Takes in a data.frame/tibble and transforms it into an aggregated/normalized user-item tibble of proportions. The user will need to input the parameters for the rows/user and the columns/items.
Usage
hai_kmeans_user_item_tbl(.data, .row_input, .col_input, .record_input)
kmeans_user_item_tbl(.data, .row_input, .col_input, .record_input)Arguments
- .data
The data that you want to transform
- .row_input
The column that is going to be the row (user)
- .col_input
The column that is going to be the column (item)
- .record_input
The column that is going to be summed up for the aggregation and normalization process.
Details
This function should be used before using a k-mean model. This is commonly referred to as a user-item matrix because "users" tend to be on the rows and "items" (e.g. orders) on the columns. You must supply a column that can be summed for the aggregation and normalization process to occur.
See also
Other Kmeans:
hai_kmeans_automl_predict(),
hai_kmeans_automl(),
hai_kmeans_mapped_tbl(),
hai_kmeans_obj(),
hai_kmeans_scree_data_tbl(),
hai_kmeans_scree_plt(),
hai_kmeans_tidy_tbl()
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()
hai_kmeans_user_item_tbl(
.data = data_tbl,
.row_input = service_line,
.col_input = payer_grouping,
.record_input = record
)
#> # A tibble: 23 × 12
#> service_line `Blue Cross` Commercial Compensation `Exchange Plans` HMO
#> <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 Alcohol Abuse 0.0941 0.0321 0.000525 0.0116 0.0788
#> 2 Bariatric Surge… 0.317 0.0583 0 0.0518 0.168
#> 3 CHF 0.0295 0.00958 0.000518 0.00414 0.0205
#> 4 COPD 0.0493 0.0228 0.000228 0.00548 0.0342
#> 5 CVA 0.0647 0.0246 0.00107 0.0107 0.0524
#> 6 Carotid Endarte… 0.0845 0.0282 0 0 0.0141
#> 7 Cellulitis 0.110 0.0339 0.0118 0.00847 0.0805
#> 8 Chest Pain 0.144 0.0391 0.00290 0.00543 0.112
#> 9 GI Hemorrhage 0.0542 0.0175 0.00125 0.00834 0.0480
#> 10 Joint Replaceme… 0.139 0.0179 0.0336 0.00673 0.0516
#> # ℹ 13 more rows
#> # ℹ 6 more variables: Medicaid <dbl>, `Medicaid HMO` <dbl>, `Medicare A` <dbl>,
#> # `Medicare HMO` <dbl>, `No Fault` <dbl>, `Self Pay` <dbl>