Removed dependency on dint (#35)

DESCRIPTION

@@ -18,7 +18,6 @@ Depends:
 Imports: 
     anytime,
     cli,
-    dint,
     dplyr,
     furrr,
     httr2,

R/beneficiaries.R

@@ -147,7 +147,7 @@ beneficiaries <- function(year = NULL,
 
   results <- httr2::resp_body_json(response, simplifyVector = TRUE)
 
-  if (tidy) results <- cols_bene(tidyup(results, int = c("year", "_benes"), yr = 'year'))
+  if (tidy) results <- cols_bene(tidyup(results, int = c('year', '_benes')))
   if (!is.null(period) && period == "Month") results <- dplyr::filter(results, period %in% month.name)
 
   return(results)

R/conditions.R

@@ -207,8 +207,7 @@ conditions <- function(year,
     results$year <- year
     results <- cols_cc(tidyup(results,
                               int = "year",
-                              dbl = c("prvlnc", "_pc", "er_"),
-                              yr = "year"))
+                              dbl = c("prvlnc", "_pc", "er_")))
   if (na.rm) {results <- narm(results)}}
   return(results)
 }

R/open_payments.R

@@ -240,8 +240,7 @@ open_payments <- function(year,
     results <- tidyup(results,
                       # yn = c(yncols),
                       dbl = "dollars",
-                      int = "program_year",
-                      yr  = "program_year") |>
+                      int = "program_year") |>
       dplyr::mutate(change_type                            = changed_logical(change_type),
                     covered_recipient_type                 = covered_recipient(covered_recipient_type),
                     nature_of_payment_or_transfer_of_value = nature(nature_of_payment_or_transfer_of_value)) |>

R/outpatient.R

@@ -96,8 +96,7 @@ outpatient <- function(year,
                       dbl = c('avg_tot_sbmtd_chrgs',
                               'avg_mdcr_alowd_amt',
                               'avg_mdcr_pymt_amt',
-                              'avg_mdcr_outlier_amt'),
-                      yr = 'year') |>
+                              'avg_mdcr_outlier_amt')) |>
       cols_out()
     if (na.rm) results <- narm(results)
   }

R/quality_payment.R

@@ -141,8 +141,8 @@ quality_payment <- function(year,
                       "pi_bonus",
                       "extreme_hardship_ia",
                       "ia_study",
-                      "extreme_hardship_cost"),
-               yr = 'year') |> cols_qpp("tidy")
+                      "extreme_hardship_cost")) |>
+      cols_qpp("tidy")
 
       if (nest) {
         pcol <- list(q = c('quality_measure_id_', 'quality_measure_score_') %s+% rep(1:10, each = 2),

R/utilization.R

@@ -278,8 +278,7 @@ tidyup_provider <- function(results, nest, detailed) {
            int  = c("year", "_hcpcs", "bene", "_srvcs"),
            dbl  = c("pay", "pymt", "charges", "allowed", "cc_", "hcc"),
            cred = "credential",
-           ent  = "entity_type",
-           yr   = 'year') |>
+           ent  = "entity_type") |>
     combine(address, c('rndrng_prvdr_st1', 'rndrng_prvdr_st2')) |>
     dplyr::mutate(specialty       = correct_specialty(specialty),
                   .copay_deduct   = tot_allowed - tot_payment,
@@ -318,8 +317,7 @@ tidyup_service <- function(results, rbcs) {
   results <- tidyup(results,
                     yn      = "_ind",
                     int     = c("year", "tot_"),
-                    dbl     = "avg_",
-                    yr      = 'year') |>
+                    dbl     = "avg_") |>
     combine(address, c('rndrng_prvdr_st1', 'rndrng_prvdr_st2')) |>
     cols_util("service") |>
     dplyr::mutate(specialty = correct_specialty(specialty))
@@ -339,8 +337,7 @@ tidyup_geography <- function(results, rbcs) {
   results <- tidyup(results,
                     yn           = "_ind",
                     int          = c("year", "tot_"),
-                    dbl          = "avg_",
-                    yr           = 'year') |>
+                    dbl          = "avg_") |>
     dplyr::mutate(place_of_srvc  = pos_char(place_of_srvc)) |>
     cols_util("geography")
 

R/utils.R

@@ -159,7 +159,6 @@ df2chr <- function(df) {
 #' @param up cols to convert to upper case
 #' @param cred cols to remove periods from
 #' @param ent cols to convert to NPI entity type
-#' @param yr cols to convert to year with [dint::as_date_y()]
 #' @returns tidy data frame
 #' @autoglobal
 #' @export
@@ -172,8 +171,7 @@ tidyup <- function(df,
                    chr = NULL,
                    up = NULL,
                    cred = NULL,
-                   ent = NULL,
-                   yr = NULL) {
+                   ent = NULL) {
 
   x <- janitor::clean_names(df) |>
     dplyr::tibble() |>
@@ -191,7 +189,6 @@ tidyup <- function(df,
   if (!is.null(up))    {x <- dplyr::mutate(x, dplyr::across(dplyr::contains(up), toupper))}
   if (!is.null(cred))  {x <- dplyr::mutate(x, dplyr::across(dplyr::contains(cred), clean_credentials))}
   if (!is.null(ent))   {x <- dplyr::mutate(x, dplyr::across(dplyr::contains(ent), entype_char))}
-  if (!is.null(yr))    {x <- dplyr::mutate(x, dplyr::across(dplyr::any_of(yr), dint::as_date_y))}
   return(x)
 }
 

tests/testthat/test-utils.R

@@ -53,7 +53,6 @@ test_that("display_long() works", {
 test_that("tidyup() works", {
   df <- dplyr::tibble(
     name = "John Doe ",
-    year = "1981",
     date = "1981/03/07",
     int = "123456789",
     dbl = "12.34",
@@ -68,7 +67,6 @@ test_that("tidyup() works", {
 
   tidy <- dplyr::tibble(
     name = "JOHN DOE",
-    year = dint::as_date_y(1981),
     date = anytime::anydate("1981/03/07"),
     int = 123456789,
     dbl = 12.34,
@@ -82,8 +80,8 @@ test_that("tidyup() works", {
     dash = NA_character_)
 
   expect_equal(tidyup(df, yn = "yn", int = c("int", "year"),
-                      dbl = "dbl", yr = "year", up = "name",
-                      cred = "cred", ent = c("ind", "org")), tidy)
+                      dbl = "dbl", up = "name", cred = "cred",
+                      ent = c("ind", "org")), tidy)
 })
 
 test_that("combine() works", {