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aggregate_outcomes.R
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aggregate_outcomes.R
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################################################################
# This script:
# - Calculates number of outcomes by week by age
################################################################
# For running locally only #
# setwd("C:/Users/aschaffer/OneDrive - Nexus365/Documents/GitHub/vax-fourth-dose-RD")
# getwd()
# Import libraries #
library('tidyverse')
library('lubridate')
library('arrow')
library('here')
library('reshape2')
library('dplyr')
library('fs')
library('ggplot2')
library('RColorBrewer')
library('data.table')
## Create directories
dir_create(here::here("output", "covid_outcomes"), showWarnings = FALSE, recurse = TRUE)
dir_create(here::here("output", "cohort"), showWarnings = FALSE, recurse = TRUE)
dir_create(here::here("output", "descriptive"), showWarnings = FALSE, recurse = TRUE)
dir_create(here::here("output", "covid_outcomes","by_start_date"), showWarnings = FALSE, recurse = TRUE)
# Load functions
source(here::here("analysis", "custom_functions.R"))
#########################################
# Total events by age in months
#########################################
agg1 <- function(start_date){
data1 <- read.csv(here::here("output", "cohort", paste0("outcomes_",start_date,".csv"))) %>%
group_by(age_mos) %>%
mutate(total = n()) %>%
ungroup() %>%
group_by(age_mos, total) %>%
summarise(n_covidcomposite = sum(covidcomposite == 1, na.rm = TRUE),
n_covidadmitted = sum(covidadmitted == 1, na.rm = TRUE),
n_coviddeath = sum(coviddeath == 1, na.rm = TRUE),
n_covidemerg = sum(covidemerg == 1, na.rm = TRUE),
n_respcomposite = sum(respcomposite ==1, na.rm = TRUE),
n_respdeath = sum(respdeath == 1, na.rm = TRUE),
n_respadmitted = sum(respadmitted == 1, na.rm = TRUE),
n_anydeath = sum(anydeath ==1, na.rm = TRUE),
n_anyadmitted = sum(anyadmitted == 1, na.rm = TRUE))
write.csv(data1, here::here("output", "covid_outcomes", "by_start_date", paste0("outcomes_byage_1mon_",start_date,".csv")), row.names = FALSE)
data6 <- read.csv(here::here("output", "cohort", paste0("outcomes_",start_date,".csv"))) %>%
mutate(age_mos6 = round(age_mos / 6) * 6) %>%
group_by(age_mos6) %>%
mutate(total = n()) %>%
ungroup() %>%
group_by(age_mos6, total) %>%
summarise(n_covidcomposite = sum(covidcomposite == 1, na.rm = TRUE),
n_covidadmitted = sum(covidadmitted == 1, na.rm = TRUE),
n_coviddeath = sum(coviddeath == 1, na.rm = TRUE),
n_covidemerg = sum(covidemerg == 1, na.rm = TRUE),
n_respcomposite = sum(respcomposite ==1, na.rm = TRUE),
n_respdeath = sum(respdeath == 1, na.rm = TRUE),
n_respadmitted = sum(respadmitted == 1, na.rm = TRUE),
n_anydeath = sum(anydeath ==1, na.rm = TRUE),
n_anyadmitted = sum(anyadmitted == 1, na.rm = TRUE))
write.csv(data6, here::here("output", "covid_outcomes", "by_start_date", paste0("outcomes_byage_6mon_",start_date,".csv")), row.names = FALSE)
data1_red <- data1 %>%
mutate(
across(contains("n_"), roundmid_any),
rate_covidcomposite = n_covidcomposite / total * 100000,
rate_covidadmitted = n_covidadmitted / total * 100000,
rate_coviddeath = n_coviddeath / total * 100000,
rate_covidemerg = n_covidemerg / total * 100000,
rate_respcomposite = n_respcomposite / total * 100000,
rate_respdeath = n_respdeath / total * 100000,
rate_respadmitted = n_respadmitted / total * 100000,
rate_anydeath = n_anydeath / total * 100000,
rate_anyadmitted = n_anyadmitted / total * 100000)
write.csv(data1_red, here::here("output", "covid_outcomes", "by_start_date", paste0("outcomes_byage_1mon_",start_date,"_red.csv")), row.names = FALSE)
data6_red <- data6 %>%
mutate(
across(contains("n_"), rounding),
rate_covidcomposite = n_covidcomposite / total * 100000,
rate_covidadmitted = n_covidadmitted / total * 100000,
rate_coviddeath = n_coviddeath / total * 100000,
rate_covidemerg = n_covidemerg / total * 100000,
rate_respcomposite = n_respcomposite / total * 100000,
rate_respdeath = n_respdeath / total * 100000,
rate_respadmitted = n_respadmitted / total * 100000,
rate_anydeath = n_anydeath / total * 100000,
rate_anyadmitted = n_anyadmitted / total * 100000)
write.csv(data6_red, here::here("output", "covid_outcomes", "by_start_date", paste0("outcomes_byage_6mon_",start_date,"_red.csv")), row.names = FALSE)
}
# Do the above over all relevant start dates
agg1("2022-09-03")
agg1("2022-10-15")
start_dates <- as.Date(0:5, origin = "2022-11-26")
sapply(start_dates,agg1)
########################################
# Create data frame with number of patients per cohort
pat <- function(start_date){
data <- read.csv(here::here("output", "cohort", paste0("outcomes_",start_date,".csv"))) %>%
mutate(n_pat = n_distinct(patient_id),
start = as.character(start_date),
n_pat = redact(n_pat),
n_pat = rounding(n_pat))
return(data)
}
extract <- sapply(start_dates, pat) %>%
t()
start <- do.call(rbind, lapply(extract[,2], as.data.frame))
colnames(start)[1] = "start"
n_pat <- do.call(rbind, lapply(extract[,1], as.data.frame))
colnames(n_pat)[1] = "n_pat"
total_n_by_date <- cbind(n_pat, start) %>%
as.data.frame() %>%
mutate(n_pat = as.integer(n_pat),
start = as.character(start))
write_csv(total_n_by_date, here::here("output", "descriptive", "total_n_by_date.csv"))