replace by_x() calls with utilization(type = x)

R/compare.R

@@ -1,4 +1,4 @@
-#' Compare Yearly Provider Data To State And National Averages
+#' Compare Providers to State and National Benchmarks
 #'
 #' @description
 #' + `compare_hcpcs()` allows the user to compare a provider's yearly HCPCS
@@ -12,34 +12,38 @@
 #' + `compare_hcpcs()`
 #' + `compare_conditions()`
 #' @examplesIf interactive()
-#' compare_hcpcs(by_service(year = 2018, npi = 1023076643))
-#' compare_conditions(by_provider(year = 2018, npi = 1023076643))
+#' compare_hcpcs(utilization(year = 2018, type = "service", npi = 1023076643))
+#' compare_conditions(utilization(year = 2018, type = "provider", npi = 1023076643))
 #'
-#' compare_hcpcs(map_dfr(pop_years(), ~by_service(year = .x, npi = 1023076643)))
-#' compare_conditions(map_dfr(pop_years(), ~by_provider(year = .x, npi = 1023076643)))
+#' compare_hcpcs(map_dfr(
+#' util_years(), ~utilization(year = .x, npi = 1023076643, type = "service")))
+#'
+#' compare_conditions(map_dfr(
+#' util_years(), ~utilization(year = .x, npi = 1023076643, type = "provider")))
 #' @name compare
 NULL
 
-#' @param serv_tbl < *tbl_df* > // **required** [tibble][tibble::tibble-package] returned from [by_service()]
+#' @param df < *tbl_df* > // **required** [tibble][tibble::tibble-package]
+#' returned from `utilization(type = "service")`
 #' @rdname compare
 #' @autoglobal
 #' @export
-compare_hcpcs <- function(serv_tbl) {
+compare_hcpcs <- function(df) {
 
-  if (!inherits(serv_tbl, "provider_by_serv")) {
+  if (!inherits(df, "utilization_service")) {
     cli::cli_abort(c(
-      "{.var serv_tbl} must be of class {.cls 'provider_by_serv'}.",
-      "x" = "{.var serv_tbl} is of class {.cls {class(serv_tbl)}}."))
+      "{.var df} must be of class {.cls 'utilization_service'}.",
+      "x" = "{.var df} is of class {.cls {class(df)}}."))
   }
 
-  x <- serv_tbl |>
+  x <- df |>
     dplyr::select(year, state, hcpcs_code, pos) |>
     dplyr::rowwise() |>
     dplyr::mutate(state = list(by_geography(year, state, hcpcs_code, pos)),
                   national = list(by_geography(year, state = "National", hcpcs_code, pos)), .keep = "none")
 
   results <- vctrs::vec_rbind(
-    dplyr::rename(serv_tbl,
+    dplyr::rename(df,
                   beneficiaries = tot_benes,
                   services = tot_srvcs) |>
       hcpcs_cols(),
@@ -83,20 +87,21 @@ hcpcs_cols <- function(df) {
   df |> dplyr::select(dplyr::any_of(cols))
 }
 
-#' @param prov_tbl < *tbl_df* > // **required** [tibble][tibble::tibble-package] returned from [by_provider()]
-#' @param pivot < *boolean* > // __default:__ `TRUE` Pivot output
+#' @param df < *tbl_df* > // **required** [tibble][tibble::tibble-package]
+#' returned from `utilization(type = "provider")`
+#' @param pivot < *boolean* > // __default:__ `FALSE` Pivot output
 #' @rdname compare
 #' @autoglobal
 #' @export
-compare_conditions <- function(prov_tbl, pivot = TRUE) {
+compare_conditions <- function(df, pivot = FALSE) {
 
-  if (!inherits(prov_tbl, "provider_by_prov")) {
+  if (!inherits(df, "utilization_provider")) {
     cli::cli_abort(c(
-      "{.var prov_tbl} must be of class {.cls 'provider_by_prov'}.",
-      "x" = "{.var prov_tbl} is of class {.cls {class(prov_tbl)}}."))
+      "{.var df} must be of class {.cls 'utilization_provider'}.",
+      "x" = "{.var df} is of class {.cls {class(df)}}."))
   }
 
-  p <- dplyr::select(prov_tbl, year, conditions) |>
+  p <- dplyr::select(df, year, conditions) |>
     tidyr::unnest(conditions) |>
     dplyr::mutate(level = "Provider", .after = year) |>
     dplyr::rename(

R/globals.R

@@ -61,6 +61,7 @@ utils::globalVariables(c(
   "level", # <compare_conditions>
   "prevalence", # <compare_conditions>
   "condition", # <compare_conditions>
+  "prov_tbl", # <compare_conditions>
   "state", # <compare_conditions>
   "sublevel", # <compare_conditions>
   "state.abb", # <conditions>
@@ -251,8 +252,8 @@ utils::globalVariables(c(
   "specialty", # <tidyup.provider>
   "medical", # <tidyup.provider>
   "drug", # <tidyup.provider>
-  "address", # <tidyup.services>
-  "specialty", # <tidyup.services>
+  "address", # <tidyup.service>
+  "specialty", # <tidyup.service>
   "place_of_srvc", # <tidyup.geography>
   "title", # <cms_update>
   "modified", # <cms_update>

R/ndc.R

@@ -49,8 +49,8 @@
 #' @examples
 #' ndc_lookup("0002-1433-80")
 #'
+#' @examplesIf interactive()
 #' medline("0002-1433-80")
-#'
 #' rxnorm("0002-1433-80")
 #'
 #' @param ndc < *character* > // **required** 10- to 11-digit National Drug Code

R/quality_payment.R

@@ -157,7 +157,7 @@ quality_payment <- function(year,
         top <- results |>
           dplyr::select(-c(dplyr::contains("measure_"),
                            dplyr::contains("ind_"))) |>
-          dplyr::mutate(npi_id = dplyr:consecutive_id(npi), .after = npi)
+          dplyr::mutate(npi_id = dplyr::consecutive_id(npi), .after = npi)
 
         measures <- dplyr::select(results, year, npi,
                                   dplyr::contains("measure_")) |>

R/utilization.R

@@ -8,15 +8,15 @@
 #' beneficiaries by physicians and other healthcare professionals; aggregated
 #' by provider, service and geography.
 #'
-#' @section `"provider"`:
+#' @section `type = "provider"`:
 #'
 #' The **Provider** dataset allows the user access to data such as
 #' services and procedures performed; charges submitted and payment received;
 #' and beneficiary demographic and health characteristics for providers
 #' treating Original Medicare (fee-for-service) Part B beneficiaries,
 #' aggregated by year.
 #'
-#' @section `"services"`:
+#' @section `type = "service"`:
 #'
 #' The **Provider and Service** dataset is aggregated by:
 #'
@@ -30,7 +30,7 @@
 #' because separate fee schedules apply depending on whether the place
 #' of service submitted on the claim is facility or non-facility.
 #'
-#' @section `"geography"`:
+#' @section `type = "geography"`:
 #'
 #' The **Geography and Service** dataset contains information on utilization,
 #' allowed amount, Medicare payment, and submitted charges organized nationally
@@ -46,17 +46,17 @@
 #' @examplesIf interactive()
 #' utilization(year = 2020, type = "provider", npi = 1003000423)
 #'
-#' utilization(year = 2019, type = "services", npi = 1003000126)
+#' utilization(year = 2019, type = "service", npi = 1003000126)
 #'
 #' utilization(year = 2020, type = "geography", hcpcs_code = "0002A")
 #'
 #' # Use the years helper function to retrieve results for every year:
-#' pop_years() |>
+#' util_years() |>
 #' map(\(x) utilization(year = x, type = "provider", npi = 1043477615)) |>
 #' list_rbind()
 #'
 #' @param year < *integer* > // **required** Year data was reported, in `YYYY`
-#' format. Run [pop_years()] to return a vector of the years currently available.
+#' format. Run [util_years()] to return a vector of the years currently available.
 #' @param type < *character* > // **required** dataset to query, `"provider"`, `"service"`, `"geography"`
 #' @param npi < *integer* > 10-digit national provider identifier
 #' @param first,last,organization < *character* > Individual/Organizational
@@ -125,7 +125,7 @@ utilization <- function(year,
 
   rlang::check_required(year)
   year <- as.character(year)
-  year <- rlang::arg_match(year, as.character(pop_years()))
+  year <- rlang::arg_match(year, as.character(util_years()))
   type <- rlang::arg_match(type, c("provider", "service", "geography"))
 
   if (type != "provider") {
@@ -198,7 +198,7 @@ utilization <- function(year,
     "Rndrng_Prvdr_Geo_Cd",            fips)
 
   if (type == "provider")   yr <- api_years("prv")
-  if (type == "services")   yr <- api_years("srv")
+  if (type == "service")    yr <- api_years("srv")
   if (type == "geography")  yr <- api_years("geo")
 
   id <- dplyr::filter(yr, year == {{ year }}) |> dplyr::pull(distro)
@@ -243,7 +243,7 @@ utilization <- function(year,
   if (tidy) {
     results$year <- year
     if (type == "provider")  results <- tidyup.provider(results, nest = nest, detailed = detailed)
-    if (type == "services")  results <- tidyup.services(results, rbcs = rbcs)
+    if (type == "service")   results <- tidyup.service(results, rbcs = rbcs)
     if (type == "geography") results <- tidyup.geography(results, rbcs = rbcs)
     if (na.rm)               results <- narm(results)
   }
@@ -292,11 +292,11 @@ tidyup.provider <- function(results, nest, detailed) {
   return(results)
 }
 
-#' @param results data frame from [utilization(type = "services")]
+#' @param results data frame from [utilization(type = "service")]
 #' @param rbcs < *boolean* > Add Restructured BETOS Classifications to HCPCS codes
 #' @autoglobal
 #' @noRd
-tidyup.services <- function(results, rbcs) {
+tidyup.service <- function(results, rbcs) {
 
   results$level <- "Provider"
 
@@ -306,12 +306,12 @@ tidyup.services <- function(results, rbcs) {
                     dbl = "avg_",
                     yr = 'year') |>
     combine(address, c('rndrng_prvdr_st1', 'rndrng_prvdr_st2')) |>
-    cols_util("services") |>
+    cols_util("service") |>
     dplyr::mutate(specialty = correct_specialty(specialty))
 
   if (rbcs) results <- rbcs_util(results)
 
-  class(results) <- c("utilization_services", class(results))
+  class(results) <- c("utilization_service", class(results))
   return(results)
 }
 
@@ -336,12 +336,12 @@ tidyup.geography <- function(results, rbcs) {
 }
 
 #' @param df data frame
-#' @param type 'provider', 'services', 'geography' or 'rbcs'
+#' @param type 'provider', 'service', 'geography' or 'rbcs'
 #' @autoglobal
 #' @noRd
 cols_util <- function(df,
                       type = c("provider",
-                               "services",
+                               "service",
                                "geography",
                                "rbcs")) {
 
@@ -365,7 +365,7 @@ cols_util <- function(df,
               "avg_std_pymt" = "avg_mdcr_stdzd_amt")
   }
 
-  if (type == "services") {
+  if (type == "service") {
 
     cols <- c('year',
               'npi'          = 'rndrng_npi',