Create a umap object from the uwot::umap() function.
Usage
hai_umap_list(.data, .kmeans_map_tbl, .k_cluster = 5)
umap_list(.data, .kmeans_map_tbl, .k_cluster = 5)Arguments
- .data
The data from the
hai_kmeans_user_item_tbl()function.- .kmeans_map_tbl
The data from the
hai_kmeans_mapped_tbl().- .k_cluster
Pick the desired amount of clusters from your analysis of the scree plot.
Details
This takes in the user item table/matix that is produced by
hai_kmeans_user_item_tbl() function. This function uses the defaults of
uwot::umap().
See also
https://github.com/jlmelville/uwot (GitHub)
https://github.com/jlmelville/uwot (arXiv paper)
Other UMAP:
hai_umap_plot()
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
)
kmm_tbl <- hai_kmeans_mapped_tbl(uit_tbl)
umap_list(.data = uit_tbl, kmm_tbl, 3)
#> New names:
#> • `` -> `...1`
#> • `` -> `...2`
#> Joining with `by = join_by(service_line)`
#> $umap_obj
#> [,1] [,2]
#> [1,] -1.1981357 2.0046529
#> [2,] -1.1084554 1.4954147
#> [3,] 1.1993915 -2.0136724
#> [4,] 0.6678632 -0.2270990
#> [5,] 1.1996123 -0.8039470
#> [6,] 0.7658432 -1.9398838
#> [7,] -0.1887519 1.0237469
#> [8,] -0.5976817 0.9337668
#> [9,] 0.4483375 -1.8110748
#> [10,] -0.1902566 -0.8540483
#> [11,] -0.8490075 1.2578320
#> [12,] 1.2085169 -1.3726707
#> [13,] -0.9003626 1.7225141
#> [14,] -0.9173445 0.6489176
#> [15,] 0.1387130 -0.2898183
#> [16,] -0.1390712 0.5165686
#> [17,] 0.8088696 -1.0972915
#> [18,] -0.3292845 1.6555008
#> [19,] -0.4978821 0.4275813
#> [20,] 0.2071733 -1.1744819
#> [21,] 0.3182040 -0.6837402
#> [22,] -0.7576456 2.1124949
#> [23,] 0.7113548 -1.5312629
#> attr(,"scaled:center")
#> [1] 0.1631531 -9.4144959
#>
#> $umap_results_tbl
#> # A tibble: 23 × 3
#> x y service_line
#> <dbl> <dbl> <chr>
#> 1 -1.20 2.00 Alcohol Abuse
#> 2 -1.11 1.50 Bariatric Surgery For Obesity
#> 3 1.20 -2.01 CHF
#> 4 0.668 -0.227 COPD
#> 5 1.20 -0.804 CVA
#> 6 0.766 -1.94 Carotid Endarterectomy
#> 7 -0.189 1.02 Cellulitis
#> 8 -0.598 0.934 Chest Pain
#> 9 0.448 -1.81 GI Hemorrhage
#> 10 -0.190 -0.854 Joint Replacement
#> # ℹ 13 more rows
#>
#> $kmeans_obj
#> K-means clustering with 3 clusters of sizes 6, 5, 12
#>
#> Cluster means:
#> Blue Cross Commercial Compensation Exchange Plans HMO Medicaid
#> 1 0.1170278 0.03141187 0.0101665392 0.013865190 0.09822472 0.08557952
#> 2 0.1495475 0.03679700 0.0003066332 0.020729565 0.16252855 0.13072521
#> 3 0.0783745 0.02182129 0.0043244347 0.006202137 0.04493860 0.03684344
#> Medicaid HMO Medicare A Medicare HMO No Fault Self Pay
#> 1 0.14652195 0.3535395 0.10524131 0.007067791 0.031353724
#> 2 0.31446157 0.1318675 0.03192357 0.001364577 0.019748398
#> 3 0.08001653 0.5625037 0.15152338 0.003475542 0.009976485
#>
#> Clustering vector:
#> [1] 2 2 3 3 3 3 1 1 3 3 2 3 2 1 3 1 3 1 1 3 3 2 3
#>
#> Within cluster sum of squares by cluster:
#> [1] 0.08456928 0.19152559 0.09625399
#> (between_SS / total_SS = 73.6 %)
#>
#> Available components:
#>
#> [1] "cluster" "centers" "totss" "withinss" "tot.withinss"
#> [6] "betweenss" "size" "iter" "ifault"
#>
#> $kmeans_cluster_tbl
#> # A tibble: 23 × 2
#> service_line .cluster
#> <chr> <fct>
#> 1 Alcohol Abuse 2
#> 2 Bariatric Surgery For Obesity 2
#> 3 CHF 3
#> 4 COPD 3
#> 5 CVA 3
#> 6 Carotid Endarterectomy 3
#> 7 Cellulitis 1
#> 8 Chest Pain 1
#> 9 GI Hemorrhage 3
#> 10 Joint Replacement 3
#> # ℹ 13 more rows
#>
#> $umap_kmeans_cluster_results_tbl
#> # A tibble: 23 × 4
#> x y service_line .cluster
#> <dbl> <dbl> <chr> <fct>
#> 1 -1.20 2.00 Alcohol Abuse 2
#> 2 -1.11 1.50 Bariatric Surgery For Obesity 2
#> 3 1.20 -2.01 CHF 3
#> 4 0.668 -0.227 COPD 3
#> 5 1.20 -0.804 CVA 3
#> 6 0.766 -1.94 Carotid Endarterectomy 3
#> 7 -0.189 1.02 Cellulitis 1
#> 8 -0.598 0.934 Chest Pain 1
#> 9 0.448 -1.81 GI Hemorrhage 3
#> 10 -0.190 -0.854 Joint Replacement 3
#> # ℹ 13 more rows
#>