Area Health Resources Files (AHRF)
National, state, and county-level data on health care professions, health facilities, population characteristics, health workforce training, hospital utilization and expenditure, and the environment.
One table with one row per county and a second table with one row per state.
Replaced annually with the latest available county- and state-level statistics.
Compiled by the Bureau of Health Workforce at the Health Services and Resources Administration.
Please skim before you begin:
User Documentation for the County Area Health Resources File (AHRF) 2021-2022 Release
A haiku regarding this microdata:
# local aggregates
# to spread merge join spline regress
# like fresh buttered breadDownload, Import, Preparation
Download and import the most current county-level file:
library(haven)
tf <- tempfile()
ahrf_url <- "https://data.hrsa.gov//DataDownload/AHRF/AHRF_2021-2022_SAS.zip"
download.file( ahrf_url , tf , mode = 'wb' )
unzipped_files <- unzip( tf , exdir = tempdir() )
sas_fn <- grep( "\\.sas7bdat$" , unzipped_files , value = TRUE )
ahrf_tbl <- read_sas( sas_fn )
ahrf_df <- data.frame( ahrf_tbl )
names( ahrf_df ) <- tolower( names( ahrf_df ) )Save Locally
Save the object at any point:
# ahrf_fn <- file.path( path.expand( "~" ) , "AHRF" , "this_file.rds" )
# saveRDS( ahrf_df , file = ahrf_fn , compress = FALSE )Load the same object:
# ahrf_df <- readRDS( ahrf_fn )Variable Recoding
Add new columns to the data set:
ahrf_df <-
transform(
ahrf_df ,
cbsa_indicator_code =
factor(
as.numeric( f1406720 ) ,
levels = 0:2 ,
labels = c( "not metro" , "metro" , "micro" )
) ,
mhi_2020 = f1322620 ,
whole_county_hpsa_2022 = as.numeric( f0978722 ) == 1 ,
census_region =
factor(
as.numeric( f04439 ) ,
levels = 1:4 ,
labels = c( "northeast" , "midwest" , "south" , "west" )
)
)Analysis Examples with base R
Unweighted Counts
Count the unweighted number of records in the table, overall and by groups:
nrow( ahrf_df )
table( ahrf_df[ , "cbsa_indicator_code" ] , useNA = "always" )Descriptive Statistics
Calculate the mean (average) of a linear variable, overall and by groups:
mean( ahrf_df[ , "mhi_2020" ] , na.rm = TRUE )
tapply(
ahrf_df[ , "mhi_2020" ] ,
ahrf_df[ , "cbsa_indicator_code" ] ,
mean ,
na.rm = TRUE
)Calculate the distribution of a categorical variable, overall and by groups:
prop.table( table( ahrf_df[ , "census_region" ] ) )
prop.table(
table( ahrf_df[ , c( "census_region" , "cbsa_indicator_code" ) ] ) ,
margin = 2
)Calculate the sum of a linear variable, overall and by groups:
sum( ahrf_df[ , "mhi_2020" ] , na.rm = TRUE )
tapply(
ahrf_df[ , "mhi_2020" ] ,
ahrf_df[ , "cbsa_indicator_code" ] ,
sum ,
na.rm = TRUE
)Calculate the median (50th percentile) of a linear variable, overall and by groups:
quantile( ahrf_df[ , "mhi_2020" ] , 0.5 , na.rm = TRUE )
tapply(
ahrf_df[ , "mhi_2020" ] ,
ahrf_df[ , "cbsa_indicator_code" ] ,
quantile ,
0.5 ,
na.rm = TRUE
)Subsetting
Limit your data.frame to California:
sub_ahrf_df <- subset( ahrf_df , f12424 == "CA" )Calculate the mean (average) of this subset:
mean( sub_ahrf_df[ , "mhi_2020" ] , na.rm = TRUE )Measures of Uncertainty
Calculate the variance, overall and by groups:
var( ahrf_df[ , "mhi_2020" ] , na.rm = TRUE )
tapply(
ahrf_df[ , "mhi_2020" ] ,
ahrf_df[ , "cbsa_indicator_code" ] ,
var ,
na.rm = TRUE
)Regression Models and Tests of Association
Perform a t-test:
t.test( mhi_2020 ~ whole_county_hpsa_2022 , ahrf_df )Perform a chi-squared test of association:
this_table <- table( ahrf_df[ , c( "whole_county_hpsa_2022" , "census_region" ) ] )
chisq.test( this_table )Perform a generalized linear model:
glm_result <-
glm(
mhi_2020 ~ whole_county_hpsa_2022 + census_region ,
data = ahrf_df
)
summary( glm_result )Replication Example
Match the record count in row number 8,543 of AHRF 2021-2022 Technical Documentation.xlsx:
stopifnot( nrow( ahrf_df ) == 3232 )Analysis Examples with dplyr
The R dplyr library offers an alternative grammar of data manipulation to base R and SQL syntax. dplyr offers many verbs, such as summarize, group_by, and mutate, the convenience of pipe-able functions, and the tidyverse style of non-standard evaluation. This vignette details the available features. As a starting point for AHRF users, this code replicates previously-presented examples:
library(dplyr)
ahrf_tbl <- as_tibble( ahrf_df )Calculate the mean (average) of a linear variable, overall and by groups:
ahrf_tbl %>%
summarize( mean = mean( mhi_2020 , na.rm = TRUE ) )
ahrf_tbl %>%
group_by( cbsa_indicator_code ) %>%
summarize( mean = mean( mhi_2020 , na.rm = TRUE ) )Analysis Examples with data.table
The R data.table library provides a high-performance version of base R’s data.frame with syntax and feature enhancements for ease of use, convenience and programming speed. data.table offers concise syntax: fast to type, fast to read, fast speed, memory efficiency, a careful API lifecycle management, an active community, and a rich set of features. This vignette details the available features. As a starting point for AHRF users, this code replicates previously-presented examples:
library(data.table)
ahrf_dt <- data.table( ahrf_df )Calculate the mean (average) of a linear variable, overall and by groups:
ahrf_dt[ , mean( mhi_2020 , na.rm = TRUE ) ]
ahrf_dt[ , mean( mhi_2020 , na.rm = TRUE ) , by = cbsa_indicator_code ]Analysis Examples with duckdb
The R duckdb library provides an embedded analytical data management system with support for the Structured Query Language (SQL). duckdb offers a simple, feature-rich, fast, and free SQL OLAP management system. This vignette details the available features. As a starting point for AHRF users, this code replicates previously-presented examples:
library(duckdb)
con <- dbConnect( duckdb::duckdb() , dbdir = 'my-db.duckdb' )
dbWriteTable( con , 'ahrf' , ahrf_df )Calculate the mean (average) of a linear variable, overall and by groups:
dbGetQuery( con , 'SELECT AVG( mhi_2020 ) FROM ahrf' )
dbGetQuery(
con ,
'SELECT
cbsa_indicator_code ,
AVG( mhi_2020 )
FROM
ahrf
GROUP BY
cbsa_indicator_code'
)