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baseline_table_2019.R
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baseline_table_2019.R
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# # # # # # # # # # # # # # # # # # # # #
# This script:
# Generate charlson comorbidity scores and baseline table for service evaluation
# # # # # # # # # # # # # # # # # # # # #
## install package
#install.packages("tableone")
## Import libraries---
library("tidyverse")
library('plyr')
library('dplyr')
library('lubridate')
library('stringr')
library("data.table")
library("ggpubr")
library("finalfit")
setwd(here::here("output", "measures"))
### read data ###
### use new synthesised .rds files for faster loading
df_input <- read_rds('basic_record_2019.rds')
df_input$date <- as.Date(df_input$date)
#df_input$cal_mon <- month(df_input$date)
#df_input$cal_year <- year(df_input$date)
# remove last month data
#last.date=max(df_input$date)
#df=df_input%>% filter(date!=last.date)
first_mon <- (format(min(df_input$date), "%m-%Y"))
last_mon <- (format(max(df_input$date), "%m-%Y"))
num_pats <- length(unique(df_input$patient_id))
num_pracs <- length(unique(df_input$practice))
overall_counts <- as.data.frame(cbind(first_mon, last_mon, num_pats, num_pracs))
write_csv(overall_counts, here::here("output", "overall_counts_blt_2019.csv"))
rm(overall_counts)
## randomly select one observation for each patient
## in the study period to generate baseline table for service evaluation
df<-df_input
df_one_pat <- df %>% dplyr::group_by(patient_id) %>%
dplyr::arrange(date, .group_by=TRUE) %>%
sample_n(1)
## clear environment to make more space on server...?
rm(df_input, df)
## create charlson index
df_one_pat$cancer_comor<- ifelse(df_one_pat$cancer_comor == 1L, 2L, 0L)
df_one_pat$cardiovascular_comor <- ifelse(df_one_pat$cardiovascular_comor == 1L, 1L, 0L)
df_one_pat$chronic_obstructive_pulmonary_comor <- ifelse(df_one_pat$chronic_obstructive_pulmonary_comor == 1L, 1L, 0)
df_one_pat$heart_failure_comor <- ifelse(df_one_pat$heart_failure_comor == 1L, 1L, 0L)
df_one_pat$connective_tissue_comor <- ifelse(df_one_pat$connective_tissue_comor == 1L, 1L, 0L)
df_one_pat$dementia_comor <- ifelse(df_one_pat$dementia_comor == 1L, 1L, 0L)
df_one_pat$diabetes_comor <- ifelse(df_one_pat$diabetes_comor == 1L, 1L, 0L)
df_one_pat$diabetes_complications_comor <- ifelse(df_one_pat$diabetes_complications_comor == 1L, 2L, 0L)
df_one_pat$hemiplegia_comor <- ifelse(df_one_pat$hemiplegia_comor == 1L, 2L, 0L)
df_one_pat$hiv_comor <- ifelse(df_one_pat$hiv_comor == 1L, 6L, 0L)
df_one_pat$metastatic_cancer_comor <- ifelse(df_one_pat$metastatic_cancer_comor == 1L, 6L, 0L)
df_one_pat$mild_liver_comor <- ifelse(df_one_pat$mild_liver_comor == 1L, 1L, 0L)
df_one_pat$mod_severe_liver_comor <- ifelse(df_one_pat$mod_severe_liver_comor == 1L, 3L, 0L)
df_one_pat$mod_severe_renal_comor <- ifelse(df_one_pat$mod_severe_renal_comor == 1L, 2L, 0L)
df_one_pat$mi_comor <- ifelse(df_one_pat$mi_comor == 1L, 1L, 0L)
df_one_pat$peptic_ulcer_comor <- ifelse(df_one_pat$peptic_ulcer_comor == 1L, 1L, 0L)
df_one_pat$peripheral_vascular_comor <- ifelse(df_one_pat$peripheral_vascular_comor == 1L, 1L, 0L)
## total charlson for each patient
charlson=c("cancer_comor","cardiovascular_comor","chronic_obstructive_pulmonary_comor",
"heart_failure_comor","connective_tissue_comor", "dementia_comor",
"diabetes_comor","diabetes_complications_comor","hemiplegia_comor",
"hiv_comor","metastatic_cancer_comor" ,"mild_liver_comor",
"mod_severe_liver_comor", "mod_severe_renal_comor", "mi_comor",
"peptic_ulcer_comor" , "peripheral_vascular_comor" )
df_one_pat$charlson_score=rowSums(df_one_pat[charlson])
## Charlson - as a catergorical group variable
df_one_pat <- df_one_pat %>%
mutate(charlsonGrp = case_when(charlson_score >0 & charlson_score <=2 ~ 2,
charlson_score >2 & charlson_score <=4 ~ 3,
charlson_score >4 & charlson_score <=6 ~ 4,
charlson_score >=7 ~ 5,
charlson_score == 0 ~ 1))
df_one_pat$charlsonGrp <- as.factor(df_one_pat$charlsonGrp)
df_one_pat$charlsonGrp <- factor(df_one_pat$charlsonGrp,
labels = c("zero", "low", "medium", "high", "very high"))
#bmi
#remove very low observations
df_one_pat$bmi <- ifelse(df_one_pat$bmi <8 | df_one_pat$bmi>50, NA, df_one_pat$bmi)
# bmi categories
df_one_pat<- df_one_pat %>%
mutate(bmi_cat = case_when(is.na(bmi) ~ "unknown",
bmi>=8 & bmi< 18.5 ~ "underweight",
bmi>=18.5 & bmi<=24.9 ~ "healthy weight",
bmi>24.9 & bmi<=29.9 ~ "overweight",
bmi>29.9 ~"obese"))
df_one_pat$bmi_cat<- as.factor(df_one_pat$bmi_cat)
#summary(df_one_pat$bmi_cat)
df_one_pat$age_cat<- as.factor(df_one_pat$age_cat)
df_one_pat$region<- as.factor(df_one_pat$region)
# smoking
#str(df_one_pat$smoking_status) #factor with 5 levels - so doesnt recognise missing values
df_one_pat <- df_one_pat %>%
mutate(smoking_cat = case_when(smoking_status=="S" ~ "current",
smoking_status=="E" ~ "former",
smoking_status=="N" ~ "never",
smoking_status=="M"| smoking_status=="" ~ "unknown"))
df_one_pat$smoking_cat<- as.factor(df_one_pat$smoking_cat)
#summary(df_one_pat$smoking_cat)
# imd levels
#summary(df_one_pat$imd) #str(df_one_pat$imd) ## int 0,1,2,3,4,5
# make it a factor variable and 0 is missing
df_one_pat$imd<- as.factor(df_one_pat$imd)
## ethnicity
df_one_pat$ethnicity=ifelse(is.na(df_one_pat$ethnicity),"6",df_one_pat$ethnicity)
df_one_pat <- df_one_pat %>%
mutate(ethnicity_6 = case_when(ethnicity == 1 ~ "White",
ethnicity == 2 ~ "Mixed",
ethnicity == 3 ~ "South Asian",
ethnicity == 4 ~ "Black",
ethnicity == 5 ~ "Other",
ethnicity == 6 ~ "Unknown"))
df_one_pat$ethnicity_6 <- as.factor(df_one_pat$ethnicity_6)
#table(df_one_pat$ethnicity_6)
# count of GP consultations in 12m before random index date
#summary(df_one_pat$gp_count) #negative values in dummy data
df_one_pat$gp_count <- ifelse(df_one_pat$gp_count > 0,
df_one_pat$gp_count, 0)
### flu vac in 12m before random index date
#summary(df_one_pat$flu_vaccine)
df_one_pat$flu_vaccine <- as.factor(df_one_pat$flu_vaccine)
# ## Any covid vaccine
# str(df_one_pat$covrx1_dat)
# summary(df_one_pat$covrx1_dat)
# summary(df_one_pat$covrx2_dat)
df_one_pat$covrx1=ifelse(is.na(df_one_pat$covrx1_dat),0,1)
df_one_pat$covrx2=ifelse(is.na(df_one_pat$covrx2_dat),0,1)
df_one_pat$covrx=ifelse(df_one_pat$covrx1 >0 | df_one_pat$covrx2 >0, 1, 0)
df_one_pat$covrx <- as.factor(df_one_pat$covrx)
# #summary(df_one_pat$covrx)
# ever died
df_one_pat$died_ever <- ifelse(is.na(df_one_pat$died_date),0,1)
df_one_pat$died_ever <- as.factor(df_one_pat$died_ever)
#summary(df_one_pat$died_ever)
## covid positive ever
#df_one_pat$covid_positive<- df_one_pat$Covid_test_result_sgss
#df_one_pat$covid_positive<-as.factor(df_one_pat$covid_positive)
df_one_pat$Covid_test_result_sgss<- as.factor(df_one_pat$Covid_test_result_sgss)
df_one_pat$hx_indications <- as.factor(df_one_pat$hx_indications)
df_one_pat$hx_antibiotics <- as.factor(df_one_pat$hx_antibiotics)
## select variables for the baseline table
bltab_vars <- select(df_one_pat, date, age, age_cat, sex, bmi,
bmi_cat, ethnicity_6, charlsonGrp, smoking_cat,
flu_vaccine, gp_count, antibacterial_brit,
antibacterial_12mb4, broad_spectrum_antibiotics_prescriptions,
Covid_test_result_sgss, imd, hx_indications, hx_antibiotics,
covrx, died_ever)
# generate data table
# columns for baseline table
colsfortab <- colnames(bltab_vars)
bltab_vars %>% summary_factorlist(explanatory = colsfortab) -> t
#str(t)
write_csv(t, here::here("output", "blt_one_random_obs_perpat_2019.csv"))