Merge branch 'development' into refactor-ae

Author: lizihao-anu

R/add_hri_variables.R

@@ -78,7 +78,7 @@ add_hri_variables <- function(
   hri_lookup <- data %>%
     dplyr::select(
       "year",
-      chi_variable,
+      dplyr::all_of(chi_variable),
       "postcode",
       "gpprac",
       "lca",
@@ -134,7 +134,7 @@ add_hri_variables <- function(
     ) %>%
     # Select only required variables for the lookup
     dplyr::select(
-      chi_variable,
+      dplyr::all_of(chi_variable),
       "hri_scot",
       "hri_scotp",
       "hri_hb",

R/add_keep_population_flag.R

@@ -17,10 +17,10 @@ add_keep_population_flag <- function(individual_file, year) {
     pop_estimates <-
       readr::read_rds(get_pop_path(type = "datazone")) %>%
       dplyr::select(
-        .data$year,
-        .data$datazone2011,
-        .data$sex,
-        .data$age0:.data$age90plus
+        "year",
+        "datazone2011",
+        "sex",
+        "age0":"age90plus"
       )
 
     # Step 1: Obtain the population estimates for Locality, AgeGroup, and Gender
@@ -56,7 +56,7 @@ add_keep_population_flag <- function(individual_file, year) {
       add_age_group(.data$age) %>%
       dplyr::left_join(
         readr::read_rds(get_locality_path()) %>%
-          dplyr::select("locality" = "hscp_locality", .data$datazone2011),
+          dplyr::select("locality" = "hscp_locality", "datazone2011"),
         by = "datazone2011"
       ) %>%
       dplyr::group_by(.data$locality, .data$age_group, .data$gender) %>%
@@ -110,7 +110,7 @@ add_keep_population_flag <- function(individual_file, year) {
       ) %>%
       dplyr::filter(.data$keep_nsu == 1L) %>%
       dplyr::ungroup() %>%
-      dplyr::select(-.data$flag_to_remove)
+      dplyr::select(-"flag_to_remove")
 
     # step 3: match the flag back onto the slf
     individual_file <- individual_file %>%

R/add_smrtype.R

@@ -157,7 +157,7 @@ add_smrtype <- function(recid,
       recid == "HL1" & main_applicant_flag == "N" ~ "HL1-Other"
     )
   } else if (all(recid == "OoH")) {
-    smrtype <- dplyr::case_match(
+    smrtype <- dplyr::recode_values(
       consultation_type,
       "DISTRICT NURSE" ~ "OOH-DN",
       "DOCTOR ADVICE/NURSE ADVICE" ~ "OOH-Advice",
@@ -167,11 +167,11 @@ add_smrtype <- function(recid,
       "COVID19 ASSESSMENT" ~ "OOH-C19Ass",
       "COVID19 ADVICE" ~ "OOH-C19Adv",
       "COVID19 OTHER" ~ "OOH-C19Oth",
-      .default = "OOH-Other"
+      default = "OOH-Other"
     )
   } else {
     # Recids that can be recoded with no identifier
-    smrtype <- dplyr::case_match(
+    smrtype <- dplyr::recode_values(
       recid,
       "00B" ~ "Outpatient",
       "04B" ~ "Psych-IP",

R/calculate_measures.R

@@ -84,7 +84,7 @@ calculate_measures <- function(
     if (group_by == "recid") {
       pivot_data <- data %>%
         tidyr::pivot_longer(
-          cols = !.data$recid,
+          cols = !"recid",
           names_to = "measure",
           values_to = "value"
         )

R/convert_ca_to_lca.R

@@ -14,7 +14,7 @@
 #' @family code functions
 #' @seealso convert_sc_sending_location_to_lca
 convert_ca_to_lca <- function(ca_var) {
-  lca <- dplyr::case_match(
+  lca <- dplyr::recode_values(
     ca_var,
     c("S12000033", "Aberdeen City") ~ "01",
     c("S12000034", "Aberdeenshire") ~ "02",

R/convert_codes_to_name.R

@@ -14,7 +14,7 @@
 #'
 #' @family code functions
 convert_hscp_to_hscpnames <- function(hscp) {
-  hscpnames <- dplyr::case_match(
+  hscpnames <- dplyr::recode_values(
     hscp,
     "S37000001" ~ "Aberdeen City",
     "S37000002" ~ "Aberdeenshire",
@@ -67,7 +67,7 @@ convert_hscp_to_hscpnames <- function(hscp) {
 #'
 #' @family code functions
 convert_hb_to_hbnames <- function(hb) {
-  hbnames <- dplyr::case_match(
+  hbnames <- dplyr::recode_values(
     hb,
     "S08000015" ~ "Ayrshire and Arran",
     "S08000016" ~ "Borders",

R/convert_monthly_rows_to_vars.R

@@ -17,16 +17,16 @@ convert_monthly_rows_to_vars <- function(data,
   month_order <- tolower(month.abb[c(4L:12L, 1L:3L)])
 
   data %>%
-    dplyr::mutate(month_name = month_order[{{ month_num_var }}]) %>%
-    dplyr::select(-{{ month_num_var }}) %>%
+    dplyr::mutate(month_name = month_order[.data[[month_num_var]]]) %>%
+    dplyr::select(-dplyr::all_of(month_num_var)) %>%
     dplyr::rename(
-      cost = {{ cost_var }},
-      beddays = {{ beddays_var }}
+      cost = dplyr::all_of(cost_var),
+      beddays = dplyr::all_of(beddays_var)
     ) %>%
     tidyr::pivot_wider(
-      names_from = .data$month_name,
+      names_from = "month_name",
       names_glue = "{month_name}_{.value}",
-      values_from = c(.data$cost, .data$beddays),
+      values_from = c("cost", "beddays"),
       values_fill = 0.00
     ) %>%
     dplyr::select(

R/convert_sc_sending_location_to_lca.R

@@ -16,7 +16,7 @@
 #'
 #' @seealso convert_ca_to_lca
 convert_sc_sending_location_to_lca <- function(sending_location) {
-  lca <- dplyr::case_match(
+  lca <- dplyr::recode_values(
     sending_location,
     100L ~ "01", # Aberdeen City
     110L ~ "02", # Aberdeenshire
@@ -50,7 +50,7 @@ convert_sc_sending_location_to_lca <- function(sending_location) {
     390L ~ "30", # Stirling
     400L ~ "31", # West Lothian
     235L ~ "32", # Na_h_Eileanan_Siar
-    .default = NA_character_
+    default = NA_character_
   )
 
   return(lca)

R/create_demographic_lookup.R

@@ -169,7 +169,7 @@ create_demographic_cohorts <- function(
       TRUE ~ "Healthy and Low User"
     )) %>%
     # Reorder variables
-    dplyr::relocate(.data$demographic_cohort, .after = .data$anon_chi)
+    dplyr::relocate("demographic_cohort", .after = "anon_chi")
 
   # Write to disk
   if (write_to_disk) {

R/create_episode_file.R

@@ -435,17 +435,17 @@ correct_cij_vars <- function(data) {
       ),
       cij_pattype_code = dplyr::if_else(
         !is.na(.data$anon_chi) & .data$recid %in% c("01B", "04B", "GLS", "02B"),
-        dplyr::case_match(
+        dplyr::recode_values(
           .data$cij_admtype,
           c("41", "42") ~ 2L,
           c("40", "48", "99") ~ 9L,
           "18" ~ 0L,
-          .default = as.integer(.data$cij_pattype_code)
+          default = as.integer(.data$cij_pattype_code)
         ),
         .data$cij_pattype_code
       ),
       # Recode cij_pattype based on above
-      cij_pattype = dplyr::case_match(
+      cij_pattype = dplyr::recode_values(
         .data$cij_pattype_code,
         0L ~ "Non-Elective",
         1L ~ "Elective",