diff --git a/docs/404.html b/docs/404.html index d1077e5..10a28b4 100644 --- a/docs/404.html +++ b/docs/404.html @@ -24,7 +24,7 @@ healthyR - 0.2.1 + 0.2.1.9000 diff --git a/docs/CODE_OF_CONDUCT.html b/docs/CODE_OF_CONDUCT.html index d15f31e..2fabb9a 100644 --- a/docs/CODE_OF_CONDUCT.html +++ b/docs/CODE_OF_CONDUCT.html @@ -10,7 +10,7 @@ healthyR - 0.2.1 + 0.2.1.9000 diff --git a/docs/LICENSE-text.html b/docs/LICENSE-text.html index b490b29..4fa6a6d 100644 --- a/docs/LICENSE-text.html +++ b/docs/LICENSE-text.html @@ -10,7 +10,7 @@ healthyR - 0.2.1 + 0.2.1.9000 diff --git a/docs/LICENSE.html b/docs/LICENSE.html index b5138f5..d56e006 100644 --- a/docs/LICENSE.html +++ b/docs/LICENSE.html @@ -10,7 +10,7 @@ healthyR - 0.2.1 + 0.2.1.9000 diff --git a/docs/articles/getting-started.html b/docs/articles/getting-started.html index 8637645..d810071 100644 --- a/docs/articles/getting-started.html +++ b/docs/articles/getting-started.html @@ -26,7 +26,7 @@ healthyR - 0.2.1 + 0.2.1.9000 @@ -83,7 +83,7 @@ A Quick Introduction Steven P. Sanderson II, MPH - 2023-03-31 + 2023-04-18 Source: vignettes/getting-started.Rmd getting-started.Rmd @@ -156,8 +156,8 @@ Plot the Time Series= "month" , .interactive = TRUE ) - -As we can see, this function has the ability to return either a + +As we can see, this function has the ability to return either a static plot or and interactive plot. Under the hood it is using the timetk::plot_time_series function. You can find out more on the the timetk function here. diff --git a/docs/articles/index.html b/docs/articles/index.html index 55a995e..51a05a2 100644 --- a/docs/articles/index.html +++ b/docs/articles/index.html @@ -10,7 +10,7 @@ healthyR - 0.2.1 + 0.2.1.9000 diff --git a/docs/authors.html b/docs/authors.html index c3deaad..7c2d143 100644 --- a/docs/authors.html +++ b/docs/authors.html @@ -10,7 +10,7 @@ healthyR - 0.2.1 + 0.2.1.9000 @@ -74,13 +74,13 @@ Citation Sanderson S (2023). healthyR: Hospital Data Analysis Workflow Tools. -R package version 0.2.1, https://github.com/spsanderson/healthyR. +R package version 0.2.1.9000, https://github.com/spsanderson/healthyR. @Manual{, title = {healthyR: Hospital Data Analysis Workflow Tools}, author = {Steven Sanderson}, year = {2023}, - note = {R package version 0.2.1}, + note = {R package version 0.2.1.9000}, url = {https://github.com/spsanderson/healthyR}, } diff --git a/docs/index.html b/docs/index.html index 20c8369..7caaf3e 100644 --- a/docs/index.html +++ b/docs/index.html @@ -36,7 +36,7 @@ healthyR - 0.2.1 + 0.2.1.9000 diff --git a/docs/news/index.html b/docs/news/index.html index dc4d5ec..ac4ac8c 100644 --- a/docs/news/index.html +++ b/docs/news/index.html @@ -10,7 +10,7 @@ healthyR - 0.2.1 + 0.2.1.9000 @@ -57,7 +57,10 @@ -healthyR 0.2.1 +healthyR (development version) + + +healthyR 0.2.1CRAN release: 2023-04-06 Breaking Changes Fix #141 - Drop support for kmeans functions and umap functions as they were moved to healthyR.ai diff --git a/docs/pkgdown.yml b/docs/pkgdown.yml index 1f075c2..0c7f47f 100644 --- a/docs/pkgdown.yml +++ b/docs/pkgdown.yml @@ -3,7 +3,7 @@ pkgdown: 2.0.7 pkgdown_sha: ~ articles: getting-started: getting-started.html -last_built: 2023-03-31T13:51Z +last_built: 2023-04-18T18:29Z urls: reference: https://www.spsanderson.com/healthyR/reference article: https://www.spsanderson.com/healthyR/articles diff --git a/docs/reference/Rplot002.png b/docs/reference/Rplot002.png index ba13aa5..67932d6 100644 Binary files a/docs/reference/Rplot002.png and b/docs/reference/Rplot002.png differ diff --git a/docs/reference/category_counts_tbl.html b/docs/reference/category_counts_tbl.html index 5c2db75..a1bbdb5 100644 --- a/docs/reference/category_counts_tbl.html +++ b/docs/reference/category_counts_tbl.html @@ -12,7 +12,7 @@ healthyR - 0.2.1 + 0.2.1.9000 diff --git a/docs/reference/color_blind.html b/docs/reference/color_blind.html index a07e1c1..760b38b 100644 --- a/docs/reference/color_blind.html +++ b/docs/reference/color_blind.html @@ -10,7 +10,7 @@ healthyR - 0.2.1 + 0.2.1.9000 diff --git a/docs/reference/diverging_bar_plt.html b/docs/reference/diverging_bar_plt.html index 8cbf69e..46550ec 100644 --- a/docs/reference/diverging_bar_plt.html +++ b/docs/reference/diverging_bar_plt.html @@ -38,7 +38,7 @@ healthyR - 0.2.1 + 0.2.1.9000 @@ -170,7 +170,6 @@ Author< Examples suppressPackageStartupMessages(library(ggplot2)) -#> Warning: package 'ggplot2' was built under R version 4.2.2 data("mtcars") mtcars$car_name <- rownames(mtcars) diff --git a/docs/reference/diverging_lollipop_plt.html b/docs/reference/diverging_lollipop_plt.html index a0d3687..f85eece 100644 --- a/docs/reference/diverging_lollipop_plt.html +++ b/docs/reference/diverging_lollipop_plt.html @@ -18,7 +18,7 @@ healthyR - 0.2.1 + 0.2.1.9000 diff --git a/docs/reference/dx_cc_mapping.html b/docs/reference/dx_cc_mapping.html index 2c720d1..c4e3743 100644 --- a/docs/reference/dx_cc_mapping.html +++ b/docs/reference/dx_cc_mapping.html @@ -12,7 +12,7 @@ healthyR - 0.2.1 + 0.2.1.9000 diff --git a/docs/reference/gartner_magic_chart_plt-1.png b/docs/reference/gartner_magic_chart_plt-1.png index c20d643..e954887 100644 Binary files a/docs/reference/gartner_magic_chart_plt-1.png and b/docs/reference/gartner_magic_chart_plt-1.png differ diff --git a/docs/reference/gartner_magic_chart_plt-2.png b/docs/reference/gartner_magic_chart_plt-2.png index 3fb533c..8062520 100644 Binary files a/docs/reference/gartner_magic_chart_plt-2.png and b/docs/reference/gartner_magic_chart_plt-2.png differ diff --git a/docs/reference/gartner_magic_chart_plt.html b/docs/reference/gartner_magic_chart_plt.html index 1c5e962..537031e 100644 --- a/docs/reference/gartner_magic_chart_plt.html +++ b/docs/reference/gartner_magic_chart_plt.html @@ -10,7 +10,7 @@ healthyR - 0.2.1 + 0.2.1.9000 diff --git a/docs/reference/hr_scale_color_colorblind.html b/docs/reference/hr_scale_color_colorblind.html index 832cc4b..267e666 100644 --- a/docs/reference/hr_scale_color_colorblind.html +++ b/docs/reference/hr_scale_color_colorblind.html @@ -10,7 +10,7 @@ healthyR - 0.2.1 + 0.2.1.9000 diff --git a/docs/reference/hr_scale_fill_colorblind.html b/docs/reference/hr_scale_fill_colorblind.html index 7fee9d3..f5aafda 100644 --- a/docs/reference/hr_scale_fill_colorblind.html +++ b/docs/reference/hr_scale_fill_colorblind.html @@ -10,7 +10,7 @@ healthyR - 0.2.1 + 0.2.1.9000 diff --git a/docs/reference/index.html b/docs/reference/index.html index d6b31c6..b05cff2 100644 --- a/docs/reference/index.html +++ b/docs/reference/index.html @@ -10,7 +10,7 @@ healthyR - 0.2.1 + 0.2.1.9000 diff --git a/docs/reference/los_ra_index_plt-1.png b/docs/reference/los_ra_index_plt-1.png index 2125bbf..3aa1dc0 100644 Binary files a/docs/reference/los_ra_index_plt-1.png and b/docs/reference/los_ra_index_plt-1.png differ diff --git a/docs/reference/los_ra_index_plt-2.png b/docs/reference/los_ra_index_plt-2.png index dc8d78c..3f5c124 100644 Binary files a/docs/reference/los_ra_index_plt-2.png and b/docs/reference/los_ra_index_plt-2.png differ diff --git a/docs/reference/los_ra_index_plt.html b/docs/reference/los_ra_index_plt.html index 7438ef8..e80f060 100644 --- a/docs/reference/los_ra_index_plt.html +++ b/docs/reference/los_ra_index_plt.html @@ -12,7 +12,7 @@ healthyR - 0.2.1 + 0.2.1.9000 diff --git a/docs/reference/los_ra_index_summary_tbl.html b/docs/reference/los_ra_index_summary_tbl.html index e6689f8..04681b8 100644 --- a/docs/reference/los_ra_index_summary_tbl.html +++ b/docs/reference/los_ra_index_summary_tbl.html @@ -10,7 +10,7 @@ healthyR - 0.2.1 + 0.2.1.9000 @@ -156,21 +156,21 @@ Examples#> # A tibble: 15 × 4 #> los_group los_index rar_index los_ra_var #> <dbl> <dbl> <dbl> <dbl> -#> 1 1 0.0957 1.08 0.981 -#> 2 2 0.247 2 1.75 -#> 3 3 0.357 1.09 0.734 -#> 4 4 0.456 1.88 1.42 -#> 5 5 0.431 1.56 1.12 -#> 6 6 0.533 1.11 0.578 -#> 7 7 0.620 1.07 0.451 -#> 8 8 0.906 0.75 0.344 -#> 9 9 0.834 1.8 0.966 -#> 10 10 1.23 1.09 0.325 -#> 11 11 1.18 1.4 0.581 -#> 12 12 1.13 1.1 0.233 -#> 13 13 1.31 1.6 0.908 -#> 14 14 1.73 1.5 1.23 -#> 15 15 1.86 1.22 1.08 +#> 1 1 0.0814 1.25 1.17 +#> 2 2 0.191 0.9 0.909 +#> 3 3 0.286 1.22 0.936 +#> 4 4 0.369 1.88 1.51 +#> 5 5 0.611 1.62 1.01 +#> 6 6 0.815 1.09 0.276 +#> 7 7 0.747 1.25 0.503 +#> 8 8 0.768 1.2 0.432 +#> 9 9 1.23 1.44 0.676 +#> 10 10 1.07 1.15 0.226 +#> 11 11 1.47 1.75 1.22 +#> 12 12 1.27 1.22 0.490 +#> 13 13 1.35 0.909 0.445 +#> 14 14 2.21 0.933 1.28 +#> 15 15 1.99 1.3 1.29 los_ra_index_summary_tbl( .data = data_tbl @@ -183,16 +183,16 @@ Examples#> # A tibble: 10 × 4 #> los_group los_index rar_index los_ra_var #> <dbl> <dbl> <dbl> <dbl> -#> 1 1 0.0957 1.08 0.981 -#> 2 2 0.247 2 1.75 -#> 3 3 0.357 1.09 0.734 -#> 4 4 0.456 1.88 1.42 -#> 5 5 0.431 1.56 1.12 -#> 6 6 0.533 1.11 0.578 -#> 7 7 0.620 1.07 0.451 -#> 8 8 0.906 0.75 0.344 -#> 9 9 0.834 1.8 0.966 -#> 10 10 1.56 1.2 0.763 +#> 1 1 0.0814 1.25 1.17 +#> 2 2 0.191 0.9 0.909 +#> 3 3 0.286 1.22 0.936 +#> 4 4 0.369 1.88 1.51 +#> 5 5 0.611 1.62 1.01 +#> 6 6 0.815 1.09 0.276 +#> 7 7 0.747 1.25 0.503 +#> 8 8 0.768 1.2 0.432 +#> 9 9 1.23 1.44 0.676 +#> 10 10 1.71 1.3 1.01 diff --git a/docs/reference/named_item_list.html b/docs/reference/named_item_list.html index 0e1ce55..20236f6 100644 --- a/docs/reference/named_item_list.html +++ b/docs/reference/named_item_list.html @@ -14,7 +14,7 @@ healthyR - 0.2.1 + 0.2.1.9000 diff --git a/docs/reference/opt_bin.html b/docs/reference/opt_bin.html index f555d53..ea0f4e7 100644 --- a/docs/reference/opt_bin.html +++ b/docs/reference/opt_bin.html @@ -12,7 +12,7 @@ healthyR - 0.2.1 + 0.2.1.9000 @@ -114,20 +114,17 @@ Examples .value_col = value , .iters = 100 ) -#> # A tibble: 11 × 1 -#> value -#> <dbl> -#> 1 -3.30 -#> 2 -2.57 -#> 3 -1.85 -#> 4 -1.13 -#> 5 -0.403 -#> 6 0.320 -#> 7 1.04 -#> 8 1.77 -#> 9 2.49 -#> 10 3.21 -#> 11 3.94 +#> # A tibble: 8 × 1 +#> value +#> <dbl> +#> 1 -3.12 +#> 2 -2.24 +#> 3 -1.35 +#> 4 -0.472 +#> 5 0.410 +#> 6 1.29 +#> 7 2.17 +#> 8 3.06 diff --git a/docs/reference/pipe.html b/docs/reference/pipe.html index 8231e33..fec520a 100644 --- a/docs/reference/pipe.html +++ b/docs/reference/pipe.html @@ -10,7 +10,7 @@ healthyR - 0.2.1 + 0.2.1.9000 diff --git a/docs/reference/px_cc_mapping.html b/docs/reference/px_cc_mapping.html index 282ef59..71e78b4 100644 --- a/docs/reference/px_cc_mapping.html +++ b/docs/reference/px_cc_mapping.html @@ -12,7 +12,7 @@ healthyR - 0.2.1 + 0.2.1.9000 diff --git a/docs/reference/save_to_excel.html b/docs/reference/save_to_excel.html index dae6fb4..77e76c6 100644 --- a/docs/reference/save_to_excel.html +++ b/docs/reference/save_to_excel.html @@ -14,7 +14,7 @@ healthyR - 0.2.1 + 0.2.1.9000 diff --git a/docs/reference/service_line_augment.html b/docs/reference/service_line_augment.html index 767bb97..d0ce495 100644 --- a/docs/reference/service_line_augment.html +++ b/docs/reference/service_line_augment.html @@ -12,7 +12,7 @@ healthyR - 0.2.1 + 0.2.1.9000 diff --git a/docs/reference/service_line_vec.html b/docs/reference/service_line_vec.html index b3ebdb7..66aa0f5 100644 --- a/docs/reference/service_line_vec.html +++ b/docs/reference/service_line_vec.html @@ -12,7 +12,7 @@ healthyR - 0.2.1 + 0.2.1.9000 diff --git a/docs/reference/sql_left.html b/docs/reference/sql_left.html index 19c0748..ca98c76 100644 --- a/docs/reference/sql_left.html +++ b/docs/reference/sql_left.html @@ -10,7 +10,7 @@ healthyR - 0.2.1 + 0.2.1.9000 diff --git a/docs/reference/sql_mid.html b/docs/reference/sql_mid.html index d257a37..79855fb 100644 --- a/docs/reference/sql_mid.html +++ b/docs/reference/sql_mid.html @@ -10,7 +10,7 @@ healthyR - 0.2.1 + 0.2.1.9000 diff --git a/docs/reference/sql_right.html b/docs/reference/sql_right.html index ed2aff7..ad5d7de 100644 --- a/docs/reference/sql_right.html +++ b/docs/reference/sql_right.html @@ -10,7 +10,7 @@ healthyR - 0.2.1 + 0.2.1.9000 diff --git a/docs/reference/tidyeval.html b/docs/reference/tidyeval.html index 059eb92..d778cc1 100644 --- a/docs/reference/tidyeval.html +++ b/docs/reference/tidyeval.html @@ -72,7 +72,7 @@ healthyR - 0.2.1 + 0.2.1.9000 diff --git a/docs/reference/top_n_tbl.html b/docs/reference/top_n_tbl.html index 036fb5f..0261aa5 100644 --- a/docs/reference/top_n_tbl.html +++ b/docs/reference/top_n_tbl.html @@ -12,7 +12,7 @@ healthyR - 0.2.1 + 0.2.1.9000 diff --git a/docs/reference/ts_alos_plt.html b/docs/reference/ts_alos_plt.html index 410e3b8..141fbec 100644 --- a/docs/reference/ts_alos_plt.html +++ b/docs/reference/ts_alos_plt.html @@ -10,7 +10,7 @@ healthyR - 0.2.1 + 0.2.1.9000 diff --git a/docs/reference/ts_census_los_daily_tbl.html b/docs/reference/ts_census_los_daily_tbl.html index dd36c93..f02f5b3 100644 --- a/docs/reference/ts_census_los_daily_tbl.html +++ b/docs/reference/ts_census_los_daily_tbl.html @@ -30,7 +30,7 @@ healthyR - 0.2.1 + 0.2.1.9000 @@ -164,7 +164,6 @@ Examples , .start_date_col = visit_start_date_time , .end_date_col = visit_end_date_time ) -#> Warning: package 'RSQLite' was built under R version 4.2.2 #> # A tibble: 45,572 × 5 #> date visit_start_date_time visit_end_date_time los census #> <date> <date> <date> <int> <dbl> diff --git a/docs/reference/ts_median_excess_plt.html b/docs/reference/ts_median_excess_plt.html index b55f52b..8d5344e 100644 --- a/docs/reference/ts_median_excess_plt.html +++ b/docs/reference/ts_median_excess_plt.html @@ -10,7 +10,7 @@ healthyR - 0.2.1 + 0.2.1.9000 diff --git a/docs/reference/ts_plt.html b/docs/reference/ts_plt.html index 42af5e8..3f547d8 100644 --- a/docs/reference/ts_plt.html +++ b/docs/reference/ts_plt.html @@ -14,7 +14,7 @@ healthyR - 0.2.1 + 0.2.1.9000 diff --git a/docs/reference/ts_readmit_rate_plt.html b/docs/reference/ts_readmit_rate_plt.html index 1ee525d..6081d1b 100644 --- a/docs/reference/ts_readmit_rate_plt.html +++ b/docs/reference/ts_readmit_rate_plt.html @@ -10,7 +10,7 @@ healthyR - 0.2.1 + 0.2.1.9000 diff --git a/docs/reference/ts_signature_tbl.html b/docs/reference/ts_signature_tbl.html index 3be24d2..5fa60e2 100644 --- a/docs/reference/ts_signature_tbl.html +++ b/docs/reference/ts_signature_tbl.html @@ -14,7 +14,7 @@ healthyR - 0.2.1 + 0.2.1.9000 diff --git a/docs/search.json b/docs/search.json index f909bca..7aede83 100644 --- a/docs/search.json +++ b/docs/search.json @@ -1 +1 @@ -[{"path":"https://www.spsanderson.com/healthyR/articles/getting-started.html","id":"libaray-load","dir":"Articles","previous_headings":"","what":"Libaray Load","title":"Getting Started with healthyR","text":"First things first, lets load library:","code":"library(healthyR) library(healthyR.data) library(timetk) library(dplyr) library(purrr)"},{"path":"https://www.spsanderson.com/healthyR/articles/getting-started.html","id":"generate-sample-data","dir":"Articles","previous_headings":"","what":"Generate Sample Data","title":"Getting Started with healthyR","text":"First going take look time series plotting functions. fairly straight forward therefore seem intuitive. going generate random numbers simulate different daily average length stay data. set seed reproducibility.","code":"# Get Length of Stay Data data_tbl <- healthyR_data df_tbl <- data_tbl %>% filter(ip_op_flag == \"I\") %>% select(visit_end_date_time, length_of_stay) %>% summarise_by_time( .date_var = visit_end_date_time , .by = \"day\" , visits = mean(length_of_stay, na.rm = TRUE) ) %>% filter_by_time( .date_var = visit_end_date_time , .start_date = \"2012\" , .end_date = \"2019\" ) %>% set_names(\"Date\",\"Values\")"},{"path":"https://www.spsanderson.com/healthyR/articles/getting-started.html","id":"plot-the-time-series","dir":"Articles","previous_headings":"","what":"Plot the Time Series","title":"Getting Started with healthyR","text":"Now data lets see easy generate ALOS chart: .interactive option set TRUE: can see, function ability return either static plot interactive plot. hood using timetk::plot_time_series function. can find timetk function . end first quick tutorial ts_alos_plt function.","code":"ts_alos_plt( .data = df_tbl , .date_col = Date , .value_col = Values , .by = \"month\" , .interactive = FALSE ) ts_alos_plt( .data = df_tbl , .date_col = Date , .value_col = Values , .by = \"month\" , .interactive = TRUE )"},{"path":"https://www.spsanderson.com/healthyR/articles/kmeans-umap.html","id":"libaray-load","dir":"Articles","previous_headings":"","what":"Libaray Load","title":"Clustering with K-Means and UMAP","text":"First things first, lets load library:","code":"library(healthyR)"},{"path":"https://www.spsanderson.com/healthyR/articles/kmeans-umap.html","id":"information","dir":"Articles","previous_headings":"","what":"Information","title":"Clustering with K-Means and UMAP","text":"K-Means partion algorithm initially designed signal processing. goal partition n observations k clusters n k. unsupervised k-means algorithm loose relationship k-nearest neighbor classifier, popular supervised machine learning technique classification often confused k-means due name. Applying 1-nearest neighbor classifier cluster centers obtained k-means classifies new data existing clusters. aim vignette showcase use healthyR wrapper kmeans function wrapper plot uwot::umap projection function. go entire workflow getting data getting fina UMAP plot.","code":""},{"path":"https://www.spsanderson.com/healthyR/articles/kmeans-umap.html","id":"generate-some-data","dir":"Articles","previous_headings":"","what":"Generate some data","title":"Clustering with K-Means and UMAP","text":"Now data need generate called user item table. use function kmeans_user_item_tbl takes just arguments. purpose user item table aggregate normalize data users items. data generated going look clustering amongst service_lines (user) payer_grouping (item) columns. Lets now create user item table.","code":"library(healthyR.data) library(dplyr) library(broom) library(ggplot2) 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() data_tbl %>% glimpse() #> Rows: 116,823 #> Columns: 3 #> $ service_line \"Medical\", \"Schizophrenia\", \"Syncope\", \"Pneumonia\", \"Ch… #> $ payer_grouping \"Blue Cross\", \"Medicare A\", \"Medicare A\", \"Medicare A\",… #> $ record 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1…"},{"path":"https://www.spsanderson.com/healthyR/articles/kmeans-umap.html","id":"user-item-tibble","dir":"Articles","previous_headings":"","what":"User Item Tibble","title":"Clustering with K-Means and UMAP","text":"table aggregated item various users algorithm applied. Now data need find optimal k (clusters). need generate table data column k k apply k-means function data k return total within sum squares. convienent function called kmeans_mapped_tbl takes sole argument output kmeans_user_item_tbl. argument .centers default set 15.","code":"uit_tbl <- kmeans_user_item_tbl(data_tbl, service_line, payer_grouping, record) uit_tbl #> # A tibble: 23 × 12 #> service_line Blue …¹ Comme…² Compe…³ Excha…⁴ HMO Medic…⁵ Medic…⁶ Medic…⁷ #> #> 1 Alcohol Abuse 0.0941 0.0321 5.25e-4 0.0116 0.0788 0.158 0.367 0.173 #> 2 Bariatric Sur… 0.317 0.0583 0 0.0518 0.168 0.00324 0.343 0.0485 #> 3 Carotid Endar… 0.0845 0.0282 0 0 0.0141 0 0.0282 0.648 #> 4 Cellulitis 0.110 0.0339 1.18e-2 0.00847 0.0805 0.0869 0.192 0.355 #> 5 Chest Pain 0.144 0.0391 2.90e-3 0.00543 0.112 0.0522 0.159 0.324 #> 6 CHF 0.0295 0.00958 5.18e-4 0.00414 0.0205 0.0197 0.0596 0.657 #> 7 COPD 0.0493 0.0228 2.28e-4 0.00548 0.0342 0.0461 0.172 0.520 #> 8 CVA 0.0647 0.0246 1.07e-3 0.0107 0.0524 0.0289 0.0764 0.555 #> 9 GI Hemorrhage 0.0542 0.0175 1.25e-3 0.00834 0.0480 0.0350 0.0855 0.588 #> 10 Joint Replace… 0.139 0.0179 3.36e-2 0.00673 0.0516 0 0.0874 0.5 #> # … with 13 more rows, 3 more variables: `Medicare HMO` , #> # `No Fault` , `Self Pay` , and abbreviated variable names #> # ¹`Blue Cross`, ²Commercial, ³Compensation, ⁴`Exchange Plans`, ⁵Medicaid, #> # ⁶`Medicaid HMO`, ⁷`Medicare A` #> # ℹ Use `print(n = ...)` to see more rows, and `colnames()` to see all variable names"},{"path":"https://www.spsanderson.com/healthyR/articles/kmeans-umap.html","id":"k-means-mapped-tibble","dir":"Articles","previous_headings":"","what":"K-Means Mapped Tibble","title":"Clustering with K-Means and UMAP","text":"see three columns, centers, k_means glance. k_means column k_means list object glance tibble returned broom::glance function. stated use tot.withinss decide become k, easy way visualize Scree Plot, also known elbow plot. done ploting x-axis centers y-axis tot.withinss.","code":"kmm_tbl <- kmeans_mapped_tbl(uit_tbl) kmm_tbl #> # A tibble: 15 × 3 #> centers k_means glance #> #> 1 1 #> 2 2 #> 3 3 #> 4 4 #> 5 5 #> 6 6 #> 7 7 #> 8 8 #> 9 9 #> 10 10 #> 11 11 #> 12 12 #> 13 13 #> 14 14 #> 15 15 kmm_tbl %>% tidyr::unnest(glance) #> # A tibble: 15 × 6 #> centers k_means totss tot.withinss betweenss iter #> #> 1 1 1.41 1.41 1.33e-15 1 #> 2 2 1.41 0.592 8.17e- 1 1 #> 3 3 1.41 0.372 1.04e+ 0 2 #> 4 4 1.41 0.276 1.13e+ 0 2 #> 5 5 1.41 0.202 1.21e+ 0 2 #> 6 6 1.41 0.159 1.25e+ 0 4 #> 7 7 1.41 0.124 1.28e+ 0 3 #> 8 8 1.41 0.0884 1.32e+ 0 2 #> 9 9 1.41 0.0745 1.33e+ 0 3 #> 10 10 1.41 0.0576 1.35e+ 0 3 #> 11 11 1.41 0.0460 1.36e+ 0 2 #> 12 12 1.41 0.0363 1.37e+ 0 3 #> 13 13 1.41 0.0272 1.38e+ 0 2 #> 14 14 1.41 0.0202 1.39e+ 0 3 #> 15 15 1.41 0.0164 1.39e+ 0 2"},{"path":"https://www.spsanderson.com/healthyR/articles/kmeans-umap.html","id":"scree-plot-and-data","dir":"Articles","previous_headings":"","what":"Scree Plot and Data","title":"Clustering with K-Means and UMAP","text":"want see scree plot data creates plot can use another function kmeans_scree_data_tbl. pieces information can decide upon value k, instance going use 3. Now can go ahead creating umap list object can take look great many things associated data.","code":"kmeans_scree_plt(.data = kmm_tbl) kmeans_scree_data_tbl(kmm_tbl) #> # A tibble: 15 × 2 #> centers tot.withinss #> #> 1 1 1.41 #> 2 2 0.592 #> 3 3 0.372 #> 4 4 0.276 #> 5 5 0.202 #> 6 6 0.159 #> 7 7 0.124 #> 8 8 0.0884 #> 9 9 0.0745 #> 10 10 0.0576 #> 11 11 0.0460 #> 12 12 0.0363 #> 13 13 0.0272 #> 14 14 0.0202 #> 15 15 0.0164"},{"path":"https://www.spsanderson.com/healthyR/articles/kmeans-umap.html","id":"umap-list-object","dir":"Articles","previous_headings":"","what":"UMAP List Object","title":"Clustering with K-Means and UMAP","text":"Now lets go ahead create UMAP list object. Now created, lets take look item list. umap_list function returns list 5 items. umap_obj umap_results_tbl kmeans_obj kmeans_cluster_tbl umap_kmeans_cluster_results_tbl Since list object can now inspect kmeans_obj, first thing use kmeans_tidy_tbl function inspect things.","code":"ump_lst <- umap_list(.data = uit_tbl, kmm_tbl, 3) km_obj <- ump_lst$kmeans_obj kmeans_tidy_tbl(.kmeans_obj = km_obj, .data = uit_tbl, .tidy_type = \"glance\") #> # A tibble: 1 × 4 #> totss tot.withinss betweenss iter #> #> 1 1.41 0.372 1.04 2 kmeans_tidy_tbl(km_obj, uit_tbl, \"augment\") #> # A tibble: 23 × 2 #> service_line cluster #> #> 1 Alcohol Abuse 3 #> 2 Bariatric Surgery For Obesity 3 #> 3 Carotid Endarterectomy 1 #> 4 Cellulitis 2 #> 5 Chest Pain 2 #> 6 CHF 1 #> 7 COPD 1 #> 8 CVA 1 #> 9 GI Hemorrhage 1 #> 10 Joint Replacement 1 #> # … with 13 more rows #> # ℹ Use `print(n = ...)` to see more rows kmeans_tidy_tbl(km_obj, uit_tbl, \"tidy\") #> # A tibble: 3 × 14 #> Blue …¹ Comme…² Compe…³ Excha…⁴ HMO Medic…⁵ Medic…⁶ Medic…⁷ Medic…⁸ No Fa…⁹ #> #> 1 0.0784 0.0218 4.32e-3 0.00620 0.0449 0.0368 0.0800 0.563 0.152 0.00348 #> 2 0.117 0.0314 1.02e-2 0.0139 0.0982 0.0856 0.147 0.354 0.105 0.00707 #> 3 0.150 0.0368 3.07e-4 0.0207 0.163 0.131 0.314 0.132 0.0319 0.00136 #> # … with 4 more variables: `Self Pay` , size , withinss , #> # cluster , and abbreviated variable names ¹`Blue Cross`, ²Commercial, #> # ³Compensation, ⁴`Exchange Plans`, ⁵Medicaid, ⁶`Medicaid HMO`, #> # ⁷`Medicare A`, ⁸`Medicare HMO`, ⁹`No Fault` #> # ℹ Use `colnames()` to see all variable names"},{"path":"https://www.spsanderson.com/healthyR/articles/kmeans-umap.html","id":"umap-plot","dir":"Articles","previous_headings":"","what":"UMAP Plot","title":"Clustering with K-Means and UMAP","text":"Now data can visualize clusters colored cluster number.","code":"umap_plt(.data = ump_lst, .point_size = 3, TRUE)"},{"path":"https://www.spsanderson.com/healthyR/authors.html","id":null,"dir":"","previous_headings":"","what":"Authors","title":"Authors and Citation","text":"Steven Sanderson. Author, maintainer. Steven Sanderson. Copyright holder.","code":""},{"path":"https://www.spsanderson.com/healthyR/authors.html","id":"citation","dir":"","previous_headings":"","what":"Citation","title":"Authors and Citation","text":"Sanderson S (2023). healthyR: Hospital Data Analysis Workflow Tools. R package version 0.2.1, https://github.com/spsanderson/healthyR.","code":"@Manual{, title = {healthyR: Hospital Data Analysis Workflow Tools}, author = {Steven Sanderson}, year = {2023}, note = {R package version 0.2.1}, url = {https://github.com/spsanderson/healthyR}, }"},{"path":[]},{"path":"https://www.spsanderson.com/healthyR/CODE_OF_CONDUCT.html","id":"our-pledge","dir":"","previous_headings":"","what":"Our Pledge","title":"Contributor Covenant Code of Conduct","text":"interest fostering open welcoming environment, contributors maintainers pledge making participation project community harassment-free experience everyone, regardless age, body size, disability, ethnicity, sex characteristics, gender identity expression, level experience, education, socio-economic status, nationality, personal appearance, race, religion, sexual identity orientation.","code":""},{"path":"https://www.spsanderson.com/healthyR/CODE_OF_CONDUCT.html","id":"our-standards","dir":"","previous_headings":"","what":"Our Standards","title":"Contributor Covenant Code of Conduct","text":"Examples behavior contributes creating positive environment include: Using welcoming inclusive language respectful differing viewpoints experiences Gracefully accepting constructive criticism Focusing best community Showing empathy towards community members Examples unacceptable behavior participants include: use sexualized language imagery unwelcome sexual attention advances Trolling, insulting/derogatory comments, personal political attacks Public private harassment Publishing others’ private information, physical electronic address, without explicit permission conduct reasonably considered inappropriate professional setting","code":""},{"path":"https://www.spsanderson.com/healthyR/CODE_OF_CONDUCT.html","id":"our-responsibilities","dir":"","previous_headings":"","what":"Our Responsibilities","title":"Contributor Covenant Code of Conduct","text":"Project maintainers responsible clarifying standards acceptable behavior expected take appropriate fair corrective action response instances unacceptable behavior. Project maintainers right responsibility remove, edit, reject comments, commits, code, wiki edits, issues, contributions aligned Code Conduct, ban temporarily permanently contributor behaviors deem inappropriate, threatening, offensive, harmful.","code":""},{"path":"https://www.spsanderson.com/healthyR/CODE_OF_CONDUCT.html","id":"scope","dir":"","previous_headings":"","what":"Scope","title":"Contributor Covenant Code of Conduct","text":"Code Conduct applies within project spaces public spaces individual representing project community. Examples representing project community include using official project e-mail address, posting via official social media account, acting appointed representative online offline event. Representation project may defined clarified project maintainers.","code":""},{"path":"https://www.spsanderson.com/healthyR/CODE_OF_CONDUCT.html","id":"enforcement","dir":"","previous_headings":"","what":"Enforcement","title":"Contributor Covenant Code of Conduct","text":"Instances abusive, harassing, otherwise unacceptable behavior may reported contacting project team support@spsanderson.com. complaints reviewed investigated result response deemed necessary appropriate circumstances. project team obligated maintain confidentiality regard reporter incident. details specific enforcement policies may posted separately. Project maintainers follow enforce Code Conduct good faith may face temporary permanent repercussions determined members project’s leadership.","code":""},{"path":"https://www.spsanderson.com/healthyR/CODE_OF_CONDUCT.html","id":"attribution","dir":"","previous_headings":"","what":"Attribution","title":"Contributor Covenant Code of Conduct","text":"Code Conduct adapted Contributor Covenant, version 1.4, available https://www.contributor-covenant.org/version/1/4/code--conduct.html answers common questions code conduct, see https://www.contributor-covenant.org/faq","code":""},{"path":"https://www.spsanderson.com/healthyR/index.html","id":"healthyr-","dir":"","previous_headings":"","what":"Hospital Data Analysis Workflow Tools","title":"Hospital Data Analysis Workflow Tools","text":"goal healthyR help quickly analyze common data problems Administrative Clincial spaces.","code":""},{"path":"https://www.spsanderson.com/healthyR/index.html","id":"installation","dir":"","previous_headings":"","what":"Installation","title":"Hospital Data Analysis Workflow Tools","text":"can install released version healthyR CRAN : development version GitHub :","code":"install.packages(\"healthyR\") # install.packages(\"devtools\") devtools::install_github(\"spsanderson/healthyR\")"},{"path":"https://www.spsanderson.com/healthyR/index.html","id":"example","dir":"","previous_headings":"","what":"Example","title":"Hospital Data Analysis Workflow Tools","text":"basic example using ts_median_excess_plt() function`: simple example using ts_signature_tbl() function: simple example using plt_gartner_magic_chart() function:","code":"library(healthyR) library(timetk) library(dplyr) ts_signature_tbl(.data = m4_daily, .date_col = date, .pad_time = TRUE, id) %>% ts_median_excess_plt( .date_col = date , .value_col = value , .x_axis = week , .ggplot_group_var = year , .years_back = 5 ) library(healthyR) library(timetk) ts_signature_tbl(.data = m4_daily, .date_col = date) #> # A tibble: 17,578 × 31 #> id date value index.num diff year year.iso half quarter month #> #> 1 D410 1978-06-23 9109. 267408000 NA 1978 1978 1 2 6 #> 2 D410 1978-06-24 9103. 267494400 86400 1978 1978 1 2 6 #> 3 D410 1978-06-25 9116. 267580800 86400 1978 1978 1 2 6 #> 4 D410 1978-06-26 9116. 267667200 86400 1978 1978 1 2 6 #> 5 D410 1978-06-27 9106. 267753600 86400 1978 1978 1 2 6 #> 6 D410 1978-06-28 9094. 267840000 86400 1978 1978 1 2 6 #> 7 D410 1978-06-29 9094. 267926400 86400 1978 1978 1 2 6 #> 8 D410 1978-06-30 9084. 268012800 86400 1978 1978 1 2 6 #> 9 D410 1978-07-01 9081. 268099200 86400 1978 1978 2 3 7 #> 10 D410 1978-07-02 9047. 268185600 86400 1978 1978 2 3 7 #> # ℹ 17,568 more rows #> # ℹ 21 more variables: month.xts , month.lbl , day , hour , #> # minute , second , hour12 , am.pm , wday , #> # wday.xts , wday.lbl , mday , qday , yday , #> # mweek , week , week.iso , week2 , week3 , #> # week4 , mday7 suppressPackageStartupMessages(library(healthyR)) suppressPackageStartupMessages(library(tibble)) suppressPackageStartupMessages(library(dplyr)) gartner_magic_chart_plt( .data = tibble(x = rnorm(100, 0, 1), y = rnorm(100, 0, 1)) , .x_col = x , .y_col = y , .y_lab = \"los\" , .x_lab = \"RA\" , .plt_title = \"Test Title\" , .tl_lbl = \"Top Left lbl\" , .tr_lbl = \"Top Right lbl\" , .bl_lbl = \"Bottom Left lbl\" , .br_lbl = \"Bottom Right lbl\" )"},{"path":"https://www.spsanderson.com/healthyR/LICENSE.html","id":null,"dir":"","previous_headings":"","what":"MIT License","title":"MIT License","text":"Copyright (c) 2020 Steven Paul Sanderson II Permission hereby granted, free charge, person obtaining copy software associated documentation files (“Software”), deal Software without restriction, including without limitation rights use, copy, modify, merge, publish, distribute, sublicense, /sell copies Software, permit persons Software furnished , subject following conditions: copyright notice permission notice shall included copies substantial portions Software. SOFTWARE PROVIDED “”, WITHOUT WARRANTY KIND, EXPRESS IMPLIED, INCLUDING LIMITED WARRANTIES MERCHANTABILITY, FITNESS PARTICULAR PURPOSE NONINFRINGEMENT. EVENT SHALL AUTHORS COPYRIGHT HOLDERS LIABLE CLAIM, DAMAGES LIABILITY, WHETHER ACTION CONTRACT, TORT OTHERWISE, ARISING , CONNECTION SOFTWARE USE DEALINGS SOFTWARE.","code":""},{"path":"https://www.spsanderson.com/healthyR/reference/category_counts_tbl.html","id":null,"dir":"Reference","previous_headings":"","what":"Counts by Category — category_counts_tbl","title":"Counts by Category — category_counts_tbl","text":"Get counts column particular grouping supplied, otherwise just get counts column.","code":""},{"path":"https://www.spsanderson.com/healthyR/reference/category_counts_tbl.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Counts by Category — category_counts_tbl","text":"","code":"category_counts_tbl(.data, .count_col, .arrange_value = TRUE, ...)"},{"path":"https://www.spsanderson.com/healthyR/reference/category_counts_tbl.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Counts by Category — category_counts_tbl","text":".data data.frame/tibble supplied. .count_col column values want count. .arrange_value Defaults true, arrange resulting tibble descending order .count_col ... Place values want pass grouping .","code":""},{"path":"https://www.spsanderson.com/healthyR/reference/category_counts_tbl.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Counts by Category — category_counts_tbl","text":"Requires data.frame/tibble. Requires value column, column going counted.","code":""},{"path":"https://www.spsanderson.com/healthyR/reference/category_counts_tbl.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Counts by Category — category_counts_tbl","text":"Steven P. Sanderson II, MPH","code":""},{"path":"https://www.spsanderson.com/healthyR/reference/category_counts_tbl.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Counts by Category — category_counts_tbl","text":"","code":"library(healthyR.data) #> #> == Welcome to healthyR.data =========================================================================== #> If you find this package useful, please leave a star: #> https://github.com/spsanderson/healthyR.data' #> #> If you encounter a bug or want to request an enhancement please file an issue at: #> https://github.com/spsanderson/healthyR.data/issues #> #> Thank you for using healthyR.data library(dplyr) #> #> Attaching package: 'dplyr' #> The following objects are masked from 'package:stats': #> #> filter, lag #> The following objects are masked from 'package:base': #> #> intersect, setdiff, setequal, union healthyR_data %>% category_counts_tbl( .count_col = payer_grouping , .arrange = TRUE , ip_op_flag ) #> # A tibble: 25 × 3 #> ip_op_flag payer_grouping n #> #> 1 I Medicare A 52621 #> 2 O Medicare B 22270 #> 3 I Medicaid HMO 15466 #> 4 I Medicare HMO 13572 #> 5 O Blue Cross 13560 #> 6 I Blue Cross 10797 #> 7 O Medicaid HMO 10018 #> 8 O HMO 9331 #> 9 I HMO 8113 #> 10 I Medicaid 7131 #> # ℹ 15 more rows healthyR_data %>% category_counts_tbl( .count_col = ip_op_flag , .arrange_value = TRUE , service_line ) #> # A tibble: 30 × 3 #> service_line ip_op_flag n #> #> 1 Medical I 64435 #> 2 General Outpatient O 50526 #> 3 Surgical I 14916 #> 4 Colonoscopy/Endoscopy O 11486 #> 5 Cataract Removal O 4930 #> 6 COPD I 4398 #> 7 CHF I 3871 #> 8 Pneumonia I 3323 #> 9 Cellulitis I 3311 #> 10 Major Depression/Bipolar Affective Disorders I 2866 #> # ℹ 20 more rows"},{"path":"https://www.spsanderson.com/healthyR/reference/color_blind.html","id":null,"dir":"Reference","previous_headings":"","what":"Provide Colorblind Compliant Colors — color_blind","title":"Provide Colorblind Compliant Colors — color_blind","text":"8 Hex RGB color definitions suitable charts colorblind people.","code":""},{"path":"https://www.spsanderson.com/healthyR/reference/color_blind.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Provide Colorblind Compliant Colors — color_blind","text":"","code":"color_blind()"},{"path":"https://www.spsanderson.com/healthyR/reference/color_blind.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Provide Colorblind Compliant Colors — color_blind","text":"vector 8 Hex RGB definitions.","code":""},{"path":"https://www.spsanderson.com/healthyR/reference/color_blind.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Provide Colorblind Compliant Colors — color_blind","text":"function used others order help render plots color blind.","code":""},{"path":"https://www.spsanderson.com/healthyR/reference/color_blind.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Provide Colorblind Compliant Colors — color_blind","text":"Steven P. Sanderson II, MPH","code":""},{"path":"https://www.spsanderson.com/healthyR/reference/color_blind.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Provide Colorblind Compliant Colors — color_blind","text":"","code":"color_blind() #> [1] \"#000000\" \"#E69F00\" \"#56B4E9\" \"#009E73\" \"#F0E442\" \"#0072B2\" \"#D55E00\" #> [8] \"#CC79A7\""},{"path":"https://www.spsanderson.com/healthyR/reference/diverging_bar_plt.html","id":null,"dir":"Reference","previous_headings":"","what":"Diverging Bar Chart — diverging_bar_plt","title":"Diverging Bar Chart — diverging_bar_plt","text":"Diverging Bars bar chart can handle negative positive values. can implemented smart tweak geom_bar(). usage geom_bar() can quite confusing. , can used make bar chart well histogram. Let explain. default, geom_bar() stat set count. means, provide just continuous X variable (Y variable), tries make histogram data. order make bar chart create bars instead histogram, need two things. Set stat = identity provide x y inside aes() , x either character factor y numeric. order make sure get diverging bars instead just bars, make sure, categorical variable 2 categories changes values certain threshold continuous variable. example, mpg mtcars data set normalized computing z score. vehicles mpg zero marked green marked red.","code":""},{"path":"https://www.spsanderson.com/healthyR/reference/diverging_bar_plt.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Diverging Bar Chart — diverging_bar_plt","text":"","code":"diverging_bar_plt( .data, .x_axis, .y_axis, .fill_col, .plot_title = NULL, .plot_subtitle = NULL, .plot_caption = NULL, .interactive = FALSE )"},{"path":"https://www.spsanderson.com/healthyR/reference/diverging_bar_plt.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Diverging Bar Chart — diverging_bar_plt","text":".data data pass function, must tibble/data.frame. .x_axis data passed x-axis. .y_axis data passed y-axis. also equal parameter label .fill_col column used fill color bars. .plot_title Default NULL .plot_subtitle Default NULL .plot_caption Default NULL .interactive Default FALSE. TRUE returns plotly plot","code":""},{"path":"https://www.spsanderson.com/healthyR/reference/diverging_bar_plt.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Diverging Bar Chart — diverging_bar_plt","text":"plotly plot ggplot2 static plot","code":""},{"path":"https://www.spsanderson.com/healthyR/reference/diverging_bar_plt.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Diverging Bar Chart — diverging_bar_plt","text":"function takes arguments returns ggplot2 object.","code":""},{"path":"https://www.spsanderson.com/healthyR/reference/diverging_bar_plt.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Diverging Bar Chart — diverging_bar_plt","text":"Steven P. Sanderson II, MPH","code":""},{"path":"https://www.spsanderson.com/healthyR/reference/diverging_bar_plt.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Diverging Bar Chart — diverging_bar_plt","text":"","code":"suppressPackageStartupMessages(library(ggplot2)) #> Warning: package 'ggplot2' was built under R version 4.2.2 data(\"mtcars\") mtcars$car_name <- rownames(mtcars) mtcars$mpg_z <- round((mtcars$mpg - mean(mtcars$mpg))/sd(mtcars$mpg), 2) mtcars$mpg_type <- ifelse(mtcars$mpg_z < 0, \"below\", \"above\") mtcars <- mtcars[order(mtcars$mpg_z), ] # sort mtcars$car_name <- factor(mtcars$car_name, levels = mtcars$car_name) diverging_bar_plt( .data = mtcars , .x_axis = car_name , .y_axis = mpg_z , .fill_col = mpg_type , .interactive = FALSE )"},{"path":"https://www.spsanderson.com/healthyR/reference/diverging_lollipop_plt.html","id":null,"dir":"Reference","previous_headings":"","what":"Diverging Lollipop Chart — diverging_lollipop_plt","title":"Diverging Lollipop Chart — diverging_lollipop_plt","text":"diverging lollipop function. Lollipop chart conveys information bar chart diverging bar. Except looks modern. Instead geom_bar, use geom_point geom_segment get lollipops right. Let’s draw lollipop using data prepared previous example diverging bars.","code":""},{"path":"https://www.spsanderson.com/healthyR/reference/diverging_lollipop_plt.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Diverging Lollipop Chart — diverging_lollipop_plt","text":"","code":"diverging_lollipop_plt( .data, .x_axis, .y_axis, .plot_title = NULL, .plot_subtitle = NULL, .plot_caption = NULL, .interactive = FALSE )"},{"path":"https://www.spsanderson.com/healthyR/reference/diverging_lollipop_plt.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Diverging Lollipop Chart — diverging_lollipop_plt","text":".data data pass function, must tibble/data.frame. .x_axis data passed x-axis. also x xend parameters geom_segment .y_axis data passed y-axis. also equal parameters yend label .plot_title Default NULL .plot_subtitle Default NULL .plot_caption Default NULL .interactive Default FALSE. TRUE returns plotly plot","code":""},{"path":"https://www.spsanderson.com/healthyR/reference/diverging_lollipop_plt.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Diverging Lollipop Chart — diverging_lollipop_plt","text":"plotly plot ggplot2 static plot","code":""},{"path":"https://www.spsanderson.com/healthyR/reference/diverging_lollipop_plt.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Diverging Lollipop Chart — diverging_lollipop_plt","text":"function takes arguments returns ggplot2 object.","code":""},{"path":"https://www.spsanderson.com/healthyR/reference/diverging_lollipop_plt.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Diverging Lollipop Chart — diverging_lollipop_plt","text":"Steven P. Sanderson II, MPH","code":""},{"path":"https://www.spsanderson.com/healthyR/reference/diverging_lollipop_plt.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Diverging Lollipop Chart — diverging_lollipop_plt","text":"","code":"suppressPackageStartupMessages(library(ggplot2)) data(\"mtcars\") mtcars$car_name <- rownames(mtcars) mtcars$mpg_z <- round((mtcars$mpg - mean(mtcars$mpg))/sd(mtcars$mpg), 2) mtcars$mpg_type <- ifelse(mtcars$mpg_z < 0, \"below\", \"above\") mtcars <- mtcars[order(mtcars$mpg_z), ] # sort mtcars$car_name <- factor(mtcars$car_name, levels = mtcars$car_name) diverging_lollipop_plt(.data = mtcars, .x_axis = car_name , .y_axis = mpg_z)"},{"path":"https://www.spsanderson.com/healthyR/reference/dx_cc_mapping.html","id":null,"dir":"Reference","previous_headings":"","what":"Diagnosis to Condition Code Mapping file — dx_cc_mapping","title":"Diagnosis to Condition Code Mapping file — dx_cc_mapping","text":"dataset containing Diagnosis Code AHRQ Condition Code Mapping used helping define service lines inpatient discharges.","code":""},{"path":"https://www.spsanderson.com/healthyR/reference/dx_cc_mapping.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Diagnosis to Condition Code Mapping file — dx_cc_mapping","text":"","code":"data(dx_cc_mapping)"},{"path":"https://www.spsanderson.com/healthyR/reference/dx_cc_mapping.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Diagnosis to Condition Code Mapping file — dx_cc_mapping","text":"data frame 86852 rows 5 variables","code":""},{"path":"https://www.spsanderson.com/healthyR/reference/dx_cc_mapping.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Diagnosis to Condition Code Mapping file — dx_cc_mapping","text":"CC_Code. DX_1, DX_2, ..., DX_n CC_Desc. DX_1 = Conduction disorders, DX_n = description ICD_Ver_Flag. ICD Version 10 9 ICDCode. ICD-9 ro ICD-10 Code Diagnosis. Long QT Syndrome","code":""},{"path":"https://www.spsanderson.com/healthyR/reference/gartner_magic_chart_plt.html","id":null,"dir":"Reference","previous_headings":"","what":"Gartner Magic Chart - Plotting of two continuous variables — gartner_magic_chart_plt","title":"Gartner Magic Chart - Plotting of two continuous variables — gartner_magic_chart_plt","text":"Plot Gartner Magic Chart two continuous variables","code":""},{"path":"https://www.spsanderson.com/healthyR/reference/gartner_magic_chart_plt.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Gartner Magic Chart - Plotting of two continuous variables — gartner_magic_chart_plt","text":"","code":"gartner_magic_chart_plt( .data, .x_col, .y_col, .point_size_col = NULL, .y_lab, .x_lab, .plt_title, .tl_lbl, .tr_lbl, .br_lbl, .bl_lbl )"},{"path":"https://www.spsanderson.com/healthyR/reference/gartner_magic_chart_plt.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Gartner Magic Chart - Plotting of two continuous variables — gartner_magic_chart_plt","text":".data data set want plot .x_col x-axis plot .y_col y-axis plot .point_size_col default NULL, want size dots column data.frame/tibble enter column name . .y_lab y-axis label .x_lab x-axis label .plt_title title plot .tl_lbl top left label .tr_lbl top right label .br_lbl bottom right label .bl_lbl bottom left label","code":""},{"path":"https://www.spsanderson.com/healthyR/reference/gartner_magic_chart_plt.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Gartner Magic Chart - Plotting of two continuous variables — gartner_magic_chart_plt","text":"ggplot plot","code":""},{"path":"https://www.spsanderson.com/healthyR/reference/gartner_magic_chart_plt.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Gartner Magic Chart - Plotting of two continuous variables — gartner_magic_chart_plt","text":"Supply data frame least two continuous variables plot ","code":""},{"path":"https://www.spsanderson.com/healthyR/reference/gartner_magic_chart_plt.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Gartner Magic Chart - Plotting of two continuous variables — gartner_magic_chart_plt","text":"Steven P. Sanderson II, MPH","code":""},{"path":"https://www.spsanderson.com/healthyR/reference/gartner_magic_chart_plt.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Gartner Magic Chart - Plotting of two continuous variables — gartner_magic_chart_plt","text":"","code":"library(dplyr) data_tbl <- tibble( x = rnorm(100, 0, 1), y = rnorm(100, 0, 1), z = abs(x) + abs(y) ) gartner_magic_chart_plt( .data = data_tbl, .x_col = x, .y_col = y, .point_size = z, .x_lab = \"los\", .y_lab = \"ra\", .plt_title = \"tst\", .tr_lbl = \"High RA-LOS\", .tl_lbl = \"High RA\", .bl_lbl = \"Leader\", .br_lbl = \"High LOS\" ) gartner_magic_chart_plt( .data = data_tbl, .x_col = x, .y_col = y, .point_size = NULL, .x_lab = \"los\", .y_lab = \"ra\", .plt_title = \"tst\", .tr_lbl = \"High RA-LOS\", .tl_lbl = \"High RA\", .bl_lbl = \"Leader\", .br_lbl = \"High LOS\" )"},{"path":"https://www.spsanderson.com/healthyR/reference/hr_scale_color_colorblind.html","id":null,"dir":"Reference","previous_headings":"","what":"Provide Colorblind Compliant Colors — hr_scale_color_colorblind","title":"Provide Colorblind Compliant Colors — hr_scale_color_colorblind","text":"8 Hex RGB color definitions suitable charts colorblind people.","code":""},{"path":"https://www.spsanderson.com/healthyR/reference/hr_scale_color_colorblind.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Provide Colorblind Compliant Colors — hr_scale_color_colorblind","text":"","code":"hr_scale_color_colorblind(..., theme = \"hr\")"},{"path":"https://www.spsanderson.com/healthyR/reference/hr_scale_color_colorblind.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Provide Colorblind Compliant Colors — hr_scale_color_colorblind","text":"... Data passed ggplot object theme Right now hr . Anything else render error.","code":""},{"path":"https://www.spsanderson.com/healthyR/reference/hr_scale_color_colorblind.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Provide Colorblind Compliant Colors — hr_scale_color_colorblind","text":"gggplot layer","code":""},{"path":"https://www.spsanderson.com/healthyR/reference/hr_scale_color_colorblind.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Provide Colorblind Compliant Colors — hr_scale_color_colorblind","text":"function used others order help render plots color blind.","code":""},{"path":"https://www.spsanderson.com/healthyR/reference/hr_scale_color_colorblind.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Provide Colorblind Compliant Colors — hr_scale_color_colorblind","text":"Steven P. Sanderson II, MPH","code":""},{"path":"https://www.spsanderson.com/healthyR/reference/hr_scale_fill_colorblind.html","id":null,"dir":"Reference","previous_headings":"","what":"Provide Colorblind Compliant Colors — hr_scale_fill_colorblind","title":"Provide Colorblind Compliant Colors — hr_scale_fill_colorblind","text":"8 Hex RGB color definitions suitable charts colorblind people.","code":""},{"path":"https://www.spsanderson.com/healthyR/reference/hr_scale_fill_colorblind.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Provide Colorblind Compliant Colors — hr_scale_fill_colorblind","text":"","code":"hr_scale_fill_colorblind(..., theme = \"hr\")"},{"path":"https://www.spsanderson.com/healthyR/reference/hr_scale_fill_colorblind.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Provide Colorblind Compliant Colors — hr_scale_fill_colorblind","text":"... Data passed ggplot object theme Right now hr . Anything else render error.","code":""},{"path":"https://www.spsanderson.com/healthyR/reference/hr_scale_fill_colorblind.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Provide Colorblind Compliant Colors — hr_scale_fill_colorblind","text":"gggplot layer","code":""},{"path":"https://www.spsanderson.com/healthyR/reference/hr_scale_fill_colorblind.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Provide Colorblind Compliant Colors — hr_scale_fill_colorblind","text":"function used others order help render plots color blind.","code":""},{"path":"https://www.spsanderson.com/healthyR/reference/hr_scale_fill_colorblind.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Provide Colorblind Compliant Colors — hr_scale_fill_colorblind","text":"Steven P. Sanderson II, MPH","code":""},{"path":"https://www.spsanderson.com/healthyR/reference/kmeans_mapped_tbl.html","id":null,"dir":"Reference","previous_headings":"","what":"K-Means Mapper — kmeans_mapped_tbl","title":"K-Means Mapper — kmeans_mapped_tbl","text":"Create tibble maps kmeans_obj() using purrr::map() create nested data.frame/tibble holds n centers. tibble used help create scree plot.","code":""},{"path":"https://www.spsanderson.com/healthyR/reference/kmeans_mapped_tbl.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"K-Means Mapper — kmeans_mapped_tbl","text":"","code":"kmeans_mapped_tbl(.data, .centers = 15)"},{"path":"https://www.spsanderson.com/healthyR/reference/kmeans_mapped_tbl.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"K-Means Mapper — kmeans_mapped_tbl","text":".data must tibble working environment kmeans_user_item_tbl() .centers many different centers want try","code":""},{"path":"https://www.spsanderson.com/healthyR/reference/kmeans_mapped_tbl.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"K-Means Mapper — kmeans_mapped_tbl","text":"nested tibble","code":""},{"path":"https://www.spsanderson.com/healthyR/reference/kmeans_mapped_tbl.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"K-Means Mapper — kmeans_mapped_tbl","text":"Takes single parameter .centers. used create tibble map kmeans_obj() function list creating nested tibble.","code":""},{"path":[]},{"path":"https://www.spsanderson.com/healthyR/reference/kmeans_mapped_tbl.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"K-Means Mapper — kmeans_mapped_tbl","text":"Steven P. Sanderson II, MPH","code":""},{"path":"https://www.spsanderson.com/healthyR/reference/kmeans_mapped_tbl.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"K-Means Mapper — kmeans_mapped_tbl","text":"","code":"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() ui_tbl <- kmeans_user_item_tbl( .data = data_tbl , .row_input = service_line , .col_input = payer_grouping , .record_input = record ) kmeans_mapped_tbl(ui_tbl) #> # A tibble: 15 × 3 #> centers k_means glance #> #> 1 1 #> 2 2 #> 3 3 #> 4 4 #> 5 5 #> 6 6 #> 7 7 #> 8 8 #> 9 9 #> 10 10 #> 11 11 #> 12 12 #> 13 13 #> 14 14 #> 15 15 "},{"path":"https://www.spsanderson.com/healthyR/reference/kmeans_obj.html","id":null,"dir":"Reference","previous_headings":"","what":"K-Means Functions — kmeans_obj","title":"K-Means Functions — kmeans_obj","text":"Takes output kmeans_user_item_tbl() function applies k-means algorithm using stats::kmeans()","code":""},{"path":"https://www.spsanderson.com/healthyR/reference/kmeans_obj.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"K-Means Functions — kmeans_obj","text":"","code":"kmeans_obj(.data, .centers = 5)"},{"path":"https://www.spsanderson.com/healthyR/reference/kmeans_obj.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"K-Means Functions — kmeans_obj","text":".data data gets passed kmeans_user_item_tbl() .centers many initial centers start ","code":""},{"path":"https://www.spsanderson.com/healthyR/reference/kmeans_obj.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"K-Means Functions — kmeans_obj","text":"stats k-means object","code":""},{"path":"https://www.spsanderson.com/healthyR/reference/kmeans_obj.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"K-Means Functions — kmeans_obj","text":"Uses stats::kmeans() function creates wrapper around .","code":""},{"path":"https://www.spsanderson.com/healthyR/reference/kmeans_obj.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"K-Means Functions — kmeans_obj","text":"Steven P. Sanderson II, MPH","code":""},{"path":"https://www.spsanderson.com/healthyR/reference/kmeans_obj.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"K-Means Functions — kmeans_obj","text":"","code":"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\""},{"path":"https://www.spsanderson.com/healthyR/reference/kmeans_scree_data_tbl.html","id":null,"dir":"Reference","previous_headings":"","what":"K-Means Scree Plot Data Table — kmeans_scree_data_tbl","title":"K-Means Scree Plot Data Table — kmeans_scree_data_tbl","text":"Take data kmeans_mapped_tbl() unnest tibble inspection use kmeans_scree_plt() function.","code":""},{"path":"https://www.spsanderson.com/healthyR/reference/kmeans_scree_data_tbl.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"K-Means Scree Plot Data Table — kmeans_scree_data_tbl","text":"","code":"kmeans_scree_data_tbl(.data)"},{"path":"https://www.spsanderson.com/healthyR/reference/kmeans_scree_data_tbl.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"K-Means Scree Plot Data Table — kmeans_scree_data_tbl","text":".data must tibble working environment kmeans_mapped_tbl()","code":""},{"path":"https://www.spsanderson.com/healthyR/reference/kmeans_scree_data_tbl.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"K-Means Scree Plot Data Table — kmeans_scree_data_tbl","text":"nested tibble","code":""},{"path":"https://www.spsanderson.com/healthyR/reference/kmeans_scree_data_tbl.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"K-Means Scree Plot Data Table — kmeans_scree_data_tbl","text":"Takes single parameter .data kmeans_mapped_tbl() transforms tibble used kmeans_scree_plt(). show values (tot.withinss) center.","code":""},{"path":"https://www.spsanderson.com/healthyR/reference/kmeans_scree_data_tbl.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"K-Means Scree Plot Data Table — kmeans_scree_data_tbl","text":"Steven P. Sanderson II, MPH","code":""},{"path":"https://www.spsanderson.com/healthyR/reference/kmeans_scree_data_tbl.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"K-Means Scree Plot Data Table — kmeans_scree_data_tbl","text":"","code":"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() ui_tbl <- kmeans_user_item_tbl( .data = data_tbl , .row_input = service_line , .col_input = payer_grouping , .record_input = record ) kmm_tbl <- kmeans_mapped_tbl(ui_tbl) kmeans_scree_data_tbl(kmm_tbl) #> # A tibble: 15 × 2 #> centers tot.withinss #> #> 1 1 1.41 #> 2 2 0.592 #> 3 3 0.372 #> 4 4 0.276 #> 5 5 0.202 #> 6 6 0.159 #> 7 7 0.124 #> 8 8 0.0922 #> 9 9 0.0722 #> 10 10 0.0576 #> 11 11 0.0461 #> 12 12 0.0363 #> 13 13 0.0272 #> 14 14 0.0231 #> 15 15 0.0160"},{"path":"https://www.spsanderson.com/healthyR/reference/kmeans_scree_plt.html","id":null,"dir":"Reference","previous_headings":"","what":"K-Means Scree Plot — kmeans_scree_plt","title":"K-Means Scree Plot — kmeans_scree_plt","text":"Create scree-plot kmeans_mapped_tbl() function.","code":""},{"path":"https://www.spsanderson.com/healthyR/reference/kmeans_scree_plt.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"K-Means Scree Plot — kmeans_scree_plt","text":"","code":"kmeans_scree_plt(.data)"},{"path":"https://www.spsanderson.com/healthyR/reference/kmeans_scree_plt.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"K-Means Scree Plot — kmeans_scree_plt","text":".data data kmeans_mapped_tbl() function","code":""},{"path":"https://www.spsanderson.com/healthyR/reference/kmeans_scree_plt.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"K-Means Scree Plot — kmeans_scree_plt","text":"ggplot2 plot","code":""},{"path":"https://www.spsanderson.com/healthyR/reference/kmeans_scree_plt.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"K-Means Scree Plot — kmeans_scree_plt","text":"Outputs scree-plot","code":""},{"path":[]},{"path":"https://www.spsanderson.com/healthyR/reference/kmeans_scree_plt.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"K-Means Scree Plot — kmeans_scree_plt","text":"Steven P. Sanderson II, MPH","code":""},{"path":"https://www.spsanderson.com/healthyR/reference/kmeans_scree_plt.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"K-Means Scree Plot — kmeans_scree_plt","text":"","code":"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() ui_tbl <- kmeans_user_item_tbl( .data = data_tbl , .row_input = service_line , .col_input = payer_grouping , .record_input = record ) kmm_tbl <- kmeans_mapped_tbl(ui_tbl) kmeans_scree_plt(.data = kmm_tbl)"},{"path":"https://www.spsanderson.com/healthyR/reference/kmeans_tidy_tbl.html","id":null,"dir":"Reference","previous_headings":"","what":"K-Means tidy Functions — kmeans_tidy_tbl","title":"K-Means tidy Functions — kmeans_tidy_tbl","text":"K-Means tidy functions","code":""},{"path":"https://www.spsanderson.com/healthyR/reference/kmeans_tidy_tbl.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"K-Means tidy Functions — kmeans_tidy_tbl","text":"","code":"kmeans_tidy_tbl(.kmeans_obj, .data, .tidy_type = \"tidy\")"},{"path":"https://www.spsanderson.com/healthyR/reference/kmeans_tidy_tbl.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"K-Means tidy Functions — kmeans_tidy_tbl","text":".kmeans_obj stats::kmeans() object .data user item tibble created kmeans_user_item_tbl() .tidy_type \"tidy\",\"glance\", \"augment\"","code":""},{"path":"https://www.spsanderson.com/healthyR/reference/kmeans_tidy_tbl.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"K-Means tidy Functions — kmeans_tidy_tbl","text":"tibble","code":""},{"path":"https://www.spsanderson.com/healthyR/reference/kmeans_tidy_tbl.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"K-Means tidy Functions — kmeans_tidy_tbl","text":"Takes k-means object associated user item tibble returns one items asked . Either: broom::tidy(), broom::glance() broom::augment(). function defaults broom::tidy().","code":""},{"path":"https://www.spsanderson.com/healthyR/reference/kmeans_tidy_tbl.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"K-Means tidy Functions — kmeans_tidy_tbl","text":"Steven P. Sanderson II, MPH","code":""},{"path":"https://www.spsanderson.com/healthyR/reference/kmeans_tidy_tbl.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"K-Means tidy Functions — kmeans_tidy_tbl","text":"","code":"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 <- kmeans_user_item_tbl( .data = data_tbl , .row_input = service_line , .col_input = payer_grouping , .record_input = record ) km_obj <- kmeans_obj(uit_tbl) kmeans_tidy_tbl( .kmeans_obj = km_obj , .data = uit_tbl , .tidy_type = \"augment\" ) #> # A tibble: 23 × 2 #> service_line cluster #> #> 1 Alcohol Abuse 5 #> 2 Bariatric Surgery For Obesity 1 #> 3 CHF 3 #> 4 COPD 3 #> 5 CVA 3 #> 6 Carotid Endarterectomy 3 #> 7 Cellulitis 4 #> 8 Chest Pain 4 #> 9 GI Hemorrhage 3 #> 10 Joint Replacement 3 #> # … with 13 more rows #> # ℹ Use `print(n = ...)` to see more rows kmeans_tidy_tbl( .kmeans_obj = km_obj , .data = uit_tbl , .tidy_type = \"glance\" ) #> # A tibble: 1 × 4 #> totss tot.withinss betweenss iter #> #> 1 1.41 0.202 1.21 2 kmeans_tidy_tbl( .kmeans_obj = km_obj , .data = uit_tbl , .tidy_type = \"tidy\" ) %>% glimpse() #> Rows: 5 #> Columns: 14 #> $ `Blue Cross` 0.27188303, 0.00000000, 0.07837450, 0.13375082, 0.079… #> $ Commercial 0.05712358, 0.00000000, 0.02182129, 0.03542694, 0.027… #> $ Compensation 0.0003293808, 0.0000000000, 0.0043244347, 0.012199847… #> $ `Exchange Plans` 0.039065198, 0.000000000, 0.006202137, 0.016160901, 0… #> $ HMO 0.18065096, 0.27272727, 0.04493860, 0.10724914, 0.077… #> $ Medicaid 0.04246134, 0.18181818, 0.03684344, 0.05150211, 0.214… #> $ `Medicaid HMO` 0.24760799, 0.45454545, 0.08001653, 0.13107693, 0.282… #> $ `Medicare A` 0.10958146, 0.09090909, 0.56250366, 0.35217108, 0.236… #> $ `Medicare HMO` 0.03584494, 0.00000000, 0.15152338, 0.11769769, 0.043… #> $ `No Fault` 0.000000000, 0.000000000, 0.003475542, 0.008242686, 0… #> $ `Self Pay` 0.015452115, 0.000000000, 0.009976485, 0.034521844, 0… #> $ size 2, 1, 12, 5, 3 #> $ withinss 0.03549821, 0.00000000, 0.09625399, 0.02592247, 0.044… #> $ cluster 1, 2, 3, 4, 5"},{"path":"https://www.spsanderson.com/healthyR/reference/kmeans_user_item_tbl.html","id":null,"dir":"Reference","previous_headings":"","what":"K-Means Functions — kmeans_user_item_tbl","title":"K-Means Functions — kmeans_user_item_tbl","text":"Takes data.frame/tibble transforms aggregated/normalized user-item tibble proportions. user need input parameters rows/user columns/items.","code":""},{"path":"https://www.spsanderson.com/healthyR/reference/kmeans_user_item_tbl.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"K-Means Functions — kmeans_user_item_tbl","text":"","code":"kmeans_user_item_tbl(.data, .row_input, .col_input, .record_input)"},{"path":"https://www.spsanderson.com/healthyR/reference/kmeans_user_item_tbl.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"K-Means Functions — kmeans_user_item_tbl","text":".data data want transform .row_input column going row (user) .col_input column going column (item) .record_input column going summed aggregattion normalization process.","code":""},{"path":"https://www.spsanderson.com/healthyR/reference/kmeans_user_item_tbl.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"K-Means Functions — kmeans_user_item_tbl","text":"aggregated/normalized user item tibble","code":""},{"path":"https://www.spsanderson.com/healthyR/reference/kmeans_user_item_tbl.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"K-Means Functions — kmeans_user_item_tbl","text":"function used using k-mean model. commonly referred user item matrix \"users\" tend rows \"items\" (e.g. orders) columns. must supply column can summed aggregation normalization process occur.","code":""},{"path":"https://www.spsanderson.com/healthyR/reference/kmeans_user_item_tbl.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"K-Means Functions — kmeans_user_item_tbl","text":"Steven P. Sanderson II, MPH","code":""},{"path":"https://www.spsanderson.com/healthyR/reference/kmeans_user_item_tbl.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"K-Means Functions — kmeans_user_item_tbl","text":"","code":"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 ) #> # A tibble: 23 × 12 #> service_line Blue …¹ Comme…² Compe…³ Excha…⁴ HMO Medic…⁵ Medic…⁶ Medic…⁷ #> #> 1 Alcohol Abuse 0.0941 0.0321 5.25e-4 0.0116 0.0788 0.158 0.367 0.173 #> 2 Bariatric Sur… 0.317 0.0583 0 0.0518 0.168 0.00324 0.343 0.0485 #> 3 CHF 0.0295 0.00958 5.18e-4 0.00414 0.0205 0.0197 0.0596 0.657 #> 4 COPD 0.0493 0.0228 2.28e-4 0.00548 0.0342 0.0461 0.172 0.520 #> 5 CVA 0.0647 0.0246 1.07e-3 0.0107 0.0524 0.0289 0.0764 0.555 #> 6 Carotid Endar… 0.0845 0.0282 0 0 0.0141 0 0.0282 0.648 #> 7 Cellulitis 0.110 0.0339 1.18e-2 0.00847 0.0805 0.0869 0.192 0.355 #> 8 Chest Pain 0.144 0.0391 2.90e-3 0.00543 0.112 0.0522 0.159 0.324 #> 9 GI Hemorrhage 0.0542 0.0175 1.25e-3 0.00834 0.0480 0.0350 0.0855 0.588 #> 10 Joint Replace… 0.139 0.0179 3.36e-2 0.00673 0.0516 0 0.0874 0.5 #> # … with 13 more rows, 3 more variables: `Medicare HMO` , #> # `No Fault` , `Self Pay` , and abbreviated variable names #> # ¹`Blue Cross`, ²Commercial, ³Compensation, ⁴`Exchange Plans`, ⁵Medicaid, #> # ⁶`Medicaid HMO`, ⁷`Medicare A` #> # ℹ Use `print(n = ...)` to see more rows, and `colnames()` to see all variable names"},{"path":"https://www.spsanderson.com/healthyR/reference/los_ra_index_plt.html","id":null,"dir":"Reference","previous_headings":"","what":"Plot LOS and Readmit Index with Variance — los_ra_index_plt","title":"Plot LOS and Readmit Index with Variance — los_ra_index_plt","text":"Plot index length stay readmit rate along variance","code":""},{"path":"https://www.spsanderson.com/healthyR/reference/los_ra_index_plt.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Plot LOS and Readmit Index with Variance — los_ra_index_plt","text":"","code":"los_ra_index_plt(.data)"},{"path":"https://www.spsanderson.com/healthyR/reference/los_ra_index_plt.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Plot LOS and Readmit Index with Variance — los_ra_index_plt","text":".data data supplied los_ra_index_summary_tbl()","code":""},{"path":"https://www.spsanderson.com/healthyR/reference/los_ra_index_plt.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Plot LOS and Readmit Index with Variance — los_ra_index_plt","text":"patchwork ggplot2 plot","code":""},{"path":"https://www.spsanderson.com/healthyR/reference/los_ra_index_plt.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Plot LOS and Readmit Index with Variance — los_ra_index_plt","text":"Expects tibble Expects Length Stay Readmit column, must numeric Uses cowplot stack plots","code":""},{"path":"https://www.spsanderson.com/healthyR/reference/los_ra_index_plt.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Plot LOS and Readmit Index with Variance — los_ra_index_plt","text":"Steven P. Sanderson II, MPH","code":""},{"path":"https://www.spsanderson.com/healthyR/reference/los_ra_index_plt.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Plot LOS and Readmit Index with Variance — los_ra_index_plt","text":"","code":"suppressPackageStartupMessages(library(dplyr)) data_tbl <- tibble( \"alos\" = runif(186, 1, 20) , \"elos\" = runif(186, 1, 17) , \"readmit_rate\" = runif(186, 0, .25) , \"readmit_rate_bench\" = runif(186, 0, .2) ) los_ra_index_summary_tbl( .data = data_tbl , .max_los = 15 , .alos_col = alos , .elos_col = elos , .readmit_rate = readmit_rate , .readmit_bench = readmit_rate_bench ) %>% los_ra_index_plt() los_ra_index_summary_tbl( .data = data_tbl , .max_los = 10 , .alos_col = alos , .elos_col = elos , .readmit_rate = readmit_rate , .readmit_bench = readmit_rate_bench ) %>% los_ra_index_plt()"},{"path":"https://www.spsanderson.com/healthyR/reference/los_ra_index_summary_tbl.html","id":null,"dir":"Reference","previous_headings":"","what":"Make LOS and Readmit Index Summary Tibble — los_ra_index_summary_tbl","title":"Make LOS and Readmit Index Summary Tibble — los_ra_index_summary_tbl","text":"Create length stay readmit index summary tibble","code":""},{"path":"https://www.spsanderson.com/healthyR/reference/los_ra_index_summary_tbl.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Make LOS and Readmit Index Summary Tibble — los_ra_index_summary_tbl","text":"","code":"los_ra_index_summary_tbl( .data, .max_los = 15, .alos_col, .elos_col, .readmit_rate, .readmit_bench )"},{"path":"https://www.spsanderson.com/healthyR/reference/los_ra_index_summary_tbl.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Make LOS and Readmit Index Summary Tibble — los_ra_index_summary_tbl","text":".data data going analyze. .max_los can give maximum LOS value. Lets say typically see los 15 days, set .max_los 15 values greater .max_los grouped .max_los .alos_col Average Length Stay column .elos_col Expected Length Stay column .readmit_rate Actual Readmit Rate column .readmit_bench Expected Readmit Rate column","code":""},{"path":"https://www.spsanderson.com/healthyR/reference/los_ra_index_summary_tbl.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Make LOS and Readmit Index Summary Tibble — los_ra_index_summary_tbl","text":"tibble","code":""},{"path":"https://www.spsanderson.com/healthyR/reference/los_ra_index_summary_tbl.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Make LOS and Readmit Index Summary Tibble — los_ra_index_summary_tbl","text":"Expects tibble Expects following columns 4 Length Stay Actual - integer Length Stacy Benchmark - integer Readmit Rate Actual - 0/1 record, 1 = readmitted, 0 . Readmit Rate Benchmark - percentage benchmark file. add column called visits count records per length stay 1 .max_los .max_los param can left blank function default 15. good default know set 75 percentile stats::quantile() function using defaults, like .max_los = stats::quantile(data_tbl$alos)[[4]] Uses data compute variance, want particular time frame filter data goes .data argument. suggested use timetk::filter_by_time() index computed excess length stay readmit rates respective expectations.","code":""},{"path":"https://www.spsanderson.com/healthyR/reference/los_ra_index_summary_tbl.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Make LOS and Readmit Index Summary Tibble — los_ra_index_summary_tbl","text":"Steven P. Sanderson II, MPH","code":""},{"path":"https://www.spsanderson.com/healthyR/reference/los_ra_index_summary_tbl.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Make LOS and Readmit Index Summary Tibble — los_ra_index_summary_tbl","text":"","code":"suppressPackageStartupMessages(library(dplyr)) data_tbl <- tibble( \"alos\" = runif(186, 1, 20) , \"elos\" = runif(186, 1, 17) , \"readmit_rate\" = runif(186, 0, .25) , \"readmit_bench\" = runif(186, 0, .2) ) los_ra_index_summary_tbl( .data = data_tbl , .max_los = 15 , .alos_col = alos , .elos_col = elos , .readmit_rate = readmit_rate , .readmit_bench = readmit_bench ) #> # A tibble: 15 × 4 #> los_group los_index rar_index los_ra_var #> #> 1 1 0.0957 1.08 0.981 #> 2 2 0.247 2 1.75 #> 3 3 0.357 1.09 0.734 #> 4 4 0.456 1.88 1.42 #> 5 5 0.431 1.56 1.12 #> 6 6 0.533 1.11 0.578 #> 7 7 0.620 1.07 0.451 #> 8 8 0.906 0.75 0.344 #> 9 9 0.834 1.8 0.966 #> 10 10 1.23 1.09 0.325 #> 11 11 1.18 1.4 0.581 #> 12 12 1.13 1.1 0.233 #> 13 13 1.31 1.6 0.908 #> 14 14 1.73 1.5 1.23 #> 15 15 1.86 1.22 1.08 los_ra_index_summary_tbl( .data = data_tbl , .max_los = 10 , .alos_col = alos , .elos_col = elos , .readmit_rate = readmit_rate , .readmit_bench = readmit_bench ) #> # A tibble: 10 × 4 #> los_group los_index rar_index los_ra_var #> #> 1 1 0.0957 1.08 0.981 #> 2 2 0.247 2 1.75 #> 3 3 0.357 1.09 0.734 #> 4 4 0.456 1.88 1.42 #> 5 5 0.431 1.56 1.12 #> 6 6 0.533 1.11 0.578 #> 7 7 0.620 1.07 0.451 #> 8 8 0.906 0.75 0.344 #> 9 9 0.834 1.8 0.966 #> 10 10 1.56 1.2 0.763"},{"path":"https://www.spsanderson.com/healthyR/reference/named_item_list.html","id":null,"dir":"Reference","previous_headings":"","what":"Tibble to named list — named_item_list","title":"Tibble to named list — named_item_list","text":"Takes data.frame/tibble creates named list supplied grouping variable. Can used conjunction save_to_excel() create new sheet group data.","code":""},{"path":"https://www.spsanderson.com/healthyR/reference/named_item_list.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Tibble to named list — named_item_list","text":"","code":"named_item_list(.data, .group_col)"},{"path":"https://www.spsanderson.com/healthyR/reference/named_item_list.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Tibble to named list — named_item_list","text":".data data.frame/tibble. .group_col column contains groupings.","code":""},{"path":"https://www.spsanderson.com/healthyR/reference/named_item_list.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Tibble to named list — named_item_list","text":"Requires data.frame/tibble grouping column.","code":""},{"path":"https://www.spsanderson.com/healthyR/reference/named_item_list.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Tibble to named list — named_item_list","text":"Steven P. Sanderson II, MPH","code":""},{"path":"https://www.spsanderson.com/healthyR/reference/named_item_list.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Tibble to named list — named_item_list","text":"","code":"library(healthyR.data) df <- healthyR_data df_list <- named_item_list(.data = df, .group_col = service_line) df_list #> tbl_df< #> mrn : character #> visit_id : character #> visit_start_date_time : datetime #> visit_end_date_time : datetime #> total_charge_amount : double #> total_amount_due : double #> total_adjustment_amount : double #> payer_grouping : character #> total_payment_amount : double #> ip_op_flag : character #> service_line : character #> length_of_stay : double #> expected_length_of_stay : logical #> length_of_stay_threshold: logical #> los_outlier_flag : double #> readmit_flag : double #> readmit_expectation : logical #> > #> >[29]> #> $`Alcohol Abuse` #> # A tibble: 1,904 × 17 #> mrn visit_id visit_start_date_time visit_end_date_time total_charge_amount #> #> 1 66681… 1027422… 2011-09-18 18:45:00 2011-09-21 15:24:00 20650. #> 2 85712… 1715006… 2011-09-24 14:23:00 2011-09-27 22:54:00 19632. #> 3 45086… 1463793… 2011-09-25 17:22:00 2011-09-30 18:48:00 27028. #> 4 53136… 1087046… 2011-10-01 08:58:00 2011-10-03 11:10:00 12214. #> 5 79908… 1933551… 2011-10-02 00:04:00 2011-10-06 13:51:00 30124. #> 6 29323… 1651882… 2011-10-06 15:08:00 2011-10-07 16:00:00 8571. #> 7 97809… 1375044… 2011-10-06 17:23:00 2011-10-08 14:19:00 13139. #> 8 88765… 1040286… 2011-10-09 00:36:00 2011-10-09 15:29:00 37944. #> 9 13303… 1814203… 2011-09-26 20:00:00 2011-10-10 11:59:00 110695. #> 10 50646… 1177441… 2011-10-11 01:00:00 2011-10-13 10:16:00 18791. #> # ℹ 1,894 more rows #> # ℹ 12 more variables: total_amount_due , total_adjustment_amount , #> # payer_grouping , total_payment_amount , ip_op_flag , #> # service_line , length_of_stay , expected_length_of_stay , #> # length_of_stay_threshold , los_outlier_flag , readmit_flag , #> # readmit_expectation #> #> $`Bariatric Surgery For Obesity` #> # A tibble: 309 × 17 #> mrn visit_id visit_start_date_time visit_end_date_time total_charge_amount #>
vignettes/getting-started.Rmd
getting-started.Rmd
As we can see, this function has the ability to return either a +
As we can see, this function has the ability to return either a static plot or and interactive plot. Under the hood it is using the timetk::plot_time_series function. You can find out more on the the timetk function here.
timetk::plot_time_series
Sanderson S (2023). healthyR: Hospital Data Analysis Workflow Tools. -R package version 0.2.1, https://github.com/spsanderson/healthyR. +R package version 0.2.1.9000, https://github.com/spsanderson/healthyR.
@Manual{, title = {healthyR: Hospital Data Analysis Workflow Tools}, author = {Steven Sanderson}, year = {2023}, - note = {R package version 0.2.1}, + note = {R package version 0.2.1.9000}, url = {https://github.com/spsanderson/healthyR}, }
CRAN release: 2023-04-06
healthyR.ai
suppressPackageStartupMessages(library(ggplot2)) -#> Warning: package 'ggplot2' was built under R version 4.2.2 data("mtcars") mtcars$car_name <- rownames(mtcars) diff --git a/docs/reference/diverging_lollipop_plt.html b/docs/reference/diverging_lollipop_plt.html index a0d3687..f85eece 100644 --- a/docs/reference/diverging_lollipop_plt.html +++ b/docs/reference/diverging_lollipop_plt.html @@ -18,7 +18,7 @@ healthyR - 0.2.1 + 0.2.1.9000 diff --git a/docs/reference/dx_cc_mapping.html b/docs/reference/dx_cc_mapping.html index 2c720d1..c4e3743 100644 --- a/docs/reference/dx_cc_mapping.html +++ b/docs/reference/dx_cc_mapping.html @@ -12,7 +12,7 @@ healthyR - 0.2.1 + 0.2.1.9000 diff --git a/docs/reference/gartner_magic_chart_plt-1.png b/docs/reference/gartner_magic_chart_plt-1.png index c20d643..e954887 100644 Binary files a/docs/reference/gartner_magic_chart_plt-1.png and b/docs/reference/gartner_magic_chart_plt-1.png differ diff --git a/docs/reference/gartner_magic_chart_plt-2.png b/docs/reference/gartner_magic_chart_plt-2.png index 3fb533c..8062520 100644 Binary files a/docs/reference/gartner_magic_chart_plt-2.png and b/docs/reference/gartner_magic_chart_plt-2.png differ diff --git a/docs/reference/gartner_magic_chart_plt.html b/docs/reference/gartner_magic_chart_plt.html index 1c5e962..537031e 100644 --- a/docs/reference/gartner_magic_chart_plt.html +++ b/docs/reference/gartner_magic_chart_plt.html @@ -10,7 +10,7 @@ healthyR - 0.2.1 + 0.2.1.9000 diff --git a/docs/reference/hr_scale_color_colorblind.html b/docs/reference/hr_scale_color_colorblind.html index 832cc4b..267e666 100644 --- a/docs/reference/hr_scale_color_colorblind.html +++ b/docs/reference/hr_scale_color_colorblind.html @@ -10,7 +10,7 @@ healthyR - 0.2.1 + 0.2.1.9000 diff --git a/docs/reference/hr_scale_fill_colorblind.html b/docs/reference/hr_scale_fill_colorblind.html index 7fee9d3..f5aafda 100644 --- a/docs/reference/hr_scale_fill_colorblind.html +++ b/docs/reference/hr_scale_fill_colorblind.html @@ -10,7 +10,7 @@ healthyR - 0.2.1 + 0.2.1.9000 diff --git a/docs/reference/index.html b/docs/reference/index.html index d6b31c6..b05cff2 100644 --- a/docs/reference/index.html +++ b/docs/reference/index.html @@ -10,7 +10,7 @@ healthyR - 0.2.1 + 0.2.1.9000 diff --git a/docs/reference/los_ra_index_plt-1.png b/docs/reference/los_ra_index_plt-1.png index 2125bbf..3aa1dc0 100644 Binary files a/docs/reference/los_ra_index_plt-1.png and b/docs/reference/los_ra_index_plt-1.png differ diff --git a/docs/reference/los_ra_index_plt-2.png b/docs/reference/los_ra_index_plt-2.png index dc8d78c..3f5c124 100644 Binary files a/docs/reference/los_ra_index_plt-2.png and b/docs/reference/los_ra_index_plt-2.png differ diff --git a/docs/reference/los_ra_index_plt.html b/docs/reference/los_ra_index_plt.html index 7438ef8..e80f060 100644 --- a/docs/reference/los_ra_index_plt.html +++ b/docs/reference/los_ra_index_plt.html @@ -12,7 +12,7 @@ healthyR - 0.2.1 + 0.2.1.9000 diff --git a/docs/reference/los_ra_index_summary_tbl.html b/docs/reference/los_ra_index_summary_tbl.html index e6689f8..04681b8 100644 --- a/docs/reference/los_ra_index_summary_tbl.html +++ b/docs/reference/los_ra_index_summary_tbl.html @@ -10,7 +10,7 @@ healthyR - 0.2.1 + 0.2.1.9000 @@ -156,21 +156,21 @@ Examples#> # A tibble: 15 × 4 #> los_group los_index rar_index los_ra_var #> <dbl> <dbl> <dbl> <dbl> -#> 1 1 0.0957 1.08 0.981 -#> 2 2 0.247 2 1.75 -#> 3 3 0.357 1.09 0.734 -#> 4 4 0.456 1.88 1.42 -#> 5 5 0.431 1.56 1.12 -#> 6 6 0.533 1.11 0.578 -#> 7 7 0.620 1.07 0.451 -#> 8 8 0.906 0.75 0.344 -#> 9 9 0.834 1.8 0.966 -#> 10 10 1.23 1.09 0.325 -#> 11 11 1.18 1.4 0.581 -#> 12 12 1.13 1.1 0.233 -#> 13 13 1.31 1.6 0.908 -#> 14 14 1.73 1.5 1.23 -#> 15 15 1.86 1.22 1.08 +#> 1 1 0.0814 1.25 1.17 +#> 2 2 0.191 0.9 0.909 +#> 3 3 0.286 1.22 0.936 +#> 4 4 0.369 1.88 1.51 +#> 5 5 0.611 1.62 1.01 +#> 6 6 0.815 1.09 0.276 +#> 7 7 0.747 1.25 0.503 +#> 8 8 0.768 1.2 0.432 +#> 9 9 1.23 1.44 0.676 +#> 10 10 1.07 1.15 0.226 +#> 11 11 1.47 1.75 1.22 +#> 12 12 1.27 1.22 0.490 +#> 13 13 1.35 0.909 0.445 +#> 14 14 2.21 0.933 1.28 +#> 15 15 1.99 1.3 1.29 los_ra_index_summary_tbl( .data = data_tbl @@ -183,16 +183,16 @@ Examples#> # A tibble: 10 × 4 #> los_group los_index rar_index los_ra_var #> <dbl> <dbl> <dbl> <dbl> -#> 1 1 0.0957 1.08 0.981 -#> 2 2 0.247 2 1.75 -#> 3 3 0.357 1.09 0.734 -#> 4 4 0.456 1.88 1.42 -#> 5 5 0.431 1.56 1.12 -#> 6 6 0.533 1.11 0.578 -#> 7 7 0.620 1.07 0.451 -#> 8 8 0.906 0.75 0.344 -#> 9 9 0.834 1.8 0.966 -#> 10 10 1.56 1.2 0.763 +#> 1 1 0.0814 1.25 1.17 +#> 2 2 0.191 0.9 0.909 +#> 3 3 0.286 1.22 0.936 +#> 4 4 0.369 1.88 1.51 +#> 5 5 0.611 1.62 1.01 +#> 6 6 0.815 1.09 0.276 +#> 7 7 0.747 1.25 0.503 +#> 8 8 0.768 1.2 0.432 +#> 9 9 1.23 1.44 0.676 +#> 10 10 1.71 1.3 1.01