN PRIMARY CARE Doing B… 3
#> 68 1588294045 SOUTHWELL MEDICAL NASHVILLE PRIMARY CARE Doing B… 3
#> 69 1649800111 SOUTHWELL MEDICAL COMMUNITY HEALTH CENTER Doing B… 3
#> 70 1508801416 BUCHANAN MEDICAL CLINIC Doing B… 3
#> 71 1992710610 PRIMARY CARE OF BREMEN Doing B… 3
#> 72 1982631321 WEST CARROLL FAMILY HEALTHCARE Doing B… 3
#> 73 1578682696 UNION GENERAL FAMILY MEDICINE Doing B… 3
#> 74 1427384353 CHATUGE MEDICAL GROUP Other N… 5
#> 75 1700392438 UPSON REGIONAL MEDICAL CENTER Doing B… 3
#> 76 1174024665 C/O HOSPITAL AUTHORITY OF WASHINGTON COUNTY Other N… 5rhc_nppes |>
slt(number, practiceLocations) |>
unnest_wider(practiceLocations) |>
sbt(!is.na(state)) |>
print(n = 100)#> # A tibble: 1 × 11
#> number country_code country_name address_purpose address_type address_1 city
#> <chr> <chr> <chr> <chr> <chr> <chr> <chr>
#> 1 187181… US United Stat… LOCATION DOM 308 N PA… ADEL
#> # ℹ 4 more variables: state <chr>, postal_code <chr>, telephone_number <chr>,
#> # fax_number <chr>Geocode with {tidygeocoder}
geo_census <- tidygeocoder::geocode(
rhc_enr,
street = address,
city = city,
state = state,
postalcode = zip,
method = "census",
full_results = TRUE)#> Passing 84 addresses to the US Census batch geocoder#> Query completed in: 1 secondsgeo_census#> # A tibble: 85 × 26
#> npi has_mult_npi ccn pac enid enid_state org_name dba_name inc_date
#> <chr> <int> <chr> <chr> <chr> <chr> <chr> <chr> <date>
#> 1 16397… 0 1189… 2961… O202… GA ACCESS … <NA> 2020-07-29
#> 2 11741… 0 1189… 2961… O202… GA ACCESS … <NA> 2021-01-11
#> 3 15681… 0 1189… 2961… O202… GA ACCESS … <NA> 2020-07-29
#> 4 18210… 1 1134… 1850… O200… GA BACON C… BACON C… 1995-06-15
#> 5 15588… 0 1185… 1850… O201… GA BACON C… NICHOLL… 1995-06-15
#> 6 17503… 0 1138… 2264… O200… GA BOWDON-… CARROLL… 1980-10-09
#> 7 14179… 0 1138… 2264… O200… GA BOWDON-… FRANLKI… 1980-10-09
#> 8 15381… 0 1138… 2264… O201… GA BOWDON-… CARROLL… 1980-10-09
#> 9 18519… 0 1185… 3476… O202… GA BURKE H… BURKE M… NA
#> 10 16392… 0 1134… 4587… O201… GA COFFEE … COFFEE … NA
#> # ℹ 75 more rows
#> # ℹ 17 more variables: inc_state <chr>, org_type <chr>, org_type_2 <chr>,
#> # proprietary_nonprofit <chr>, address <chr>, city <chr>, state <chr>,
#> # zip <chr>, lat <dbl>, long <dbl>, id <int>, input_address <chr>,
#> # match_indicator <chr>, match_type <chr>, matched_address <chr>,
#> # tiger_line_id <int>, tiger_side <chr>geo_arcgis <- tidygeocoder::geocode(
rhc_enr,
street = address,
city = city,
state = state,
postalcode = zip,
method = "arcgis",
full_results = TRUE)#> Passing 84 addresses to the ArcGIS single address geocoder#> Query completed in: 35.6 secondsgeo_arcgis#> # A tibble: 85 × 87
#> npi has_mult_npi ccn pac enid enid_state org_name dba_name inc_date
#> <chr> <int> <chr> <chr> <chr> <chr> <chr> <chr> <date>
#> 1 16397… 0 1189… 2961… O202… GA ACCESS … <NA> 2020-07-29
#> 2 11741… 0 1189… 2961… O202… GA ACCESS … <NA> 2021-01-11
#> 3 15681… 0 1189… 2961… O202… GA ACCESS … <NA> 2020-07-29
#> 4 18210… 1 1134… 1850… O200… GA BACON C… BACON C… 1995-06-15
#> 5 15588… 0 1185… 1850… O201… GA BACON C… NICHOLL… 1995-06-15
#> 6 17503… 0 1138… 2264… O200… GA BOWDON-… CARROLL… 1980-10-09
#> 7 14179… 0 1138… 2264… O200… GA BOWDON-… FRANLKI… 1980-10-09
#> 8 15381… 0 1138… 2264… O201… GA BOWDON-… CARROLL… 1980-10-09
#> 9 18519… 0 1185… 3476… O202… GA BURKE H… BURKE M… NA
#> 10 16392… 0 1134… 4587… O201… GA COFFEE … COFFEE … NA
#> # ℹ 75 more rows
#> # ℹ 78 more variables: inc_state <chr>, org_type <chr>, org_type_2 <chr>,
#> # proprietary_nonprofit <chr>, address <chr>, city <chr>, state <chr>,
#> # zip <chr>, lat <dbl>, long <dbl>, arcgis_address <chr>, score <dbl>,
#> # location.x <dbl>, location.y <dbl>, attributes.Loc_name <chr>,
#> # attributes.Status <chr>, attributes.Score <dbl>,
#> # attributes.Match_addr <chr>, attributes.LongLabel <chr>, …Retrieve Georgia counties shapefile from {tigris}
tig_cnt <- tigris::counties(
state = "GA",
year = 2022,
progress_bar = FALSE)
tig_cnt#> Simple feature collection with 159 features and 17 fields
#> Geometry type: MULTIPOLYGON
#> Dimension: XY
#> Bounding box: xmin: -85.6 ymin: 30.4 xmax: -80.8 ymax: 35
#> Geodetic CRS: NAD83
#> First 10 features:
#> STATEFP COUNTYFP COUNTYNS GEOID NAME NAMELSAD LSAD CLASSFP MTFCC
#> 16 13 189 00348794 13189 McDuffie McDuffie County 06 H1 G4020
#> 53 13 025 00351605 13025 Brantley Brantley County 06 H1 G4020
#> 54 13 171 00326713 13171 Lamar Lamar County 06 H1 G4020
#> 83 13 115 00353665 13115 Floyd Floyd County 06 H1 G4020
#> 101 13 273 00352238 13273 Terrell Terrell County 06 H1 G4020
#> 102 13 063 01672399 13063 Clayton Clayton County 06 H1 G4020
#> 105 13 073 00348865 13073 Columbia Columbia County 06 H1 G4020
#> 126 13 193 00346957 13193 Macon Macon County 06 H1 G4020
#> 146 13 049 00357747 13049 Charlton Charlton County 06 H1 G4020
#> 147 13 233 00343585 13233 Polk Polk County 06 H1 G4020
#> CSAFP CBSAFP METDIVFP FUNCSTAT ALAND AWATER INTPTLAT INTPTLON
#> 16 <NA> <NA> <NA> A 666591525 23112523 +33.4806126 -082.4795333
#> 53 <NA> <NA> <NA> A 1147972258 10291563 +31.1973339 -081.9829779
#> 54 <NA> <NA> <NA> A 475264405 6044329 +33.0744405 -084.1466893
#> 83 <NA> <NA> <NA> A 1320431515 22387093 +34.2636901 -085.2136849
#> 101 <NA> <NA> <NA> A 869695791 4951325 +31.7771909 -084.4394464
#> 102 <NA> <NA> <NA> A 367145192 6928044 +33.5516027 -084.4128678
#> 105 <NA> <NA> <NA> A 751598754 45345677 +33.5520746 -082.2496077
#> 126 <NA> <NA> <NA> A 1037709945 13841916 +32.3626859 -084.0512306
#> 146 <NA> <NA> <NA> A 2020415648 5963237 +30.7799036 -082.1396442
#> 147 <NA> <NA> <NA> A 803775591 4664760 +33.9960153 -085.1883384
#> geometry
#> 16 MULTIPOLYGON (((-82.6 33.6,...
#> 53 MULTIPOLYGON (((-81.9 31.1,...
#> 54 MULTIPOLYGON (((-84.2 33.1,...
#> 83 MULTIPOLYGON (((-85 34.3, -...
#> 101 MULTIPOLYGON (((-84.3 31.7,...
#> 102 MULTIPOLYGON (((-84.5 33.6,...
#> 105 MULTIPOLYGON (((-82.4 33.6,...
#> 126 MULTIPOLYGON (((-83.8 32.4,...
#> 146 MULTIPOLYGON (((-82.4 30.8,...
#> 147 MULTIPOLYGON (((-85.2 33.9,...summary(tig_cnt)#> STATEFP COUNTYFP COUNTYNS GEOID
#> Length:159 Length:159 Length:159 Length:159
#> Class :character Class :character Class :character Class :character
#> Mode :character Mode :character Mode :character Mode :character
#>
#>
#>
#> NAME NAMELSAD LSAD CLASSFP
#> Length:159 Length:159 Length:159 Length:159
#> Class :character Class :character Class :character Class :character
#> Mode :character Mode :character Mode :character Mode :character
#>
#>
#>
#> MTFCC CSAFP CBSAFP METDIVFP
#> Length:159 Length:159 Length:159 Length:159
#> Class :character Class :character Class :character Class :character
#> Mode :character Mode :character Mode :character Mode :character
#>
#>
#>
#> FUNCSTAT ALAND AWATER INTPTLAT
#> Length:159 Min. : 308985572 Min. : 488463 Length:159
#> Class :character 1st Qu.: 645741035 1st Qu.: 6553565 Class :character
#> Mode :character Median : 887636774 Median : 11818093 Mode :character
#> Mean : 940165210 Mean : 27790668
#> 3rd Qu.:1151923513 3rd Qu.: 19959364
#> Max. :2329020700 Max. :509854701
#> INTPTLON geometry
#> Length:159 MULTIPOLYGON :159
#> Class :character epsg:4269 : 0
#> Mode :character +proj=long...: 0
#>
#>
#> sf_cnt <- ms_simplify(tig_cnt)
sf_cnt$mid <- st_centroid(sf_cnt$geometry)Map with {ggplot}
ggplot(sf_cnt) +
geom_sf(
fill = "skyblue",
colour = "white",
alpha = 0.5) +
geom_sf_text(
aes(geometry = geometry,
label = NAME),
size = 3.5,
check_overlap = TRUE) +
geom_jitter(
data = geo_census,
mapping = aes(long, lat),
fill = "yellow",
color = "darkred",
alpha = 0.75,
size = 4,
shape = 21,
stroke = 1) +
theme_void()