- ask SC team as last meeting they said to look at extract date - these dont rel...

[github-actions]

e.g. extract date later than period

Record date is the last day of the quarter

qtr_start is the first day of the quarter

replace social_care_id with latest

drop variables not needed

https://api.github.com/Public-Health-Scotland/source-linkage-files/blob/4ec0aadae5af0569118cd5ed123254dd64cad31c/_SPSS_archived/All_years/04-Social_Care/03-Alarms_Telecare_data.R#L49

#####################################################
# Social Care Alarms Telecare Data
# Author: Jennifer Thom
# Date: April 2022
# Written on RStudio Server
# Version of R - 3.6.1
# Input - Data from Social care database DVPROD
# Description - Get Alarms Telecare data
#####################################################

## load packages ##

library(dplyr)
library(lubridate)


# Set up------------------------------------------------------------------

source("All_years/04-Social_Care/00-Social_Care_functions.R")


# Read Demographic file----------------------------------------------------

sc_demographics <- haven::read_sav(fs::path(
  social_care_dir,
  paste0("sc_demographics_lookup_", latest_update()),
  ext = "zsav"
))

# Query to database -------------------------------------------------------

# set-up connection to platform
db_connection <- phs_db_connection(dsn = "DVPROD")

# read in data - social care 2 demographic
at_full_data <- tbl(
  db_connection,
  dbplyr::in_schema("social_care_2", "equipment_snapshot")
) %>%
  select(
    sending_location,
    social_care_id,
    period,
    service_type,
    service_start_date,
    service_end_date
  ) %>%
  # fix bad period (2017, 2020 & 2021)
  # TODO - ask SC team as last meeting they said to look at extract date - these dont relate.
  # e.g. extract date later than period
  mutate(
    period = if_else(period == "2017", "2017Q4", period),
    period = if_else(period == "2020", "2020Q4", period),
    period = if_else(period == "2021", "2021Q4", period)
  ) %>%
  # order
  arrange(sending_location, social_care_id) %>%
  collect()


# Data Cleaning-----------------------------------------------------

# Work out the dates for each period
# Record date is the last day of the quarter
# qtr_start is the first day of the quarter
pre_compute_record_dates <- at_full_data %>%
  distinct(period) %>%
  mutate(
    record_date = yq(period) %m+% period(6, "months") %m-% days(1),
    qtr_start = yq(period) %m+% period(3, "months")
  )

replaced_start_dates <- at_full_data %>%
  # Replace missing start dates with the start of the FY
  left_join(pre_compute_record_dates, by = "period") %>%
  dplyr::mutate(
    start_date_missing = is.na(service_start_date),
    service_start_date = if_else(
      start_date_missing,
      start_fy(year = substr(period, 1, 4), format = "alternate"),
      service_start_date
    )
  )

at_full_clean <- replaced_start_dates %>%
  # Match on demographics data (CHI, gender, DoB and postcode)
  left_join(sc_demographics, by = c("sending_location", "social_care_id")) %>%
  # rename for matching source variables
  rename(
    record_keydate1 = service_start_date,
    record_keydate2 = service_end_date
  ) %>%
  # Include source variables
  mutate(
    recid = "AT",
    smrtype = case_when(
      service_type == 1 ~ "AT-Alarm",
      service_type == 2 ~ "AT-Tele"
    ),
    # Create person id variable
    person_id = glue::glue("{sending_location}-{social_care_id}"),
    # Use function for creating sc send lca variables
    sc_send_lca = convert_sc_sl_to_lca(sending_location)
  ) %>%
  # when multiple social_care_id from sending_location for single CHI
  # replace social_care_id with latest
  group_by(sending_location, chi) %>%
  mutate(latest_sc_id = last(social_care_id)) %>%
  # count changed social_care_id
  mutate(
    changed_sc_id = !is.na(chi) & social_care_id != latest_sc_id,
    social_care_id = if_else(changed_sc_id, latest_sc_id, social_care_id)
  ) %>%
  ungroup()

# Deal with episodes which have a package across quarters.
qtr_merge <- at_full_clean %>%
  # Use lazy_dt() for faster running of code
  dtplyr::lazy_dt() %>%
  group_by(
    sending_location,
    social_care_id,
    record_keydate1,
    smrtype,
    period
  ) %>%
  # Create a count for the package number across episodes
  mutate(
    pkg_count = row_number()
  ) %>%
  # Sort prior to merging
  arrange(.by_group = TRUE) %>%
  # group for merging episodes
  group_by(
    sending_location,
    social_care_id,
    record_keydate1,
    smrtype,
    pkg_count
  ) %>%
  # merge episodes with packages across quarters
  # drop variables not needed
  summarise(
    sending_location = last(sending_location),
    social_care_id = last(social_care_id),
    sc_latest_submission = last(period),
    record_keydate1 = last(record_keydate1),
    record_keydate2 = last(record_keydate2),
    smrtype = last(smrtype),
    pkg_count = last(pkg_count),
    chi = last(chi),
    gender = last(gender),
    dob = last(dob),
    postcode = last(postcode),
    recid = last(recid),
    person_id = last(person_id),
    sc_send_lca = last(sc_send_lca)
  ) %>%
  # sort after merging
  arrange(
    sending_location,
    social_care_id,
    record_keydate1,
    smrtype,
    sc_latest_submission
  ) %>%
  # end of lazy_dt()
  as_tibble() %>%
  # Sort for running SPSS
  arrange(
    sending_location,
    social_care_id
  )


# Save outfile------------------------------------------------

qtr_merge %>%
  # save rds file
  readr::write_rds(fs::path(social_care_dir, stringr::str_glue("all_at_episodes_{latest_update()}.rds")),
    compress = "xz"
  ) %>%
  # save sav file
  haven::write_sav(fs::path(social_care_dir, stringr::str_glue("all_at_episodes_{latest_update()}.zsav")),
    compress = "zsav"
  )

[Jennit07]

I think this is fine and the todo comment can just be removed!

[SwiftySalmon]

remove to do label