Change depreciation process scripts

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/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_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(-all_of(month_num_var)) %>%
     dplyr::rename(
-      cost = {{ cost_var }},
-      beddays = {{ beddays_var }}
+      cost = all_of(cost_var),
+      beddays = 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/fill_ch_names.R

@@ -84,7 +84,7 @@ fill_ch_names <- function(ch_data,
       ch_name_validated = clean_up_free_text(.data[["ch_name_validated"]]),
       ch_date_registered = lubridate::as_date(.data[["ch_date_registered"]]),
       ch_date_cancelled = lubridate::as_date(.data[["ch_date_cancelled"]]),
-      ch_active = dplyr::case_match(
+      ch_active = dplyr::recode_values(
         .data[["ch_active"]],
         "Active" ~ TRUE,
         c("Cancelled", "Inactive") ~ FALSE
@@ -106,7 +106,7 @@ fill_ch_names <- function(ch_data,
       ch_active = any(.data[["ch_active"]])
     ) %>%
     dplyr::ungroup() %>%
-    dplyr::rename(ch_postcode_lookup = .data[["ch_postcode"]]) %>%
+    dplyr::rename(ch_postcode_lookup = all_of("ch_postcode")) %>%
     dplyr::mutate(
       ch_pc_partial = stringr::str_sub(.data[["ch_postcode_lookup"]], 1, -2),
       ch_pc_partial2 = stringr::str_sub(.data[["ch_postcode_lookup"]], 1, -3),

R/get_la_code_opendata_lookup.R

@@ -14,11 +14,11 @@ get_la_code_opendata_lookup <- function() {
   ) %>%
     dplyr::distinct() %>%
     dplyr::mutate(
-      sending_local_authority_name = dplyr::case_match(
+      sending_local_authority_name = dplyr::recode_values(
         .data$CAName,
         "City of Edinburgh" ~ "Edinburgh",
         "Na h-Eileanan Siar" ~ "Eilean Siar",
-        .default = .data$CAName
+        default = .data$CAName
       ) %>%
         stringr::str_replace("\\sand\\s", " \\& ")
     )

R/get_source_extract_path.R

@@ -50,7 +50,7 @@ get_source_extract_path <- function(year,
     return(get_dummy_boxi_extract_path(BYOC_MODE = BYOC_MODE))
   }
 
-  file_name <- dplyr::case_match(
+  file_name <- dplyr::recode_values(
     type,
     "acute" ~ "anon-acute_for_source",
     "ae" ~ "anon-a_and_e_for_source",

R/process_extract_acute.R

@@ -61,7 +61,7 @@ process_extract_acute <- function(data,
     # Apply new costs for C3 specialty, these are taken from the 2017/18 file
     fix_c3_costs(year) %>%
     # initialise monthly cost/beddays variables in a separate data frame for matching
-    convert_monthly_rows_to_vars(.data$costmonthnum, .data$cost_total_net, .data$yearstay) %>%
+    convert_monthly_rows_to_vars("costmonthnum", "cost_total_net", "yearstay") %>%
     # add yearstay and cost_total_net variables
     dplyr::mutate(
       yearstay = rowSums(dplyr::pick(tidyselect::ends_with("_beddays"))),

R/process_extract_delayed_discharges.R

@@ -48,8 +48,8 @@ process_extract_delayed_discharges <- function(
 
   dd_clean <- data %>%
     dplyr::rename(
-      record_keydate1 = .data[["rdd"]],
-      record_keydate2 = .data[["delay_end_date"]]
+      record_keydate1 = all_of("rdd"),
+      record_keydate2 = all_of("delay_end_date")
     ) %>%
     # Use end of the month date for records with no end date
     # (but we think have ended)
@@ -74,7 +74,7 @@ process_extract_delayed_discharges <- function(
     ) %>%
     # recode blanks to NA
     dplyr::mutate(
-      dplyr::across(tidyselect::ends_with("_delay_reason"), dplyr::na_if, "")
+      dplyr::across(tidyselect::ends_with("_delay_reason"), \(x) dplyr::na_if(x, ""))
     ) %>%
     # create flags for no_end_date and correct_dates
     dplyr::mutate(

R/process_extract_district_nursing.R

@@ -55,13 +55,13 @@ process_extract_district_nursing <- function(
   dn_costs <- dn_clean %>%
     # Recode HB codes to HB2019 so they match the cost lookup
     dplyr::mutate(
-      hbtreatcode = dplyr::case_match(
+      hbtreatcode = dplyr::recode_values(
         .data$hbtreatcode,
         "S08000018" ~ "S08000029", # Fife 2014
         "S08000027" ~ "S08000030", # Tayside 2014
         "S08000021" ~ "S08000031", # Glasgow 2018
         "S08000023" ~ "S08000032", # Lanarkshire 2018
-        .default = .data$hbtreatcode
+        default = .data$hbtreatcode
       )
     ) %>%
     # match files with DN Cost Lookup
@@ -76,11 +76,11 @@ process_extract_district_nursing <- function(
     create_day_episode_costs(.data$record_keydate1, .data$cost_total_net) %>%
     # Return HB values to HB2018
     dplyr::mutate(
-      hbtreatcode = dplyr::case_match(
+      hbtreatcode = dplyr::recode_values(
         .data$hbtreatcode,
         "S08000031" ~ "S08000021", # Glasgow
         "S08000032" ~ "S08000023", # Lanarkshire
-        .default = .data$hbtreatcode
+        default = .data$hbtreatcode
       )
     )
 

R/process_extract_homelessness.R

@@ -156,7 +156,7 @@ process_extract_homelessness <- function(
   hl1_data <- data %>%
     dplyr::left_join(
       completeness_data %>%
-        dplyr::select(.data$sending_local_authority_name, .data$pct_complete_all),
+        dplyr::select("sending_local_authority_name", "pct_complete_all"),
       by = dplyr::join_by("sending_local_authority_name")
     ) %>%
     dplyr::rename(hl1_completeness = "pct_complete_all") %>%

R/process_extract_maternity.R

@@ -63,7 +63,7 @@ process_extract_maternity <- function(data,
         levels = c(1L:5L, 8L)
       ),
       smrtype = add_smrtype(.data$recid, .data$mpat),
-      ipdc = dplyr::case_match(
+      ipdc = dplyr::recode_values(
         .data$smrtype,
         "Matern-IP" ~ "I",
         "Matern-DC" ~ "D"

R/process_extract_mental_health.R

@@ -39,7 +39,7 @@ process_extract_mental_health <- function(data, year, write_to_disk = TRUE) {
         "NA"
       )
     ) %>%
-    dplyr::select(-.data$cij_inpatient) %>%
+    dplyr::select(-"cij_inpatient") %>%
     # cij_admtype recode unknown to 99
     dplyr::mutate(
       cij_admtype = dplyr::if_else(
@@ -50,9 +50,9 @@ process_extract_mental_health <- function(data, year, write_to_disk = TRUE) {
     ) %>%
     # monthly beddays and costs
     convert_monthly_rows_to_vars(
-      .data$costmonthnum,
-      .data$cost_total_net,
-      .data$yearstay
+      "costmonthnum",
+      "cost_total_net",
+      "yearstay"
     ) %>%
     dplyr::mutate(
       # yearstay

R/process_extract_ooh_consultations.R

@@ -49,7 +49,7 @@ process_extract_ooh_consultations <- function(data, year) {
     # change back to anon_chi
     slfhelper::get_anon_chi() %>%
     dplyr::mutate(
-      attendance_status = dplyr::case_match(
+      attendance_status = dplyr::recode_values(
         .data$attendance_status,
         "Y" ~ 1L,
         "N" ~ 8L

R/process_extract_ooh_diagnosis.R

@@ -142,8 +142,8 @@ process_extract_ooh_diagnosis <- function(data, year) {
     dplyr::rename(diag = "readcode") %>%
     # restructure data
     tidyr::pivot_wider(
-      names_from = .data$diag_n,
-      values_from = c(.data$diag, .data$description),
+      names_from = "diag_n",
+      values_from = c("diag", "description"),
       names_glue = "{.value}{diag_n}"
     ) %>%
     dplyr::select(

R/process_extract_ooh_outcomes.R

@@ -25,7 +25,7 @@ process_extract_ooh_outcomes <- function(data, year) {
     data.table::as.data.table() %>%
     # Recode outcome
     dplyr::mutate(
-      outcome = dplyr::case_match(
+      outcome = dplyr::recode_values(
         .data$outcome,
         "DEATH" ~ "00",
         "999/AMBULANCE" ~ "01",
@@ -38,7 +38,7 @@ process_extract_ooh_outcomes <- function(data, year) {
         "REFERRED TO SOCIAL SERVICES" ~ "24",
         "OTHER HC REFERRAL/ADVISED TO CONTACT OTHER HCP (NON-EMERGENCY)" ~ "29",
         "OTHER" ~ "99",
-        .default = .data$outcome
+        default = .data$outcome
       )
     ) %>%
     # Sort so we prefer 'lower' outcomes e.g. Death, over things like 'Other'
@@ -48,9 +48,9 @@ process_extract_ooh_outcomes <- function(data, year) {
     dplyr::ungroup() %>%
     # use row order to pivot outcomes
     tidyr::pivot_wider(
-      names_from = .data$outcome_n,
+      names_from = "outcome_n",
       names_prefix = "ooh_outcome",
-      values_from = .data$outcome
+      values_from = "outcome"
     ) %>%
     dplyr::select(
       "ooh_case_id",

R/process_it_chi_deaths.R

@@ -21,7 +21,7 @@ process_it_chi_deaths <- function(data, write_to_disk = TRUE) {
     dplyr::distinct(.data$anon_chi, .keep_all = TRUE) %>%
     # remove death_date_nrs as this is the nrs weekly unvalidated data and we should not use this.
     # the boxi nrs death date is more reliable as this is provided monthly and is validated.
-    dplyr::select(.data$anon_chi, .data$death_date_chi) %>%
+    dplyr::select("anon_chi", "death_date_chi") %>%
     dplyr::mutate(
       death_date_chi = lubridate::ymd(.data$death_date_chi)
     )

R/process_lookup_gpprac.R

@@ -69,11 +69,11 @@ process_lookup_gpprac <- function(
     ) %>%
     dplyr::mutate(
       lca = convert_ca_to_lca(.data$ca2018),
-      hbpraccode = dplyr::case_match(
+      hbpraccode = dplyr::recode_values(
         .data$gpprac,
         c(99942L, 99957L, 99961L, 99981L, 99999L) ~ "S08200003",
         99995L ~ "S08200001",
-        .default = .data$hbpraccode
+        default = .data$hbpraccode
       )
     )
 

R/process_lookup_homelessness.R

@@ -22,7 +22,7 @@ create_homelessness_lookup <- function(
   }
   homelessness_lookup <- homelessness_data %>%
     dplyr::distinct(.data$anon_chi, .data$record_keydate1, .data$record_keydate2) %>%
-    tidyr::drop_na(.data$anon_chi) %>%
+    tidyr::drop_na("anon_chi") %>%
     dplyr::mutate(hl1_in_fy = 1L)
 
   log_slf_event(stage = "process", status = "complete", type = "homelessness_lookup", year = year)

R/process_lookup_sc_client.R

@@ -40,7 +40,7 @@ process_lookup_sc_client <-
       replace_sc_id_with_latest() %>%
       # remove cases with no data in client
       dplyr::filter(!(is.na(.data$financial_year))) %>%
-      dplyr::select(-.data$latest_sc_id, -.data$period)
+      dplyr::select(-"latest_sc_id", -"period")
 
     client_clean <- sc_client_demographics %>%
       dplyr::group_by(.data$sending_location, .data$social_care_id, .data$anon_chi) %>%
@@ -92,7 +92,7 @@ process_lookup_sc_client <-
             "other_vulnerable_groups",
             "type_of_housing"
           ),
-          tidyr::replace_na, 9L
+          \(x) tidyr::replace_na(x, 9L)
         )
       ) %>%
       # factor labels
@@ -117,9 +117,11 @@ process_lookup_sc_client <-
             "autism",
             "other_vulnerable_groups"
           ),
-          factor,
-          levels = c(0L, 1L, 9L),
-          labels = c("No", "Yes", "Not Known")
+          \(x) factor(
+            x,
+            levels = c(0L, 1L, 9L),
+            labels = c("No", "Yes", "Not Known")
+          )
         ),
         type_of_housing = factor(.data$type_of_housing,
           levels = 1L:9L,
@@ -202,7 +204,7 @@ process_lookup_sc_client <-
       ) %>%
       dplyr::arrange(.data$anon_chi, .data$count_not_known) %>%
       dplyr::distinct(.data$anon_chi, .keep_all = TRUE) %>%
-      dplyr::select(-.data$sending_location, -.data$count_not_known)
+      dplyr::select(-"sending_location", -"count_not_known")
 
     if (write_to_disk) {
       write_file(