Takes the output of the kmeans_user_item_tbl() function and applies the
k-means algorithm to it using stats::kmeans()
Arguments
- .data
The data that gets passed from
kmeans_user_item_tbl()- .centers
How many initial centers to start with
Details
Uses the stats::kmeans() function and creates a wrapper around it.
Examples
library(healthyR.data)
library(dplyr)
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()
kmeans_user_item_tbl(
.data = data_tbl
, .row_input = service_line
, .col_input = payer_grouping
, .record_input = record
) %>%
kmeans_obj()
#> K-means clustering with 5 clusters of sizes 2, 5, 1, 3, 12
#>
#> Cluster means:
#> Blue Cross Commercial Compensation Exchange Plans HMO Medicaid
#> 1 0.27188303 0.05712358 0.0003293808 0.039065198 0.18065096 0.04246134
#> 2 0.13375082 0.03542694 0.0121998471 0.016160901 0.10724914 0.05150211
#> 3 0.00000000 0.00000000 0.0000000000 0.000000000 0.27272727 0.18181818
#> 4 0.07912806 0.02702478 0.0002914681 0.009301354 0.07723873 0.21428392
#> 5 0.07837450 0.02182129 0.0043244347 0.006202137 0.04493860 0.03684344
#> Medicaid HMO Medicare A Medicare HMO No Fault Self Pay
#> 1 0.24760799 0.10958146 0.03584494 0.000000000 0.015452115
#> 2 0.13107693 0.35217108 0.11769769 0.008242686 0.034521844
#> 3 0.45454545 0.09090909 0.00000000 0.000000000 0.000000000
#> 4 0.28209782 0.23654904 0.04362913 0.002672067 0.027783628
#> 5 0.08001653 0.56250366 0.15152338 0.003475542 0.009976485
#>
#> Clustering vector:
#> [1] 4 1 5 5 5 5 2 2 5 5 1 5 4 2 5 2 5 4 2 5 5 3 5
#>
#> Within cluster sum of squares by cluster:
#> [1] 0.03549821 0.02592247 0.00000000 0.04450884 0.09625399
#> (between_SS / total_SS = 85.6 %)
#>
#> Available components:
#>
#> [1] "cluster" "centers" "totss" "withinss" "tot.withinss"
#> [6] "betweenss" "size" "iter" "ifault"