R/smr_wrangling.R
In Public-Health-Scotland/hsmr: Reproducible Analytical Pipeline of Hospital Standardised Mortality Ratios Quarterly Publication
Defines functions
smr_wrangling
Documented in
smr_wrangling
#' @title Initial Wrangling for HSMR data
#'
#' @description Creates the majority of necessary variables to be used in the
#' HSMR model.
#'
#'
#' @details \code{smr_wrangling} expects a \code{tibble} of data extracted from
#' SMR01. It also expects \code{tibbles} of data extracted from the GRO deaths
#' database and lookups for primary diagnosis, postcode (for deprivation) and
#' morbidities (comorbs_sum). This is the first step in a four-step process.
#' The next steps are \code{\link{smr_pmorbs}}, then \code{\link{smr_model}}
#' and finally \code{\link{smr_data}}.
#'
#'
#' @param smr01 Input tibble for admissions, see details.
#' @param gro Input tibble for deaths, see details.
#' @param pdiags Input tibble for primary diagnosis groupings lookup.
#' @param postcode Input tibble for deprivation lookup.
#' @param morbs Input tibble for the charlson index for comorbidities lookup.
#' @param spec Input tibble for for specialty groupings lookup.
#'
#' @importFrom magrittr %>%
#' @importFrom magrittr %<>%
#'
#' @export
smr_wrangling <- function(smr01, gro, pdiags, postcode, morbs, spec) {
### 1 - Error handling ----
if(!tibble::is_tibble(smr01) | !tibble::is_tibble(gro) |
!tibble::is_tibble(pdiags) | !tibble::is_tibble(postcode) |
!tibble::is_tibble(morbs) | !tibble::is_tibble(spec)) {
stop(paste0("All arguments provided to the function ",
"must be in tibble format. Verify whether ",
"an object is a tibble or not with ",
"the tibble::is_tibble() function"))
}
if(!all(c("link_no", "admission_date", "discharge_date", "cis_marker",
"postcode", "discharge_type", "sex", "admgrp", "ci_chi_number",
"admfgrp", "ipdc", "age_grp", "quarter", "location",
"main_condition", "other_condition_1", "other_condition_2",
"other_condition_3", "other_condition_4", "other_condition_5",
"specialty", "ipdc", "age_in_years", "hbtreat_currentdate",
"year") %in% names(smr01))) {
stop(paste0("Object smr01 does not contain the required variables. ",
"Must contain:
link_no
admission_date
discharge_date
cis_marker
postcode
discharge_type
sex
admgrp
ci_chi_number
admfgrp
ipdc
age_in_years
age_grp
quarter
year
location
hbtreat_currentdate
main_condition
other_condition_1 to other_condition_5
specialty
ipdc"))
}
if(!all(c("diag1_4", "diagnosis_group") %in% names(pdiags))){
stop(paste0("Object pdiags does not contain the required variables.",
"Must contain:
diag1_4
diagnosis_group"))
}
if(!all(c("morb", "wmorbs", "diag") %in% names(morbs))){
stop(paste0("Object morbs does not contain the required variables.",
"Must contain:
morb
wmorbs
diag"))
}
if(!all(c("link_no", "date_of_death") %in% names(gro))) {
stop(paste0("Object gro does not contain the required variables.",
"Must contain:
link_no
date_of_death"))
}
if(!is.numeric(smr01$link_no)){
stop(paste0("Link_no must be a numeric"))
}
if(!is.numeric(smr01$cis_marker)){
stop(paste0("cis_marker must be a numeric"))
}
if(!lubridate::is.POSIXct(smr01$admission_date)){
stop(paste0("Admission_date variable must be POSIXct of format",
" %Y-%m-%d"))
}
if(!lubridate::is.POSIXct(smr01$discharge_date)){
stop(paste0("Discharge_date variable must be POSIXct of format",
" %Y-%m-%d"))
}
if(!all(1:140 %in% pdiags$diagnosis_group)){
stop(paste0("Primary diagnosis lookup does not contain all 140 ",
"primary diagnosis groupings."))
}
if(!all(1:17 %in% morbs$morb)){
stop(paste0("Co-morbidities lookup does not contain all 17 comorbidity
groups."))
}
### 2 - Match deaths data to SMR01 ----
# Remove duplicate records on link_no
# The deaths file is matched on to SMR01 by link_no,
# therefore link_no needs to be unique
gro %<>%
tidylog::distinct(link_no, .keep_all = TRUE)
# Match deaths data on to SMR01 data
smr01 %<>%
tidylog::left_join(gro, by = "link_no") %>%
# Sort data by link_no, cis_marker, adm_date and dis_date as per guidance
dplyr::arrange(link_no, cis_marker, admission_date, discharge_date)
### 3 - Basic SMR01 processing ----
# Create the following variables:
# death_inhosp = 1 if the patient died in hospital during that episode of care
# dthdays = the number of days from admission till death
# death30 = 1 if the patient died within 30 days of admission date
# quarter_name = quarter name in text form
# quarter = quarter in number form (quarter 1 = Jan - Mar 2011)
# location = recodes some hospital codes for combined sites
# diag1_4 = main condition ICD-10 code to 4 digits
# diagx = ICD-10 code to 3 and 4 digits, separated by an underscore
# pdiag_grp = matches the primary diagnosis group on the 4-digit ICD-10
# code
# wcomorbsx = matches the charlson index weighting if the relevant ICD-10
# codes are present in any of the five "other diagnosis"
# positions
# comorbs_sum = sum of the wcomorbsx values across the episode
smr01 %<>%
tidylog::mutate(death_inhosp = dplyr::if_else(dplyr::between(
as.numeric(discharge_type), 40, 49),
1, 0),
dthdays = lubridate::interval(admission_date, date_of_death) /
lubridate::days(1),
death30 = dplyr::case_when(
dplyr::between(dthdays, 0, 30) ~ 1,
TRUE ~ 0),
quarter_name = paste0(year, "Q", quarter),
quarter = as.numeric(as.factor(quarter_name)),
diag1_4 = substr(main_condition, 1, 4),
diag2 = paste(substr(other_condition_1, 1, 3),
substr(other_condition_1, 1, 4),
sep = "_"),
diag3 = paste(substr(other_condition_2, 1, 3),
substr(other_condition_2, 1, 4),
sep = "_"),
diag4 = paste(substr(other_condition_3, 1, 3),
substr(other_condition_3, 1, 4),
sep = "_"),
diag5 = paste(substr(other_condition_4, 1, 3),
substr(other_condition_4, 1, 4),
sep = "_"),
diag6 = paste(substr(other_condition_5, 1, 3),
substr(other_condition_5, 1, 4),
sep = "_")) %>%
# Create the pdiag_grp and wcomorbsx variables using joins to the morbs
# dataset
tidylog::left_join(tidylog::select(pdiags,
pdiag_grp = diagnosis_group,
diag1_4),
by = "diag1_4") %>%
# Match on specialty grouping by the specialty variable
tidylog::left_join(spec, by = "specialty") %>%
# Fuzzy joins add the (in this case, not needed) joining variable by
# default, so append these with "_z" so they can be easily removed
# afterwards
fuzzyjoin::fuzzy_left_join(tidylog::select(morbs,
wcomorbs1 = wmorbs,
comorbs1 = morb,
diag2_z = diag),
by = c("diag2" = "diag2_z"),
match_fun = stringr::str_detect) %>%
fuzzyjoin::fuzzy_left_join(tidylog::select(morbs,
wcomorbs2 = wmorbs,
comorbs2 = morb,
diag3_z = diag),
by = c("diag3" = "diag3_z"),
match_fun = stringr::str_detect) %>%
fuzzyjoin::fuzzy_left_join(tidylog::select(morbs,
wcomorbs3 = wmorbs,
comorbs3 = morb,
diag4_z = diag),
by = c("diag4" = "diag4_z"),
match_fun = stringr::str_detect) %>%
fuzzyjoin::fuzzy_left_join(tidylog::select(morbs,
wcomorbs4 = wmorbs,
comorbs4 = morb,
diag5_z = diag),
by = c("diag5" = "diag5_z"),
match_fun = stringr::str_detect) %>%
fuzzyjoin::fuzzy_left_join(tidylog::select(morbs,
wcomorbs5 = wmorbs,
comorbs5 = morb,
diag6_z = diag),
by = c("diag6" = "diag6_z"),
match_fun = stringr::str_detect) %>%
# Remove joining variables
tidylog::select(-dplyr::ends_with("_z")) %>%
# Replace cases with no match with zero
tidyr::replace_na(list(wcomorbs1 = 0,
wcomorbs2 = 0,
wcomorbs3 = 0,
wcomorbs4 = 0,
wcomorbs5 = 0,
comorbs1 = 0,
comorbs2 = 0,
comorbs3 = 0,
comorbs4 = 0,
comorbs5 = 0)) %>%
# Comorbs cleaning
tidylog::mutate(wcomorbs1 = replace(wcomorbs1,
comorbs1 == 12 &
(comorbs2 != 6 & comorbs3 != 6 &
comorbs4 != 6 & comorbs5 != 6),
2),
wcomorbs2 = replace(wcomorbs2,
comorbs2 == 12 &
(comorbs1 != 6 & comorbs3 != 6 &
comorbs4 != 6 & comorbs5 != 6),
2),
wcomorbs3 = replace(wcomorbs3,
comorbs3 == 12 &
(comorbs2 != 6 & comorbs1 != 6 &
comorbs4 != 6 & comorbs5 != 6),
2),
wcomorbs4 = replace(wcomorbs4,
comorbs4 == 12 &
(comorbs2 != 6 & comorbs3 != 6 &
comorbs1 != 6 & comorbs5 != 6),
2),
wcomorbs5 = replace(wcomorbs5,
comorbs5 == 12 &
(comorbs2 != 6 & comorbs3 != 6 &
comorbs4 != 6 & comorbs1 != 6),
2)) %>%
tidylog::mutate(wcomorbs1 = replace(wcomorbs1,
comorbs1 == 11 & (comorbs2 == 15 |
comorbs3 == 15 |
comorbs4 == 15 |
comorbs5 == 15),
0),
wcomorbs2 = replace(wcomorbs2,
comorbs2 == 11 & (comorbs1 == 15 |
comorbs3 == 15 |
comorbs4 == 15 |
comorbs5 == 15),
0),
wcomorbs3 = replace(wcomorbs3,
comorbs3 == 11 & (comorbs2 == 15 |
comorbs1 == 15 |
comorbs4 == 15 |
comorbs5 == 15),
0),
wcomorbs4 = replace(wcomorbs4,
comorbs4 == 11 & (comorbs2 == 15 |
comorbs3 == 15 |
comorbs1 == 15 |
comorbs5 == 15),
0),
wcomorbs5 = replace(wcomorbs5,
comorbs5 == 11 & (comorbs2 == 15 |
comorbs3 == 15 |
comorbs4 == 15 |
comorbs1 == 15),
0)) %>%
tidylog::mutate(wcomorbs2 = replace(wcomorbs2,
comorbs2 == comorbs1,
0),
wcomorbs3 = replace(wcomorbs3,
comorbs3 == comorbs1 |
comorbs3 == comorbs2,
0),
wcomorbs4 = replace(wcomorbs4,
comorbs4 == comorbs1 |
comorbs4 == comorbs2 |
comorbs4 == comorbs3,
0),
wcomorbs5 = replace(wcomorbs5,
comorbs5 == comorbs1 |
comorbs5 == comorbs2 |
comorbs5 == comorbs3 |
comorbs5 == comorbs4,
0)) %>%
tidylog::mutate(comorbs_sum = rowSums(
tidylog::select(., dplyr::starts_with("wcomorbs")))) %>%
# Create two further variables at CIS level:
# epinum = the episode number for each individual episode within the CIS
# death_inhosp_max = 1 if the patient died in hospital during any episode
# of the CIS
tidylog::group_by(link_no, cis_marker) %>%
tidylog::mutate(epinum = dplyr::row_number(),
death_inhosp_max = max(death_inhosp)) %>%
dplyr::ungroup() %>%
# Sort data as per guidance and remove variables no longer required
dplyr::arrange(link_no, cis_marker, admission_date, discharge_date) %>%
tidylog::select(-dplyr::contains("condition"),
-dplyr::starts_with("wcomorbs"),
-comorbs1, -comorbs2, -comorbs3, -comorbs4, -comorbs5,
-quarter_name)
### 4 - SIMD ----
# Fix formatting of postcode variable
smr01 %<>%
# First remove all spaces from postcode variable
tidylog::mutate(postcode = gsub("\\s", "", postcode),
# Then add space (or spaces) at appropriate juncture
# (depending on the number of characters) to get the
# postcode into 7-character format
postcode = dplyr::case_when(
is.na(postcode)
~ NA_character_,
stringr::str_length(postcode) == 5
~ sub("(.{2})", "\\1 ", postcode),
stringr::str_length(postcode) == 6
~ sub("(.{3})", "\\1 ", postcode),
TRUE
~ postcode
)) %>%
# Join to the postcode lookup
tidylog::left_join(postcode, by = "postcode") %>%
# Assign the appropriate SIMD value to a patient depending on the year they
# were admitted
tidylog::mutate(simd = dplyr::case_when(
year >=2017 ~ simd_2020,
year >= 2014 & year < 2017 ~ simd_2016,
year < 2014 ~ simd_2012
)) %>%
# Remove the not needed year-specific SIMD variables
tidylog::select(-c(simd_2012, simd_2016, simd_2020)) %>%
tidylog::mutate(simd = dplyr::case_when(
is.na(simd) & postcode == "NK010AA" ~ 6,
is.na(simd) & postcode != "NK010AA" ~ 7,
TRUE ~ simd
))
return(smr01)
}
### END OF SCRIPT ###
Public-Health-Scotland/hsmr documentation built on June 24, 2024, 1:48 a.m.
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Defines functions smr_wrangling
Documented in smr_wrangling
#' @title Initial Wrangling for HSMR data
#'
#' @description Creates the majority of necessary variables to be used in the
#' HSMR model.
#'
#'
#' @details \code{smr_wrangling} expects a \code{tibble} of data extracted from
#' SMR01. It also expects \code{tibbles} of data extracted from the GRO deaths
#' database and lookups for primary diagnosis, postcode (for deprivation) and
#' morbidities (comorbs_sum). This is the first step in a four-step process.
#' The next steps are \code{\link{smr_pmorbs}}, then \code{\link{smr_model}}
#' and finally \code{\link{smr_data}}.
#'
#'
#' @param smr01 Input tibble for admissions, see details.
#' @param gro Input tibble for deaths, see details.
#' @param pdiags Input tibble for primary diagnosis groupings lookup.
#' @param postcode Input tibble for deprivation lookup.
#' @param morbs Input tibble for the charlson index for comorbidities lookup.
#' @param spec Input tibble for for specialty groupings lookup.
#'
#' @importFrom magrittr %>%
#' @importFrom magrittr %<>%
#'
#' @export
smr_wrangling <- function(smr01, gro, pdiags, postcode, morbs, spec) {
### 1 - Error handling ----
if(!tibble::is_tibble(smr01) | !tibble::is_tibble(gro) |
!tibble::is_tibble(pdiags) | !tibble::is_tibble(postcode) |
!tibble::is_tibble(morbs) | !tibble::is_tibble(spec)) {
stop(paste0("All arguments provided to the function ",
"must be in tibble format. Verify whether ",
"an object is a tibble or not with ",
"the tibble::is_tibble() function"))
}
if(!all(c("link_no", "admission_date", "discharge_date", "cis_marker",
"postcode", "discharge_type", "sex", "admgrp", "ci_chi_number",
"admfgrp", "ipdc", "age_grp", "quarter", "location",
"main_condition", "other_condition_1", "other_condition_2",
"other_condition_3", "other_condition_4", "other_condition_5",
"specialty", "ipdc", "age_in_years", "hbtreat_currentdate",
"year") %in% names(smr01))) {
stop(paste0("Object smr01 does not contain the required variables. ",
"Must contain:
link_no
admission_date
discharge_date
cis_marker
postcode
discharge_type
sex
admgrp
ci_chi_number
admfgrp
ipdc
age_in_years
age_grp
quarter
year
location
hbtreat_currentdate
main_condition
other_condition_1 to other_condition_5
specialty
ipdc"))
}
if(!all(c("diag1_4", "diagnosis_group") %in% names(pdiags))){
stop(paste0("Object pdiags does not contain the required variables.",
"Must contain:
diag1_4
diagnosis_group"))
}
if(!all(c("morb", "wmorbs", "diag") %in% names(morbs))){
stop(paste0("Object morbs does not contain the required variables.",
"Must contain:
morb
wmorbs
diag"))
}
if(!all(c("link_no", "date_of_death") %in% names(gro))) {
stop(paste0("Object gro does not contain the required variables.",
"Must contain:
link_no
date_of_death"))
}
if(!is.numeric(smr01$link_no)){
stop(paste0("Link_no must be a numeric"))
}
if(!is.numeric(smr01$cis_marker)){
stop(paste0("cis_marker must be a numeric"))
}
if(!lubridate::is.POSIXct(smr01$admission_date)){
stop(paste0("Admission_date variable must be POSIXct of format",
" %Y-%m-%d"))
}
if(!lubridate::is.POSIXct(smr01$discharge_date)){
stop(paste0("Discharge_date variable must be POSIXct of format",
" %Y-%m-%d"))
}
if(!all(1:140 %in% pdiags$diagnosis_group)){
stop(paste0("Primary diagnosis lookup does not contain all 140 ",
"primary diagnosis groupings."))
}
if(!all(1:17 %in% morbs$morb)){
stop(paste0("Co-morbidities lookup does not contain all 17 comorbidity
groups."))
}
### 2 - Match deaths data to SMR01 ----
# Remove duplicate records on link_no
# The deaths file is matched on to SMR01 by link_no,
# therefore link_no needs to be unique
gro %<>%
tidylog::distinct(link_no, .keep_all = TRUE)
# Match deaths data on to SMR01 data
smr01 %<>%
tidylog::left_join(gro, by = "link_no") %>%
# Sort data by link_no, cis_marker, adm_date and dis_date as per guidance
dplyr::arrange(link_no, cis_marker, admission_date, discharge_date)
### 3 - Basic SMR01 processing ----
# Create the following variables:
# death_inhosp = 1 if the patient died in hospital during that episode of care
# dthdays = the number of days from admission till death
# death30 = 1 if the patient died within 30 days of admission date
# quarter_name = quarter name in text form
# quarter = quarter in number form (quarter 1 = Jan - Mar 2011)
# location = recodes some hospital codes for combined sites
# diag1_4 = main condition ICD-10 code to 4 digits
# diagx = ICD-10 code to 3 and 4 digits, separated by an underscore
# pdiag_grp = matches the primary diagnosis group on the 4-digit ICD-10
# code
# wcomorbsx = matches the charlson index weighting if the relevant ICD-10
# codes are present in any of the five "other diagnosis"
# positions
# comorbs_sum = sum of the wcomorbsx values across the episode
smr01 %<>%
tidylog::mutate(death_inhosp = dplyr::if_else(dplyr::between(
as.numeric(discharge_type), 40, 49),
1, 0),
dthdays = lubridate::interval(admission_date, date_of_death) /
lubridate::days(1),
death30 = dplyr::case_when(
dplyr::between(dthdays, 0, 30) ~ 1,
TRUE ~ 0),
quarter_name = paste0(year, "Q", quarter),
quarter = as.numeric(as.factor(quarter_name)),
diag1_4 = substr(main_condition, 1, 4),
diag2 = paste(substr(other_condition_1, 1, 3),
substr(other_condition_1, 1, 4),
sep = "_"),
diag3 = paste(substr(other_condition_2, 1, 3),
substr(other_condition_2, 1, 4),
sep = "_"),
diag4 = paste(substr(other_condition_3, 1, 3),
substr(other_condition_3, 1, 4),
sep = "_"),
diag5 = paste(substr(other_condition_4, 1, 3),
substr(other_condition_4, 1, 4),
sep = "_"),
diag6 = paste(substr(other_condition_5, 1, 3),
substr(other_condition_5, 1, 4),
sep = "_")) %>%
# Create the pdiag_grp and wcomorbsx variables using joins to the morbs
# dataset
tidylog::left_join(tidylog::select(pdiags,
pdiag_grp = diagnosis_group,
diag1_4),
by = "diag1_4") %>%
# Match on specialty grouping by the specialty variable
tidylog::left_join(spec, by = "specialty") %>%
# Fuzzy joins add the (in this case, not needed) joining variable by
# default, so append these with "_z" so they can be easily removed
# afterwards
fuzzyjoin::fuzzy_left_join(tidylog::select(morbs,
wcomorbs1 = wmorbs,
comorbs1 = morb,
diag2_z = diag),
by = c("diag2" = "diag2_z"),
match_fun = stringr::str_detect) %>%
fuzzyjoin::fuzzy_left_join(tidylog::select(morbs,
wcomorbs2 = wmorbs,
comorbs2 = morb,
diag3_z = diag),
by = c("diag3" = "diag3_z"),
match_fun = stringr::str_detect) %>%
fuzzyjoin::fuzzy_left_join(tidylog::select(morbs,
wcomorbs3 = wmorbs,
comorbs3 = morb,
diag4_z = diag),
by = c("diag4" = "diag4_z"),
match_fun = stringr::str_detect) %>%
fuzzyjoin::fuzzy_left_join(tidylog::select(morbs,
wcomorbs4 = wmorbs,
comorbs4 = morb,
diag5_z = diag),
by = c("diag5" = "diag5_z"),
match_fun = stringr::str_detect) %>%
fuzzyjoin::fuzzy_left_join(tidylog::select(morbs,
wcomorbs5 = wmorbs,
comorbs5 = morb,
diag6_z = diag),
by = c("diag6" = "diag6_z"),
match_fun = stringr::str_detect) %>%
# Remove joining variables
tidylog::select(-dplyr::ends_with("_z")) %>%
# Replace cases with no match with zero
tidyr::replace_na(list(wcomorbs1 = 0,
wcomorbs2 = 0,
wcomorbs3 = 0,
wcomorbs4 = 0,
wcomorbs5 = 0,
comorbs1 = 0,
comorbs2 = 0,
comorbs3 = 0,
comorbs4 = 0,
comorbs5 = 0)) %>%
# Comorbs cleaning
tidylog::mutate(wcomorbs1 = replace(wcomorbs1,
comorbs1 == 12 &
(comorbs2 != 6 & comorbs3 != 6 &
comorbs4 != 6 & comorbs5 != 6),
2),
wcomorbs2 = replace(wcomorbs2,
comorbs2 == 12 &
(comorbs1 != 6 & comorbs3 != 6 &
comorbs4 != 6 & comorbs5 != 6),
2),
wcomorbs3 = replace(wcomorbs3,
comorbs3 == 12 &
(comorbs2 != 6 & comorbs1 != 6 &
comorbs4 != 6 & comorbs5 != 6),
2),
wcomorbs4 = replace(wcomorbs4,
comorbs4 == 12 &
(comorbs2 != 6 & comorbs3 != 6 &
comorbs1 != 6 & comorbs5 != 6),
2),
wcomorbs5 = replace(wcomorbs5,
comorbs5 == 12 &
(comorbs2 != 6 & comorbs3 != 6 &
comorbs4 != 6 & comorbs1 != 6),
2)) %>%
tidylog::mutate(wcomorbs1 = replace(wcomorbs1,
comorbs1 == 11 & (comorbs2 == 15 |
comorbs3 == 15 |
comorbs4 == 15 |
comorbs5 == 15),
0),
wcomorbs2 = replace(wcomorbs2,
comorbs2 == 11 & (comorbs1 == 15 |
comorbs3 == 15 |
comorbs4 == 15 |
comorbs5 == 15),
0),
wcomorbs3 = replace(wcomorbs3,
comorbs3 == 11 & (comorbs2 == 15 |
comorbs1 == 15 |
comorbs4 == 15 |
comorbs5 == 15),
0),
wcomorbs4 = replace(wcomorbs4,
comorbs4 == 11 & (comorbs2 == 15 |
comorbs3 == 15 |
comorbs1 == 15 |
comorbs5 == 15),
0),
wcomorbs5 = replace(wcomorbs5,
comorbs5 == 11 & (comorbs2 == 15 |
comorbs3 == 15 |
comorbs4 == 15 |
comorbs1 == 15),
0)) %>%
tidylog::mutate(wcomorbs2 = replace(wcomorbs2,
comorbs2 == comorbs1,
0),
wcomorbs3 = replace(wcomorbs3,
comorbs3 == comorbs1 |
comorbs3 == comorbs2,
0),
wcomorbs4 = replace(wcomorbs4,
comorbs4 == comorbs1 |
comorbs4 == comorbs2 |
comorbs4 == comorbs3,
0),
wcomorbs5 = replace(wcomorbs5,
comorbs5 == comorbs1 |
comorbs5 == comorbs2 |
comorbs5 == comorbs3 |
comorbs5 == comorbs4,
0)) %>%
tidylog::mutate(comorbs_sum = rowSums(
tidylog::select(., dplyr::starts_with("wcomorbs")))) %>%
# Create two further variables at CIS level:
# epinum = the episode number for each individual episode within the CIS
# death_inhosp_max = 1 if the patient died in hospital during any episode
# of the CIS
tidylog::group_by(link_no, cis_marker) %>%
tidylog::mutate(epinum = dplyr::row_number(),
death_inhosp_max = max(death_inhosp)) %>%
dplyr::ungroup() %>%
# Sort data as per guidance and remove variables no longer required
dplyr::arrange(link_no, cis_marker, admission_date, discharge_date) %>%
tidylog::select(-dplyr::contains("condition"),
-dplyr::starts_with("wcomorbs"),
-comorbs1, -comorbs2, -comorbs3, -comorbs4, -comorbs5,
-quarter_name)
### 4 - SIMD ----
# Fix formatting of postcode variable
smr01 %<>%
# First remove all spaces from postcode variable
tidylog::mutate(postcode = gsub("\\s", "", postcode),
# Then add space (or spaces) at appropriate juncture
# (depending on the number of characters) to get the
# postcode into 7-character format
postcode = dplyr::case_when(
is.na(postcode)
~ NA_character_,
stringr::str_length(postcode) == 5
~ sub("(.{2})", "\\1 ", postcode),
stringr::str_length(postcode) == 6
~ sub("(.{3})", "\\1 ", postcode),
TRUE
~ postcode
)) %>%
# Join to the postcode lookup
tidylog::left_join(postcode, by = "postcode") %>%
# Assign the appropriate SIMD value to a patient depending on the year they
# were admitted
tidylog::mutate(simd = dplyr::case_when(
year >=2017 ~ simd_2020,
year >= 2014 & year < 2017 ~ simd_2016,
year < 2014 ~ simd_2012
)) %>%
# Remove the not needed year-specific SIMD variables
tidylog::select(-c(simd_2012, simd_2016, simd_2020)) %>%
tidylog::mutate(simd = dplyr::case_when(
is.na(simd) & postcode == "NK010AA" ~ 6,
is.na(simd) & postcode != "NK010AA" ~ 7,
TRUE ~ simd
))
return(smr01)
}
### END OF SCRIPT ###
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