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00_import_data.R
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00_import_data.R
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# =======================================================
# Project: Diabetes outpatient care
# Purpose: Read in raw CPRD and linked data sets, combine files and save as Rds files
# Author: Fiona Grimm
# =======================================================
library(tidyverse)
# Source file paths: raw_data_path, raw_data_path_linked
source('R/file_paths.R')
# Patient -----------------------------------------------------------------
extract_patient <- read_tsv(str_c(raw_data_path, '2019_07_24_diabetes_Extract_Patient_001.txt'),
col_types = cols(frd = col_date(format = "%d/%m/%Y"),
crd = col_date(format = "%d/%m/%Y"),
CHSdate = col_date(format = "%d/%m/%Y"),
deathdate = col_date(format = "%d/%m/%Y"),
tod = col_date(format = "%d/%m/%Y")))
saveRDS(extract_patient, str_c(raw_RDS_path, 'Extract_patient.Rds'))
# ONS mortality -----------------------------------------------------------
death_patient <- read_tsv(str_c(raw_data_path_linked, 'death_patient_19_138.txt'),
col_types = cols(dor = col_date(format = "%d/%m/%Y"),
dod = col_date(format = "%d/%m/%Y")))
saveRDS(death_patient, str_c(raw_RDS_path, 'Linked_death_patient.Rds'))
# IMD ---------------------------------------------------------------------
imd_patient <- read_tsv(str_c(raw_data_path_linked, 'patient_imd2015_19_138.txt'))
saveRDS(imd_patient, str_c(raw_RDS_path, 'Linked_patient_imd.Rds'))
# Rural-urban indicator ---------------------------------------------------
rururb_practice <- read_tsv(str_c(raw_data_path_linked,'practice_urban_rural_19_138.txt'))
saveRDS(rururb_practice, str_c(raw_RDS_path, 'Linked_rururb_practice.Rds'))
# Clinical ----------------------------------------------------------------
clinical_files <- str_c(raw_data_path, '2019_07_24_diabetes_Extract_Clinical_00', seq(1:5),'.txt')
extract_clinical <- map(clinical_files, read_tsv, col_types = cols(eventdate = col_date(format = "%d/%m/%Y"),
sysdate = col_date(format = "%d/%m/%Y"))) %>%
reduce(rbind)
saveRDS(extract_clinical, str_c(raw_RDS_path, 'Extract_clinical.Rds'))
# Therapy -----------------------------------------------------------------
therapy_files <- c(str_c(raw_data_path, '2019_07_24_diabetes_Extract_Therapy_00',
seq(1:9), '.txt'),
str_c(raw_data_path, '2019_07_24_diabetes_Extract_Therapy_01',
c(0, seq(1:5)), '.txt'))
extract_therapy <- map(therapy_files, read_tsv, col_types = cols(eventdate = col_date(format = "%d/%m/%Y"),
sysdate = col_date(format = "%d/%m/%Y"),
qty = col_double(),
numpacks = col_double())) %>%
reduce(rbind)
saveRDS(extract_therapy, str_c(raw_RDS_path, 'Extract_therapy.Rds'))
# Test --------------------------------------------------------------------
test_files <- str_c(raw_data_path, '2019_07_24_diabetes_Extract_Test_00', seq(1:6), '.txt')
extract_tests <- map(test_files, read_tsv, col_types = cols(eventdate = col_date(format = "%d/%m/%Y"),
data1 = col_character(),
data2 = col_character(),
data3 = col_character(),
data4 = col_character(),
data5 = col_character(),
data6 = col_character(),
data7 = col_character(),
data8 = col_character())) %>%
reduce(rbind)
saveRDS(extract_tests, str_c(raw_RDS_path, 'Extract_test.Rds'))
# Additional clinical details ---------------------------------------------
extract_additional_clinical <- read_tsv(str_c(raw_data_path, '2019_07_24_diabetes_Extract_Additional_001.txt'),
col_types = cols(data1 = col_character(),
data2 = col_character(),
data3 = col_character(),
data4 = col_character(),
data5 = col_character(),
data6 = col_character(),
data7 = col_character()))
saveRDS(extract_additional_clinical, str_c(raw_RDS_path, 'Extract_additional_clinical.Rds'))
# Referral ----------------------------------------------------------------
extract_referral <- read_tsv(str_c(raw_data_path, '2019_07_24_diabetes_Extract_Referral_001.txt'),
col_types = cols(eventdate = col_date(format = "%d/%m/%Y"),
sysdate = col_date(format = "%d/%m/%Y")))
saveRDS(extract_referral, str_c(raw_RDS_path, 'Extract_additional_referral.Rds'))
# Consultation ------------------------------------------------------------
consultation_files <- str_c(raw_data_path, '2019_07_24_diabetes_Extract_Consultation_00', seq(1:5),'.txt')
extract_consultation <- map(consultation_files, read_tsv, col_types = cols(eventdate = col_date(format = "%d/%m/%Y"),
sysdate = col_date(format = "%d/%m/%Y"))) %>%
reduce(rbind)
saveRDS(extract_consultation, str_c(raw_RDS_path, 'Extract_consultation.Rds'))
# Staff -------------------------------------------------------------------
extract_staff <- read_tsv(str_c(raw_data_path, '2019_07_24_diabetes_Extract_Staff_001.txt'))
saveRDS(extract_staff, str_c(raw_RDS_path, 'Extract_staff.Rds'))
# Practice ----------------------------------------------------------------
extract_spractice <- read_tsv(str_c(raw_data_path, '2019_07_24_diabetes_Extract_Practice_001.txt'),
col_types = cols(lcd = col_date(format = "%d/%m/%Y"),
uts = col_date(format = "%d/%m/%Y")))
saveRDS(extract_spractice, str_c(raw_RDS_path, 'Extract_practice.Rds'))
# HES Patient ------------------------------------------------------------
hes_patient <- read_tsv(str_c(raw_data_path_linked, 'hes_patient_19_138.txt'))
saveRDS(hes_patient, str_c(raw_RDS_path, 'HES_patient.Rds'))
# HES Outpatients ------------------------------------------------------------
hesop_appts_raw <- read_tsv(str_c(raw_data_path_linked, 'hesop_appointment_19_138.txt'),
col_types = cols(patid = col_integer(),
apptdate = col_date(format = "%d/%m/%Y"),
dnadate = col_date(format = "%d/%m/%Y"),
reqdate = col_date(format = "%d/%m/%Y"),
apptage = col_integer()))
hesop_clinical_raw <- read_tsv(str_c(raw_data_path_linked, 'hesop_clinical_19_138.txt'),
col_types = cols(patid = col_integer(),
tretspef = col_integer(),
mainspef = col_integer()))
# NB specifying tretspef and mainspef as integeres will result in parsing failures, but this is intended
# the aim is to convert '&' into NAs during import
# Join treatment speciality and main speciality columns from hesop_clinical file
hesop_appts <- hesop_appts_raw %>%
left_join(hesop_clinical_raw[, c('patid', 'attendkey', 'tretspef','mainspef')], by = c('patid', 'attendkey'))
saveRDS(hesop_appts, str_c(raw_RDS_path, 'HES_outpatients.Rds'))
# HES Admitted Patient Care ------------------------------------------------------------
# Episodes
hesapc_episodes <- read_tsv(str_c(raw_data_path_linked, 'hes_episodes_19_138.txt'),
col_types = cols(admidate = col_date(format = "%d/%m/%Y"),
epistart = col_date(format = "%d/%m/%Y"),
epiend = col_date(format = "%d/%m/%Y"),
discharged = col_date(format = "%d/%m/%Y")))
saveRDS(hesapc_episodes, str_c(raw_RDS_path, 'HES_APC_episodes.Rds'))
hesapc_episodes_diagnoses <- read_tsv(str_c(raw_data_path_linked, 'hes_diagnosis_epi_19_138.txt'),
col_types = cols(epistart = col_date(format = "%d/%m/%Y"),
epiend = col_date(format = "%d/%m/%Y")))
hesapc_episodes_diagnoses <- hesapc_episodes_diagnoses %>%
select(-ICDx) %>%
mutate(d_order = ifelse(d_order %in% c(1:9), str_c('Diag_0', d_order), str_c('Diag_', d_order))) %>%
spread(key = 'd_order', value = 'ICD')
saveRDS(hesapc_episodes_diagnoses, str_c(raw_RDS_path, 'HES_APC_episodes_diagnoses.Rds'))
# Spells
hesapc_spells <- read_tsv(str_c(raw_data_path_linked, 'hes_hospital_19_138.txt'),
col_types = cols(admidate = col_date(format = "%d/%m/%Y"),
discharged = col_date(format = "%d/%m/%Y")))
saveRDS(hesapc_spells, str_c(raw_RDS_path, 'HES_APC_spells.Rds'))
# HES A&E attendances -----------------------------------------------------
hesae_attendances <- read_tsv(str_c(raw_data_path_linked, 'hesae_attendance_19_138.txt'),
col_types = cols(patid = col_integer(),
arrivaldate = col_date(format = "%d/%m/%Y")))
saveRDS(hesae_attendances, str_c(raw_RDS_path, 'HES_AE_attendances.Rds'))