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"
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().
See also
Other Kmeans:
hai_kmeans_automl(),
hai_kmeans_automl_predict(),
hai_kmeans_mapped_tbl(),
hai_kmeans_obj(),
hai_kmeans_scree_data_tbl(),
hai_kmeans_scree_plt(),
hai_kmeans_user_item_tbl()
Examples
library(healthyR.data)
library(dplyr)
library(broom)
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 4
#> 2 Bariatric Surgery For Obesity 3
#> 3 CHF 1
#> 4 COPD 1
#> 5 CVA 1
#> 6 Carotid Endarterectomy 1
#> 7 Cellulitis 2
#> 8 Chest Pain 2
#> 9 GI Hemorrhage 1
#> 10 Joint Replacement 1
#> # 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 2
hai_kmeans_tidy_tbl(
.kmeans_obj = km_obj,
.data = uit_tbl,
.tidy_type = "tidy"
) %>%
glimpse()
#> Rows: 5
#> Columns: 14
#> $ `Blue Cross` <dbl> 0.07837450, 0.13375082, 0.27188303, 0.07912806, 0.000…
#> $ Commercial <dbl> 0.02182129, 0.03542694, 0.05712358, 0.02702478, 0.000…
#> $ Compensation <dbl> 0.0043244347, 0.0121998471, 0.0003293808, 0.000291468…
#> $ `Exchange Plans` <dbl> 0.006202137, 0.016160901, 0.039065198, 0.009301354, 0…
#> $ HMO <dbl> 0.04493860, 0.10724914, 0.18065096, 0.07723873, 0.272…
#> $ Medicaid <dbl> 0.03684344, 0.05150211, 0.04246134, 0.21428392, 0.181…
#> $ `Medicaid HMO` <dbl> 0.08001653, 0.13107693, 0.24760799, 0.28209782, 0.454…
#> $ `Medicare A` <dbl> 0.56250366, 0.35217108, 0.10958146, 0.23654904, 0.090…
#> $ `Medicare HMO` <dbl> 0.15152338, 0.11769769, 0.03584494, 0.04362913, 0.000…
#> $ `No Fault` <dbl> 0.003475542, 0.008242686, 0.000000000, 0.002672067, 0…
#> $ `Self Pay` <dbl> 0.009976485, 0.034521844, 0.015452115, 0.027783628, 0…
#> $ size <int> 12, 5, 2, 3, 1
#> $ withinss <dbl> 0.09625399, 0.02592247, 0.03549821, 0.04450884, 0.000…
#> $ cluster <fct> 1, 2, 3, 4, 5