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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.

Installation

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/ahrf@v0.0.1")

# To uninstall the package, use:
# remove.packages("ahrf")

Usage

Load the state file

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>, ...

There're 3230 rows and 6921 columns in the county file (wide format)

dim(ahrf::ahrf_county)
#> [1] 3230 6921

Variable labels are included

library(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 Estimate

County-level hospital beds in 2013

ahrf::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