K-Means Object Tidy Functions

K-Means tidy functions

Usage

hai_kmeans_tidy_tbl(.kmeans_obj, .data, .tidy_type = "tidy")

kmeans_tidy_tbl(.kmeans_obj, .data, .tidy_type = "tidy")

Arguments

.kmeans_obj: A stats::kmeans() object
.data: The user item tibble created from hai_kmeans_user_item_tbl()
.tidy_type: "tidy","glance", or "augment"

Value

A tibble

Details

Takes in a k-means object and its associated user item tibble and then returns one of the items asked for. Either: broom::tidy(), broom::glance() or broom::augment(). The function defaults to broom::tidy().

Author

Steven P. Sanderson II, MPH

Examples

library(healthyR.data)
library(dplyr)
library(broom)
#> Warning: package 'broom' was built under R version 4.2.2

data_tbl <- healthyR_data %>%
  filter(ip_op_flag == "I") %>%
  filter(payer_grouping != "Medicare B") %>%
  filter(payer_grouping != "?") %>%
  select(service_line, payer_grouping) %>%
  mutate(record = 1) %>%
  as_tibble()

uit_tbl <- hai_kmeans_user_item_tbl(
  .data = data_tbl,
  .row_input = service_line,
  .col_input = payer_grouping,
  .record_input = record
)

km_obj <- hai_kmeans_obj(uit_tbl)

hai_kmeans_tidy_tbl(
  .kmeans_obj = km_obj,
  .data = uit_tbl,
  .tidy_type = "augment"
)
#> # A tibble: 23 × 2
#>    service_line                  cluster
#>    <chr>                         <fct>  
#>  1 Alcohol Abuse                 2      
#>  2 Bariatric Surgery For Obesity 3      
#>  3 CHF                           4      
#>  4 COPD                          4      
#>  5 CVA                           4      
#>  6 Carotid Endarterectomy        4      
#>  7 Cellulitis                    1      
#>  8 Chest Pain                    1      
#>  9 GI Hemorrhage                 4      
#> 10 Joint Replacement             4      
#> # ℹ 13 more rows

hai_kmeans_tidy_tbl(
  .kmeans_obj = km_obj,
  .data = uit_tbl,
  .tidy_type = "glance"
)
#> # A tibble: 1 × 4
#>   totss tot.withinss betweenss  iter
#>   <dbl>        <dbl>     <dbl> <int>
#> 1  1.41        0.202      1.21     3

hai_kmeans_tidy_tbl(
  .kmeans_obj = km_obj,
  .data = uit_tbl,
  .tidy_type = "tidy"
) %>%
  glimpse()
#> Rows: 5
#> Columns: 14
#> $ `Blue Cross`     <dbl> 0.13375082, 0.07912806, 0.27188303, 0.07837450, 0.000…
#> $ Commercial       <dbl> 0.03542694, 0.02702478, 0.05712358, 0.02182129, 0.000…
#> $ Compensation     <dbl> 0.0121998471, 0.0002914681, 0.0003293808, 0.004324434…
#> $ `Exchange Plans` <dbl> 0.016160901, 0.009301354, 0.039065198, 0.006202137, 0…
#> $ HMO              <dbl> 0.10724914, 0.07723873, 0.18065096, 0.04493860, 0.272…
#> $ Medicaid         <dbl> 0.05150211, 0.21428392, 0.04246134, 0.03684344, 0.181…
#> $ `Medicaid HMO`   <dbl> 0.13107693, 0.28209782, 0.24760799, 0.08001653, 0.454…
#> $ `Medicare A`     <dbl> 0.35217108, 0.23654904, 0.10958146, 0.56250366, 0.090…
#> $ `Medicare HMO`   <dbl> 0.11769769, 0.04362913, 0.03584494, 0.15152338, 0.000…
#> $ `No Fault`       <dbl> 0.008242686, 0.002672067, 0.000000000, 0.003475542, 0…
#> $ `Self Pay`       <dbl> 0.034521844, 0.027783628, 0.015452115, 0.009976485, 0…
#> $ size             <int> 5, 3, 2, 12, 1
#> $ withinss         <dbl> 0.02592247, 0.04450884, 0.03549821, 0.09625399, 0.000…
#> $ cluster          <fct> 1, 2, 3, 4, 5