This repo contains R scripts (in the data-raw folder) that download county-level and state-level Area Health Resources Files (AHRF). The datasets are stored in the data folder.
AHRF is issued annually. The most recent release is in 2016 (as of July 24, 2017).
For more information on the AHRF files, see https://datawarehouse.hrsa.gov/topics/ahrf.aspx.
You can also download the datasets as an R package. The size of ahrf_county.rda is 17.5M, so it might take a while to install and load into memory.
# install.packages("devtools")
devtools::install_github("jjchern/[email protected]")
# To uninstall the package, use:
# remove.packages("ahrf")library(tidyverse)
ahrf::ahrf_state
#> # A tibble: 52 x 1,122
#> SF00001 SF00002 `SF01201-14` `SF01202-14` `SF01203-14` `SF01204-14`
#> <chr> <chr> <chr> <chr> <chr> <chr>
#> 1 01 AL 010290 007558 002732 001996
#> 2 02 AK 001969 001039 . .
#> 3 04 AZ 015654 010877 004777 002979
#> 4 05 AR 005698 004063 001635 000828
#> 5 06 CA 101580 064218 037362 017869
#> 6 08 CO 014720 008623 006097 002623
#> 7 09 CT 013741 008745 004996 003031
#> 8 10 DE 002734 001863 000871 000516
#> 9 11 DC 007130 003946 003184 002754
#> 10 12 FL 050556 036039 014517 006760
#> # ... with 42 more rows, and 1116 more variables: `SF01205-14` <chr>,
#> # `SF01206-14` <chr>, `SF01207-14` <chr>, `SF01208-14` <chr>,
#> # `SF01209-14` <chr>, `SF01210-14` <chr>, `SF01211-14` <chr>,
#> # `SF01212-14` <chr>, `SF01220-14` <chr>, `SF01221-14` <chr>,
#> # `SF01226-14` <chr>, `SF01228-14` <chr>, `SF01229-14` <chr>,
#> # `SF01099-15` <chr>, `SF01078-14` <chr>, `SF01079-14` <chr>,
#> # `SF01088-14` <chr>, `SF01089-14` <chr>, `SF01090-14` <chr>,
#> # `SF01091-14` <chr>, `SF01092-14` <chr>, `SF01093-14` <chr>,
#> # `SF01101-14` <chr>, `SF01102-14` <chr>, `SF01095-14` <chr>,
#> # `SF01096-14` <chr>, `SF01097-14` <chr>, `SF01098-14` <chr>,
#> # `SF02201-14` <chr>, `SF02202-14` <chr>, `SF02203-14` <chr>,
#> # `SF02204-14` <chr>, `SF02205-14` <chr>, `SF02206-14` <chr>,
#> # `SF02207-14` <chr>, `SF02208-14` <chr>, `SF02209-14` <chr>,
#> # `SF02210-14` <chr>, `SF02211-14` <chr>, `SF02212-14` <chr>,
#> # `SF02221-14` <chr>, `SF02222-14` <chr>, `SF02226-14` <chr>,
#> # `SF02228-14` <chr>, `SF02229-14` <chr>, `SF02099-15` <chr>,
#> # `SF02100-15` <dbl>, `SF02078-14` <chr>, `SF02079-14` <chr>,
#> # `SF02081-14` <chr>, `SF02082-14` <chr>, `SF02083-14` <chr>,
#> # `SF02084-14` <chr>, `SF02085-14` <chr>, `SF02086-14` <chr>,
#> # `SF02087-14` <chr>, `SF02088-14` <chr>, `SF02089-14` <chr>,
#> # `SF02090-14` <chr>, `SF02091-14` <chr>, `SF02092-14` <chr>,
#> # `SF02093-14` <chr>, `SF02101-14` <chr>, `SF02102-14` <chr>,
#> # `SF02095-14` <chr>, `SF02096-14` <chr>, `SF02097-14` <chr>,
#> # `SF02098-14` <chr>, `SF03201-14` <chr>, `SF03202-14` <chr>,
#> # `SF03203-14` <chr>, `SF03204-14` <chr>, `SF03205-14` <chr>,
#> # `SF03206-14` <chr>, `SF03207-14` <chr>, `SF03208-14` <chr>,
#> # `SF03209-14` <chr>, `SF03210-14` <chr>, `SF03211-14` <chr>,
#> # `SF03212-14` <chr>, `SF03217-14` <chr>, `SF03219-14` <chr>,
#> # `SF03220-14` <chr>, `SF03221-14` <chr>, `SF03226-14` <chr>,
#> # `SF03227-14` <chr>, `SF03228-14` <chr>, `SF03229-14` <chr>,
#> # `SF03230-14` <chr>, `SF03237-14` <chr>, `SF03238-14` <chr>,
#> # `SF03099-15` <chr>, `SF03100-15` <dbl>, `SF03078-14` <chr>,
#> # `SF03079-14` <chr>, `SF03080-14` <chr>, `SF03081-14` <chr>,
#> # `SF03082-14` <chr>, `SF03083-14` <chr>, `SF03084-14` <chr>, ...dim(ahrf::ahrf_county)
#> [1] 3230 6921library(labelled)
ahrf::ahrf_county %>%
select(F04437, F00002, contains("F08921"), contains("F11984")) %>%
var_label() %>%
enframe() %>%
unnest()
#> # A tibble: 16 x 2
#> name value
#> <chr> <chr>
#> 1 F04437 County Name w/State Abbrev
#> 2 F00002 Header - FIPS St & Cty Code
#> 3 F08921-13 Hospital Beds
#> 4 F08921-10 Hospital Beds
#> 5 F08921-05 Hospital Beds
#> 6 F11984-15 Population Estimate
#> 7 F11984-14 Population Estimate
#> 8 F11984-13 Population Estimate
#> 9 F11984-12 Population Estimate
#> 10 F11984-11 Population Estimate
#> 11 F11984-09 Population Estimate
#> 12 F11984-08 Population Estimate
#> 13 F11984-07 Population Estimate
#> 14 F11984-06 Population Estimate
#> 15 F11984-05 Population Estimate
#> 16 F11984-95 Population Estimateahrf::ahrf_county %>%
select(county = F04437,
fips = F00002,
beds_2013 = `F08921-13`,
pop_2013 = `F11984-13`) %>%
mutate(beds_2013 = as.integer(beds_2013),
pop_2013 = as.integer(pop_2013),
beds_2013_p10k = beds_2013 / pop_2013 * 10000) -> beds
beds
#> # A tibble: 3,230 x 5
#> county fips beds_2013 pop_2013 beds_2013_p10k
#> <chr> <chr> <int> <int> <dbl>
#> 1 Autauga, AL 01001 50 55246 9.050429
#> 2 Baldwin, AL 01003 364 195540 18.615117
#> 3 Barbour, AL 01005 47 27076 17.358546
#> 4 Bibb, AL 01007 20 22512 8.884151
#> 5 Blount, AL 01009 40 57872 6.911805
#> 6 Bullock, AL 01011 54 10639 50.756650
#> 7 Butler, AL 01013 83 20265 40.957316
#> 8 Calhoun, AL 01015 458 116736 39.233827
#> 9 Chambers, AL 01017 188 34162 55.031907
#> 10 Cherokee, AL 01019 45 26203 17.173606
#> # ... with 3,220 more rows
lapply(beds, summary)
#> $county
#> Length Class Mode
#> 3230 character character
#>
#> $fips
#> Length Class Mode
#> 3230 character character
#>
#> $beds_2013
#> Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
#> 0.0 17.0 50.0 294.6 181.5 25311.0 7
#>
#> $pop_2013
#> Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
#> 90 11236 26134 99287 66665 10017068 8
#>
#> $beds_2013_p10k
#> Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
#> 0.000 7.391 20.128 30.673 37.272 781.810 13
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