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,] 0.28528859 2.2491667
#> [2,] -0.13117764 2.1084384
#> [3,] -0.24542640 -2.2402340
#> [4,] 0.80739882 -0.5729858
#> [5,] 0.44699981 -1.3484811
#> [6,] -0.61160252 -2.1092884
#> [7,] -0.80734990 1.2504653
#> [8,] -0.50440572 0.8303128
#> [9,] 0.24279811 -1.9372908
#> [10,] -0.17718671 -0.5774085
#> [11,] -0.23473046 1.4898180
#> [12,] -0.24119590 -1.2576221
#> [13,] 0.12464406 1.7042488
#> [14,] -0.20695231 0.9352088
#> [15,] 0.38569762 -0.2300152
#> [16,] -0.31607697 0.3515507
#> [17,] 0.03603007 -1.5945875
#> [18,] 0.69957330 2.1887216
#> [19,] 0.13563564 0.6237887
#> [20,] 0.50685622 -0.9394032
#> [21,] 0.05819347 -0.9300539
#> [22,] 0.47927787 1.7866853
#> [23,] -0.73228906 -1.7810346
#> attr(,"scaled:center")
#> [1] -4.707077 -2.644019
#>
#> $umap_results_tbl
#> # A tibble: 23 × 3
#> x y service_line
#> <dbl> <dbl> <chr>
#> 1 0.285 2.25 Alcohol Abuse
#> 2 -0.131 2.11 Bariatric Surgery For Obesity
#> 3 -0.245 -2.24 CHF
#> 4 0.807 -0.573 COPD
#> 5 0.447 -1.35 CVA
#> 6 -0.612 -2.11 Carotid Endarterectomy
#> 7 -0.807 1.25 Cellulitis
#> 8 -0.504 0.830 Chest Pain
#> 9 0.243 -1.94 GI Hemorrhage
#> 10 -0.177 -0.577 Joint Replacement
#> # ℹ 13 more rows
#>
#> $kmeans_obj
#> K-means clustering with 3 clusters of sizes 5, 6, 12
#>
#> Cluster means:
#> Blue Cross Commercial Compensation Exchange Plans HMO Medicaid
#> 1 0.1495475 0.03679700 0.0003066332 0.020729565 0.16252855 0.13072521
#> 2 0.1170278 0.03141187 0.0101665392 0.013865190 0.09822472 0.08557952
#> 3 0.0783745 0.02182129 0.0043244347 0.006202137 0.04493860 0.03684344
#> Medicaid HMO Medicare A Medicare HMO No Fault Self Pay
#> 1 0.31446157 0.1318675 0.03192357 0.001364577 0.019748398
#> 2 0.14652195 0.3535395 0.10524131 0.007067791 0.031353724
#> 3 0.08001653 0.5625037 0.15152338 0.003475542 0.009976485
#>
#> Clustering vector:
#> [1] 1 1 3 3 3 3 2 2 3 3 1 3 1 2 3 2 3 2 2 3 3 1 3
#>
#> Within cluster sum of squares by cluster:
#> [1] 0.19152559 0.08456928 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 1
#> 2 Bariatric Surgery For Obesity 1
#> 3 CHF 3
#> 4 COPD 3
#> 5 CVA 3
#> 6 Carotid Endarterectomy 3
#> 7 Cellulitis 2
#> 8 Chest Pain 2
#> 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 0.285 2.25 Alcohol Abuse 1
#> 2 -0.131 2.11 Bariatric Surgery For Obesity 1
#> 3 -0.245 -2.24 CHF 3
#> 4 0.807 -0.573 COPD 3
#> 5 0.447 -1.35 CVA 3
#> 6 -0.612 -2.11 Carotid Endarterectomy 3
#> 7 -0.807 1.25 Cellulitis 2
#> 8 -0.504 0.830 Chest Pain 2
#> 9 0.243 -1.94 GI Hemorrhage 3
#> 10 -0.177 -0.577 Joint Replacement 3
#> # ℹ 13 more rows
#>