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andrewallenbruce · Oct 10, 2023 · 19354c4 · 19354c4
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Showing 27 changed files with 507 additions and 565 deletions.
diff --git a/R/affiliations.R b/R/affiliations.R
@@ -13,8 +13,8 @@
 #'
 #' *Update Frequency:* **Monthly**
 #'
-#' @param npi < *integer | character* > 10-digit Individual National Provider Identifier
-#' @param pac < *integer | character* > 10-digit Individual PECOS Associate Control ID
+#' @param npi < *integer* > 10-digit Individual National Provider Identifier
+#' @param pac < *integer* > 10-digit Individual PECOS Associate Control ID
 #' @param first,middle,last < *character* > Individual Provider's name
 #' @param facility_type < *character* >
 #' + `"Hospital"` or `"hp"`
@@ -25,7 +25,7 @@
 #' + `"Skilled nursing facility"` or `"snf"`
 #' + `"Hospice"` or `"hs"`
 #' + `"Dialysis facility"` or `"df"`
-#' @param facility_ccn < *integer | character* > 6-digit CMS Certification Number of
+#' @param facility_ccn < *character* > 6-digit CMS Certification Number of
 #' facility or unit within hospital where an individual provider provides service
 #' @param parent_ccn < *integer* > 6-digit CMS Certification Number of a sub-unit's
 #' primary hospital, should the provider provide services in said unit

diff --git a/R/beneficiaries.R b/R/beneficiaries.R
@@ -48,13 +48,13 @@
 #' beneficiaries(level = "State", fips = "10")
 #' @autoglobal
 #' @export
-beneficiaries <- function(year        = NULL,
-                          period      = NULL,
-                          level       = NULL,
-                          state       = NULL,
-                          county      = NULL,
-                          fips        = NULL,
-                          tidy        = TRUE) {
+beneficiaries <- function(year = NULL,
+                          period = NULL,
+                          level = NULL,
+                          state = NULL,
+                          county = NULL,
+                          fips = NULL,
+                          tidy = TRUE) {
 
   if (!is.null(year)) {
     year <- as.character(year)
@@ -115,7 +115,6 @@ beneficiaries <- function(year        = NULL,
       tidyr::unnest(cols = c(y))
 
     format_cli(cli_args)
-
     return(invisible(NULL))
 
   }
@@ -125,8 +124,6 @@ beneficiaries <- function(year        = NULL,
   if (tidy) {
     results <- tidyup(results) |>
       dplyr::mutate(year = as.integer(year),
-                    bene_fips_cd = stringr::str_squish(bene_fips_cd),
-                    dplyr::across(dplyr::where(is.character), na_star),
                     dplyr::across(dplyr::contains("_benes"), as.integer)) |>
       bene_cols()
 
@@ -169,6 +166,6 @@ bene_cols <- function(df) {
             "bene_rx_lis_part"  = "prscrptn_drug_partial_lis_benes",
             "bene_rx_lis_no"    = "prscrptn_drug_no_lis_benes")
 
-  df |> dplyr::select(dplyr::all_of(cols))
+  df |> dplyr::select(dplyr::any_of(cols))
 
 }
diff --git a/R/betos.R b/R/betos.R
@@ -1,19 +1,21 @@
-#' Restructured BETOS Classification System
+#' Restructured BETOS Classification for HCPCS
 #'
 #' @description
+#' `r lifecycle::badge("experimental")`
 #'
-#' `betos_classification()` allows the user to group HCPCS codes into clinically
-#' meaningful categories based on the original Berenson-Eggers Type of Service
-#' (BETOS) classification. Users may use the RBCS to analyze trends and perform
-#' other types of health services analytic work.
+#' [betos()] allows the user to group HCPCS codes into clinically
+#' meaningful categories based on the original _Berenson-Eggers Type of Service_
+#' (BETOS) classification.
+#'
+#' @section From BETOS to RBCS:
 #'
-#' ## BETOS
 #' The Restructured BETOS Classification System (RBCS) is a taxonomy that allows
-#' researchers to group healthcare service codes for Medicare Part B services
-#' into clinically meaningful categories and subcategories. It is based on the
-#' original Berenson-Eggers Type of Service (BETOS) classification created in
-#' the 1980s, and includes notable updates such as Part B non-physician services.
-#' The RBCS will undergo annual updates by a technical expert panel of
+#' researchers to group Medicare Part B healthcare service codes into clinically
+#' meaningful categories and subcategories.
+#'
+#' Based on the original Berenson-Eggers Type of Service (BETOS) classification
+#' created in the 1980s, it includes notable updates such as Part B non-physician
+#' services and undergoes annual updates by a technical expert panel of
 #' researchers and clinicians.
 #'
 #' The general framework for grouping service codes into the new RBCS taxonomy
@@ -26,8 +28,8 @@
 #'
 #' Links:
 #'
-#' - [Restructured BETOS Classification System](https://data.cms.gov/provider-summary-by-type-of-service/provider-service-classifications/restructured-betos-classification-system)
-#' - [Restructured BETOS Classification System Data Dictionary](https://data.cms.gov/resources/restructured-betos-classification-system-data-dictionary)
+#' + [Restructured BETOS Classification System](https://data.cms.gov/provider-summary-by-type-of-service/provider-service-classifications/restructured-betos-classification-system)
+#' + [RBCS Data Dictionary](https://data.cms.gov/resources/restructured-betos-classification-system-data-dictionary)
 #'
 #' *Update Frequency:* **Annually**
 #'
@@ -37,7 +39,7 @@
 #' @param family < *character* > RBCS Family Description
 #' @param procedure < *character* > Whether the HCPCS code is a Major (`"M"`),
 #' Other (`"O"`), or Non-Procedure code (`"N"`).
-#' @param tidy < *boolean* > Tidy output; default is `TRUE`
+#' @param tidy < *boolean* > // __default:__ `TRUE` Tidy output
 #'
 #' @return A [tibble][tibble::tibble-package] with the columns:
 #'
@@ -99,10 +101,8 @@ betos <- function(hcpcs_code = NULL,
   results <- httr2::resp_body_json(response, simplifyVector = TRUE)
 
   if (tidy) {
-    results <- tidyup(results) |>
-      dplyr::mutate(dplyr::across(dplyr::everything(), stringr::str_squish),
-                    dplyr::across(dplyr::contains("dt"), anytime::anydate),
-                    rbcs_major_ind = dplyr::case_match(rbcs_major_ind,
+    results <- tidyup(results, dt = c("dt")) |>
+      dplyr::mutate(rbcs_major_ind = dplyr::case_match(rbcs_major_ind,
                                                        "N" ~ "Non-procedure",
                                                        "M" ~ "Major",
                                                        "O" ~ "Other")) |>
@@ -130,6 +130,6 @@ betos_cols <- function(df) {
             'rbcs_start_date' = 'rbcs_assignment_eff_dt',
             'rbcs_end_date' = 'rbcs_assignment_end_dt')
 
-  df |> dplyr::select(dplyr::all_of(cols))
+  df |> dplyr::select(dplyr::any_of(cols))
 
 }
diff --git a/R/clinicians.R b/R/clinicians.R
@@ -11,8 +11,8 @@
 #'
 #' *Update Frequency:* **Monthly**
 #'
-#' @param npi < *integer|character* > 10-digit Individual National Provider Identifier
-#' @param pac < *integer|character* > 10-digit Individual PECOS Associate Control ID
+#' @param npi < *integer* > 10-digit Individual National Provider Identifier
+#' @param pac < *integer* > 10-digit Individual PECOS Associate Control ID
 #' @param enid < *character* > 15-digit Individual Medicare Enrollment ID
 #' @param first,middle,last < *character* > Individual provider's name
 #' @param gender < *character* > Individual provider's gender; `"F"` (Female)
@@ -23,7 +23,7 @@
 #' specialty reported in the selected enrollment
 #' @param facility_name < *character* > Name of facility associated with the
 #' individual provider
-#' @param pac_org < *integer|character* > 10-digit Organizational PECOS
+#' @param pac_org < *integer* > 10-digit Organizational PECOS
 #' Associate Control ID
 #' @param city < *character* > Provider's city
 #' @param state < *character* > Provider's state
@@ -64,7 +64,7 @@
 #' @seealso [hospitals()], [providers()], [affiliations()]
 #'
 #' @examplesIf interactive()
-#' clinicians(enroll_id_ind = "I20081002000549")
+#' clinicians(enid = "I20081002000549")
 #' clinicians(school = "NEW YORK UNIVERSITY SCHOOL OF MEDICINE")
 #' @autoglobal
 #' @export
@@ -152,13 +152,10 @@ clinicians <- function(npi = NULL,
   }
 
   if (tidy) {
-    results <- tidyup(results) |>
-      tidyr::unite("address_org", adr_ln_1:adr_ln_2,
-                   remove = TRUE, na.rm = TRUE, sep = " ") |>
-      dplyr::mutate(address_org = stringr::str_squish(address_org),
-                    num_org_mem = as.integer(num_org_mem),
-                    grd_yr = as.integer(grd_yr),
-                    telehlth = yn_logical(telehlth)) |>
+    results <- tidyup(results, yn = c("telehlth")) |>
+      address(c("adr_ln_1", "adr_ln_2")) |>
+      dplyr::mutate(num_org_mem = as.integer(num_org_mem),
+                    grd_yr = as.integer(grd_yr)) |>
       clin_cols()
     if (na.rm) {
       results <- janitor::remove_empty(results, which = c("rows", "cols"))}
@@ -187,7 +184,7 @@ clin_cols <- function(df) {
             'organization' = 'facility_name',
             'pac_org' = 'org_pac_id',
             'members_org' = 'num_org_mem',
-            'address_org',
+            'address_org' = 'address',
             # 'address_id' = 'adrs_id',
             'city_org' = 'city_town',
             'state_org' = 'state',

diff --git a/R/hospitals.R b/R/hospitals.R
@@ -3,7 +3,6 @@
 #' @description
 #' `r lifecycle::badge("experimental")`
 #'
-#'
 #' [hospitals()] allows the user to search for information on all hospitals
 #' currently enrolled in Medicare. Data returned includes the hospital's
 #' sub-group types, legal business name, doing-business-as name, organization
@@ -13,11 +12,11 @@
 #'
 #' *Update Frequency:* **Monthly**
 #'
-#' @param npi < *integer | character* > 10-digit Organizational National Provider Identifier
-#' @param pac_org < *integer | character* > 10-digit Organizational PECOS Associate Control ID
+#' @param npi < *integer* > 10-digit Organizational National Provider Identifier
+#' @param pac_org < *integer* > 10-digit Organizational PECOS Associate Control ID
 #' @param enid_org < *character* > 15-digit Organizational Medicare Enrollment ID
 #' @param enid_state < *character* > Hospital’s enrollment state
-#' @param facility_ccn < *integer | character* > 6-digit CMS Certification Number
+#' @param facility_ccn < *integer* > 6-digit CMS Certification Number
 #' @param specialty_code < *character* > Medicare Part A Provider specialty code:
 #' + `"00-00"`: Religious Non-Medical Healthcare Institution (RNHCI)
 #' + `"00-01"`: Community Mental Health Center
@@ -40,7 +39,7 @@
 #' @param dba < *character* > Hospital’s doing-business-as name
 #' @param city < *character* > City of the hospital’s practice location
 #' @param state < *character* > State of the hospital’s practice location
-#' @param zip < *integer | character* > Zip code of the hospital’s practice location
+#' @param zip < *integer* > Zip code of the hospital’s practice location
 #' @param registration < *character* > Hospital's IRS designation:
 #' + `"P"`: Registered as __Proprietor__
 #' + `"N"`: Registered as __Non-Profit__
@@ -101,6 +100,7 @@
 #' hospitals(pac_org = 6103733050)
 #'
 #' hospitals(state = "GA", reh = TRUE)
+#'
 #' @autoglobal
 #' @export
 hospitals <- function(npi = NULL,
@@ -235,16 +235,11 @@ hospitals <- function(npi = NULL,
   results <- httr2::resp_body_json(response, simplifyVector = TRUE)
 
   if (tidy) {
-    results <- tidyup(results) |>
-      dplyr::mutate(dplyr::across(dplyr::contains(c("flag", "subgroup")), yn_logical),
-                    dplyr::across(dplyr::contains("date"), anytime::anydate),
-                    dplyr::across(dplyr::contains("location"), stringr::str_squish),
-                    proprietary_nonprofit = dplyr::case_match(proprietary_nonprofit,
+    results <- tidyup(results, yn = c("flag", "subgroup")) |>
+      dplyr::mutate(roprietary_nonprofit = dplyr::case_match(proprietary_nonprofit,
                       "P" ~ "Proprietary", "N" ~ "Non-Profit", .default = NA),
                     zip_code = as.character(zip_code)) |>
-      tidyr::unite("address",
-                   address_line_1:address_line_2,
-                   remove = TRUE, na.rm = TRUE) |>
+      address(c("address_line_1", "address_line_2")) |>
       tidyr::unite("structure",
                    organization_type_structure:organization_other_type_text,
                    remove = TRUE, na.rm = TRUE, sep = ": ") |>