Updates to SC client

R/process_extract_alarms_telecare.R

@@ -27,7 +27,7 @@ process_extract_alarms_telecare <- function(
   }
 
   # Now select episodes for given FY
-  at_data <- data %>%
+  at_processed <- data %>%
     dplyr::filter(is_date_in_fyyear(
       year,
       .data[["record_keydate1"]],
@@ -52,21 +52,15 @@ process_extract_alarms_telecare <- function(
       "record_keydate1",
       "record_keydate2",
       "person_id",
-      "sc_latest_submission",
-      "sc_living_alone",
-      "sc_support_from_unpaid_carer",
-      "sc_social_worker",
-      "sc_type_of_housing",
-      "sc_meals",
-      "sc_day_care"
+      tidyselect::starts_with("sc_")
     )
 
   if (write_to_disk) {
-    at_data %>%
-      write_file(
-        get_source_extract_path(year, type = "AT", check_mode = "write")
-      )
+    write_file(
+      at_processed,
+      get_source_extract_path(year, type = "AT", check_mode = "write")
+    )
   }
 
-  return(at_data)
+  return(at_processed)
 }

R/process_extract_home_care.R

@@ -34,7 +34,10 @@ process_extract_home_care <- function(
     # remove any episodes where the latest submission was before the current year
     dplyr::filter(substr(.data$sc_latest_submission, 1, 4) >= convert_fyyear_to_year(year)) %>%
     # Match to client data
-    dplyr::left_join(client_lookup, by = c("sending_location", "social_care_id")) %>%
+    dplyr::left_join(
+      client_lookup,
+      by = c("sending_location", "social_care_id")
+    ) %>%
     dplyr::mutate(year = year)
 
   # Home Care Hours ---------------------------------------

R/process_extract_sds.R

@@ -26,14 +26,17 @@ process_extract_sds <- function(
     return(tibble::tibble())
   }
 
-  outfile <- data %>%
+  sds_processed <- data %>%
     # Select episodes for given FY
     dplyr::filter(is_date_in_fyyear(
       year,
       .data[["record_keydate1"]],
       .data[["record_keydate2"]]
     )) %>%
-    dplyr::left_join(client_lookup, by = c("sending_location", "social_care_id")) %>%
+    dplyr::left_join(
+      client_lookup,
+      by = c("sending_location", "social_care_id")
+    ) %>%
     dplyr::mutate(
       year = year
     ) %>%
@@ -48,18 +51,15 @@ process_extract_sds <- function(
       "record_keydate1",
       "record_keydate2",
       "sc_send_lca",
-      "sc_living_alone",
-      "sc_support_from_unpaid_carer",
-      "sc_social_worker",
-      "sc_type_of_housing",
-      "sc_meals",
-      "sc_day_care"
+      tidyselect::starts_with("sc_")
     )
 
   if (write_to_disk) {
-    outfile %>%
-      write_file(get_source_extract_path(year, type = "SDS", check_mode = "write"))
+    write_file(
+      sds_processed,
+      get_source_extract_path(year, type = "SDS", check_mode = "write")
+    )
   }
 
-  return(outfile)
+  return(sds_processed)
 }

R/process_lookup_sc_client.R

@@ -65,7 +65,7 @@ process_lookup_sc_client <- function(data, year, write_to_disk = TRUE) {
       ),
       type_of_housing = tidyr::replace_na(.data$type_of_housing, 6L)
     ) %>%
-    # factor labels
+    # Create factors with labels
     dplyr::mutate(
       dplyr::across(
         c(
@@ -82,9 +82,11 @@ process_lookup_sc_client <- function(data, year, write_to_disk = TRUE) {
           "autism",
           "other_vulnerable_groups"
         ),
-        factor,
-        levels = c(0L, 1L),
-        labels = c("No", "Yes")
+        ~ factor(
+          .x,
+          levels = c(0L, 1L),
+          labels = c("No", "Yes")
+        )
       ),
       dplyr::across(
         c(
@@ -94,41 +96,26 @@ process_lookup_sc_client <- function(data, year, write_to_disk = TRUE) {
           "meals",
           "day_care"
         ),
-        factor,
-        levels = c(0L, 1L, 9L),
-        labels = c("No", "Yes", "Not Known")
+        ~ factor(
+          .x,
+          levels = c(0L, 1L, 9L),
+          labels = c("No", "Yes", "Not Known")
+        )
       ),
-      type_of_housing = factor(.data$type_of_housing,
-        levels = 1L:6L
+      type_of_housing = factor(
+        .data$type_of_housing,
+        levels = c(1L:6L, 8L, 9L)
       )
     ) %>%
-    # rename variables
     dplyr::rename_with(
       .cols = -c("sending_location", "social_care_id"),
       .fn = ~ paste0("sc_", .x)
     )
 
-
-  ## save outfile ---------------------------------------
-  outfile <-
-    client_clean %>%
-    # reorder
-    dplyr::select(
-      "sending_location",
-      "social_care_id",
-      "sc_living_alone",
-      "sc_support_from_unpaid_carer",
-      "sc_social_worker",
-      "sc_type_of_housing",
-      "sc_meals",
-      "sc_day_care"
-    )
-
   if (write_to_disk) {
-    # Save .rds file
-    outfile %>%
+    client_clean %>%
       write_file(get_source_extract_path(year, "Client", check_mode = "write"))
   }
 
-  return(outfile)
+  return(client_clean)
 }

R/read_lookup_sc_client.R

@@ -9,12 +9,17 @@
 #' @return the final data as a [tibble][tibble::tibble-package].
 #' @export
 #' @family process extracts
-read_lookup_sc_client <- function(sc_dvprod_connection = phs_db_connection(dsn = "DVPROD"), fyyear) {
+read_lookup_sc_client <- function(
+    sc_dvprod_connection = phs_db_connection(dsn = "DVPROD"),
+    fyyear) {
   check_year_format(fyyear)
   year <- convert_fyyear_to_year(fyyear)
 
   # read in data - social care 2 client
-  client_data <- dplyr::tbl(sc_dvprod_connection, dbplyr::in_schema("social_care_2", "client")) %>%
+  client_query <- dplyr::tbl(
+    sc_dvprod_connection,
+    dbplyr::in_schema("social_care_2", "client")
+  ) %>%
     dplyr::select(
       "sending_location",
       "social_care_id",
@@ -67,14 +72,7 @@ read_lookup_sc_client <- function(sc_dvprod_connection = phs_db_connection(dsn =
         ),
         as.integer
       )
-    ) %>%
-    dplyr::arrange(
-      .data$sending_location,
-      .data$social_care_id,
-      .data$financial_year,
-      .data$financial_quarter
-    ) %>%
-    dplyr::collect()
+    )
 
-  return(client_data)
+  return(dplyr::collect(client_query))
 }