Refresh data 2023 2024

EDA/eda_parent_uprn_and_pat_threshold.R

@@ -9,7 +9,8 @@ con <- nhsbsaR::con_nhsbsa(database = "DALP")
 
 # Create a lazy table from year month dim table in DWCP
 data <- con %>%
-  tbl(from = in_schema("ADNSH", "INT646_BASE_20210401_20220331"))
+  tbl(from = in_schema("DALL_REF", "INT646_BASE_20200401_20230331")) %>% 
+  filter(FY == "2020/21")
 
 # Get chapter info
 chapters = con %>% 
@@ -39,7 +40,7 @@ df_std = data %>%
     NURSING_HOME_FLAG = max(NURSING_HOME_FLAG),
     RESIDENTIAL_HOME_FLAG = max(RESIDENTIAL_HOME_FLAG),
     MAX_MONTHLY_PATIENTS = max(MAX_MONTHLY_PATIENTS),
-    NUMBER_OF_BEDS = max(NUMBER_OF_BEDS),
+    #NUMBER_OF_BEDS = max(NUMBER_OF_BEDS),
     MONTHS = n_distinct(YEAR_MONTH),
     PATS = n_distinct(NHS_NO),
     ITEMS = sum(ITEM_COUNT),
@@ -65,7 +66,7 @@ df_merge = data %>%
     NURSING_HOME_FLAG = max(NURSING_HOME_FLAG),
     RESIDENTIAL_HOME_FLAG = max(RESIDENTIAL_HOME_FLAG),
     MAX_MONTHLY_PATIENTS = max(MAX_MONTHLY_PATIENTS),
-    NUMBER_OF_BEDS = max(NUMBER_OF_BEDS),
+    #NUMBER_OF_BEDS = max(NUMBER_OF_BEDS),
     MONTHS = n_distinct(YEAR_MONTH),
     PATS = n_distinct(NHS_NO),
     ITEMS = sum(ITEM_COUNT),

R/mod_06_geo_ch_flag.R

@@ -16,11 +16,12 @@ mod_06_geo_ch_flag_ui <- function(id) {
             choices = c(
               "2020/21",
               "2021/22",
-              "2022/23"
+              "2022/23",
+              "2023/24"
             ),
             selected = carehomes2::metrics_by_geo_and_ch_flag_df$FY %>%
               levels() %>%
-              max,
+              max(),
             full_width = TRUE
           )
         ),
@@ -247,7 +248,7 @@ mod_06_geo_ch_flag_server <- function(id) {
               span(
                 class = "nhsuk-body-s",
                 style = "font-size: 12px;",
-                gsub(" NCH", " ", gsub(" CH", "", col))
+                gsub("^20", "", gsub(" NCH", " ", gsub(" CH", "", col)))
               ) %>%
                 as.character()
             }
@@ -307,11 +308,11 @@ mod_06_geo_ch_flag_server <- function(id) {
           ),
           rowCallback = DT::JS(rowCallback)
         ),
-        height = "400px",
+        height = "500px",
         filter = "none",
         selection = "single"
       ) %>%
-        DT::formatStyle(columns = 1:7, `font-size` = "12px")
+        DT::formatStyle(columns = 1:9, `font-size` = "12px")
     }
 
     # Create download data (all data)

data-raw/app/00_run_all.R

@@ -1,13 +1,20 @@
+Sys.time()
+# Load library and generate base geo data
+library(tictoc)
 source("data-raw/app/data_raw_helpers.R")
 source("data-raw/app/geo_data.R")
 
-keep_vars = c(ls(), 'keep_vars', 'get_metrics')
+# Define vars to retain in workflow
+keep_vars = c(ls(), 'keep_vars')
+
+# Run all scripts that generate an Rda file
+tic(); source("data-raw/app/01_headline_figures_df.R"); toc()                   # 10 mins
+tic(); source("data-raw/app/02_patients_age_gender_df.R"); toc()
+tic(); source("data-raw/app/03_patients_by_imd_df.R"); toc()                    # 10 mins
+tic(); source("data-raw/app/04_metrics_by_ch_type_85_split_df.R"); toc()        # 3  hours
+tic(); source("data-raw/app/05_metrics_age_gender_df.R"); toc()                 # 30 mins
+tic(); source("data-raw/app/06_metrics_by_geo_and_ch_flag_df.R"); toc()         # 90 mins
+tic(); source("data-raw/app/07_ch_flag_drug_df.R"); toc()                       # 30 mins
+tic(); source("data-raw/app/08_geo_ch_flag_drug_df.R"); toc()                   # 90 mins
+Sys.time()
 
-source("data-raw/app/01_headline_figures_df.R")
-source("data-raw/app/02_patients_age_gender_df.R")
-source("data-raw/app/03_patients_by_imd_df.R")
-source("data-raw/app/04_metrics_by_ch_type_df.R")
-source("data-raw/app/05_metrics_age_gender_df.R")
-source("data-raw/app/06_metrics_by_geo_and_ch_flag_df.R")
-source("data-raw/app/07_ch_flag_drug_df.R")
-source("data-raw/app/08_geo_ch_flag_drug_df.R")

data-raw/app/01_headline_figures_df.R

@@ -8,7 +8,7 @@ con <- nhsbsaR::con_nhsbsa(database = "DALP")
 
 # Create a lazy table from year month dim table in DWCP
 data_db <- con %>%
-  tbl(from = in_schema("DALL_REF", "INT646_BASE_20200401_20230331"))
+  tbl(from = in_schema("DALL_REF", "INT646_BASE_20200401_20240331"))
 
 # Key findings used within analysis summary text
 data_db %>% 
@@ -104,4 +104,10 @@ mod_headline_figures_df = rbind(annual_df, monthly_df)
 usethis::use_data(mod_headline_figures_df, overwrite = TRUE)
 
 # Disconnect from database
-DBI::dbDisconnect(con); rm(list = ls()); gc()
+DBI::dbDisconnect(con)
+
+# Remove vars specific to script
+remove_vars <- setdiff(ls(), keep_vars)
+
+# Remove objects and clean environment
+rm(list = remove_vars, remove_vars); gc()

data-raw/app/02_patients_age_gender_df.R

@@ -1,5 +1,5 @@
-# Running time ~10 min
 
+# Running time ~10 min
 library(dplyr)
 library(dbplyr)
 devtools::load_all()
@@ -9,14 +9,13 @@ con <- nhsbsaR::con_nhsbsa(database = "DALP")
 
 # Item-level base table
 base_db <- con |>
-  tbl(from = in_schema("DALL_REF", "INT646_BASE_20200401_20230331"))
+  tbl(from = in_schema("DALL_REF", "INT646_BASE_20200401_20240331"))
 
 # Add a dummy overall column
 base_db <- base_db |>
    mutate(OVERALL = "Overall")
 
 # Loop over each geography and aggregate using purrr's map function approach
-
 patients_by_fy_geo_age_gender_fun <- function(geography_name) {
 
   # Identify geography cols
@@ -59,6 +58,7 @@ patients_by_fy_geo_age_gender_fun <- function(geography_name) {
 
 }
 
+# Map function
 patients_by_fy_geo_age_gender_df <- purrr::map(
   names(geographies),
   patients_by_fy_geo_age_gender_fun
@@ -74,6 +74,7 @@ patients_by_fy_geo_age_gender_df <-
     #PCT_PATIENTS = janitor::round_half_up(PCT_PATIENTS, 1)
   )
 
+# Calculate patient proportions
 patients_by_fy_geo_age_gender_df <- patients_by_fy_geo_age_gender_df |>
   group_by(CH_FLAG, FY, GEOGRAPHY, SUB_GEOGRAPHY_CODE, SUB_GEOGRAPHY_NAME) |>
   mutate(
@@ -121,4 +122,9 @@ usethis::use_data(
 
 # Disconnect from database
 DBI::dbDisconnect(con)
-rm(list = ls()); gc()
+
+# Remove vars specific to script
+remove_vars <- setdiff(ls(), keep_vars)
+
+# Remove objects and clean environment
+rm(list = remove_vars, remove_vars); gc()

data-raw/app/03_patients_by_imd_df.R

@@ -8,7 +8,7 @@ con <- nhsbsaR::con_nhsbsa(database = "DALP")
 
 # Create a lazy table from the item level base table
 fact_db <- con %>%
-  tbl(from = in_schema("DALL_REF", "INT646_BASE_20200401_20230331"))
+  tbl(from = in_schema("DALL_REF", "INT646_BASE_20200401_20240331"))
 
 # Count care home patients in each decile
 mod_patients_by_imd_df <- fact_db %>%
@@ -37,4 +37,10 @@ mod_patients_by_imd_df <- fact_db %>%
 usethis::use_data(mod_patients_by_imd_df, overwrite = TRUE)
 
 # Disconnect
-DBI::dbDisconnect(con); rm(list = ls()); gc()
+DBI::dbDisconnect(con)
+
+# Remove vars specific to script
+remove_vars <- setdiff(ls(), keep_vars)
+
+# Remove objects and clean environment
+rm(list = remove_vars, remove_vars); gc()

data-raw/app/04_metrics_by_ch_type_85_split_df.R

@@ -1,11 +1,9 @@
 # Initial setup -----------------------------------------------------------
 
-# Expected run time ~35 minutes @parallel 24
-
+# Expected run time ~35 minutes @parallel 36
 library(dplyr)
 library(dbplyr)
 library(tidyr)
-
 devtools::load_all()
 
 # Set up connection to DALP
@@ -17,7 +15,7 @@ con <- nhsbsaR::con_nhsbsa(database = "DALP")
 
 # Item-level base table
 base_db <- con %>%
-  tbl(from = in_schema("DALL_REF", "INT646_BASE_20200401_20230331"))
+  tbl(from = in_schema("DALL_REF", "INT646_BASE_20200401_20240331"))
 
 # Initial manipulation to create CH_TYPE column, later to be grouped by
 init_db <- base_db %>%
@@ -48,6 +46,7 @@ init_db <- base_db %>%
     )
   ) 
 
+# Union both initi_db variants
 init_db <- init_db %>% 
   union(
     init_db %>% 
@@ -57,6 +56,7 @@ init_db <- init_db %>%
 
 ## Process ----------------------------------------------------------------
 
+# Get metrics
 metrics_by_ch_type_85_split_df <- get_metrics(
   init_db,
   first_grouping = c(
@@ -73,6 +73,7 @@ metrics_by_ch_type_85_split_df <- get_metrics(
   )
 )
 
+# Generate age band categories
 metrics_by_ch_type_85_split_df <- metrics_by_ch_type_85_split_df %>% 
   mutate(
     AGE_BAND = dplyr::case_match(
@@ -84,9 +85,16 @@ metrics_by_ch_type_85_split_df <- metrics_by_ch_type_85_split_df %>%
   ) %>% 
   dplyr::relocate(AGE_BAND, .after = CH_TYPE)
 
-## Save -------------------------------------------------------------------
+## Save ------------------------------------------------------------------------
 usethis::use_data(metrics_by_ch_type_85_split_df, overwrite = TRUE)
 
-# Cleanup -----------------------------------------------------------------
+# Cleanup ----------------------------------------------------------------------
+
+# Disconnect
 DBI::dbDisconnect(con)
-rm(list = ls())
+
+# Remove vars specific to script
+remove_vars <- setdiff(ls(), keep_vars)
+
+# Remove objects and clean environment
+rm(list = remove_vars, remove_vars); gc()

data-raw/app/05_metrics_age_gender_df.R

@@ -1,5 +1,6 @@
 # Running time ~35 min
 
+# Libraries and functions
 library(dplyr)
 library(dbplyr)
 devtools::load_all()
@@ -10,10 +11,10 @@ con <- nhsbsaR::con_nhsbsa(database = "DALP")
 
 # Item-level base table
 base_db <- con |>
-  tbl(from = in_schema("DALL_REF", "INT646_BASE_20200401_20230331")) |>
+  tbl(from = in_schema("DALL_REF", "INT646_BASE_20200401_20240331")) %>% 
   filter(GENDER %in% c("Male", "Female"))
 
-
+# Get metrics
 metrics_by_age_gender_and_ch_flag_df <- get_metrics(
   base_db,
   first_grouping = c(
@@ -46,4 +47,9 @@ usethis::use_data(
 
 # Disconnect from database
 DBI::dbDisconnect(con)
-rm(list = ls()); gc()
+
+# Remove vars specific to script
+remove_vars <- setdiff(ls(), keep_vars)
+
+# Remove objects and clean environment
+rm(list = remove_vars, remove_vars); gc()