The goal of the { healthyR.data } package is to provide a simple yet feature rich administrative data-set allowing for the testing of functions inside of the { healthyR } package. It can be used to test its functions or any function you create.
Installation
You can install the released version of healthyR.data from CRAN with:
install.packages("healthyR.data")And the development version from GitHub with:
# install.packages("devtools")
devtools::install_github("spsanderson/healthyR.data")Example
This is a basic example which shows you how to solve a common problem:
library(healthyR.data)
library(dplyr)
df <- healthyR_data
glimpse(df)
#> Rows: 187,721
#> Columns: 17
#> $ mrn <chr> "86069614", "60856527", "80673110", "55897373…
#> $ visit_id <chr> "3519249247", "3602225015", "3125290892", "38…
#> $ visit_start_date_time <dttm> 2010-01-04 05:00:00, 2010-01-04 05:00:00, 20…
#> $ visit_end_date_time <dttm> 2010-01-04, 2010-01-04, 2010-01-04, 2010-01-…
#> $ total_charge_amount <dbl> 25983.88, 22774.05, 10690.45, 8788.02, 7325.1…
#> $ total_amount_due <dbl> 0.00, 0.00, 0.00, 0.00, 0.00, 201.52, 20.00, …
#> $ total_adjustment_amount <dbl> -20799.61, -12978.37, -7596.09, -7663.57, -60…
#> $ payer_grouping <chr> "Medicare B", "Medicare HMO", "HMO", "Medicar…
#> $ total_payment_amount <dbl> -5184.27, -9795.68, -3094.36, -1124.45, -1269…
#> $ ip_op_flag <chr> "O", "O", "O", "O", "O", "O", "O", "O", "O", …
#> $ service_line <chr> "General Outpatient", "General Outpatient", "…
#> $ length_of_stay <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
#> $ expected_length_of_stay <lgl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
#> $ length_of_stay_threshold <lgl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
#> $ los_outlier_flag <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
#> $ readmit_flag <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
#> $ readmit_expectation <lgl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
df %>%
count(ip_op_flag, service_line) %>%
arrange(ip_op_flag, desc(n)) %>%
rename(count = n)
#> # A tibble: 30 × 3
#> ip_op_flag service_line count
#> <chr> <chr> <int>
#> 1 I Medical 64435
#> 2 I Surgical 14916
#> 3 I COPD 4398
#> 4 I CHF 3871
#> 5 I Pneumonia 3323
#> 6 I Cellulitis 3311
#> 7 I Major Depression/Bipolar Affective Disorders 2866
#> 8 I Chest Pain 2766
#> 9 I GI Hemorrhage 2404
#> 10 I MI 2253
#> # ℹ 20 more rows