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table_1.R
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table_1.R
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######################################
# This script
# - produces a table with the number of patients fully vaccinated (2 doses + 2 weeks) in initial priority groups,
# - and the number of patients with each outcome.
# - saves table as html
######################################
# Preliminaries ----
## Import libraries
library('tidyverse')
library('lubridate')
library('reshape2')
library('here')
library('gt')
## Create output directory
dir.create(here::here("output", "tables"), showWarnings = FALSE, recursive=TRUE)
## Import data
data_processed <- read_rds(here::here("output", "data", "data_all.rds"))
## Format groups
data_processed <- data_processed %>%
mutate(group = ifelse(care_home_65plus == 1, 1, NA),
group = ifelse(is.na(group) & ageband == 3, 2, group),
group = ifelse(is.na(group) & hscworker == 1, 3, group),
group = ifelse(is.na(group) & ageband == 2, 4, group),
group = ifelse(is.na(group) & shielded == 1, 5, group),
group = ifelse(is.na(group) & age >=50 & age <70, 6, group),
group = ifelse(is.na(group), 7, group),
group = factor(group))
# Table 1 shell ----
results.table <- data.frame(matrix(nrow = 8, ncol = 22))
colnames(results.table) <- c("Group","Fully vaccinated",
"Positive COVID test", "count_1", "rate_1", "lci_1", "uci_1",
"Hospitalised with COVID", "count_2", "rate_2", "lci_2", "uci_2",
"Critical care with COVID", "count_3", "rate_3", "lci_3", "uci_3",
"COVID Deaths", "count_4", "rate_4", "lci_4", "uci_4")
results.table[1:8,1] <- c("All",
"Care home (priority group 1)",
"80+ (priority group 2)",
"Health / care workers (priority groups 1-2)",
"70-79 (priority groups 3-4)",
"Shielding (age 16-69) (priority group 4)",
"50-69 (priority groups 5-9)",
"Others not in the above groups")
# Fill in table ----
datasets <- list(data_processed %>% filter(covid_positive_test == 1),
data_processed %>% filter(covid_hospital_admission == 1),
data_processed %>% filter(covid_hospitalisation_critical_care == 1),
data_processed %>% filter(covid_death == 1))
## Fully vaccinated
results.table[1,2] <- nrow(data_processed)
results.table[2,2] <- nrow(data_processed %>% filter(group == 1))
results.table[3,2] <- nrow(data_processed %>% filter(group == 2))
results.table[4,2] <- nrow(data_processed %>% filter(group == 3))
results.table[5,2] <- nrow(data_processed %>% filter(group == 4))
results.table[6,2] <- nrow(data_processed %>% filter(group == 5))
results.table[7,2] <- nrow(data_processed %>% filter(group == 6))
results.table[8,2] <- nrow(data_processed %>% filter(group == 7))
## Other outcomes
for (i in 1:length(datasets)) {
# Select dataset
data <- datasets[[i]]
# Counts
results.table[1,(5*i - 2)] <- nrow(data)
results.table[2,(5*i - 2)] <- nrow(data %>% filter(group == 1))
results.table[3,(5*i - 2)] <- nrow(data %>% filter(group == 2))
results.table[4,(5*i - 2)] <- nrow(data %>% filter(group == 3))
results.table[5,(5*i - 2)] <- nrow(data %>% filter(group == 4))
results.table[6,(5*i - 2)] <- nrow(data %>% filter(group == 5))
results.table[7,(5*i - 2)] <- nrow(data %>% filter(group == 6))
results.table[8,(5*i - 2)] <- nrow(data %>% filter(group == 7))
# Counts (as %)
results.table[1:8,(5*i - 1)] <- round((results.table[1:8,(5*i - 2)]/results.table[1:8,2])*100, digits = 2)
# Rates
Y = 100000
dig = 2
results.table[1,((5*i):(5*i + 2))] <- data %>%
summarise(
n_postest = ifelse(i == 1, sum(covid_positive_test),
ifelse(i == 2, sum(covid_hospital_admission),
ifelse(i == 3, sum(covid_hospitalisation_critical_care),
sum(covid_death)))),
person_time = ifelse(i == 1, sum(time_to_positive_test),
ifelse(i == 2, sum(time_to_hospitalisation),
ifelse(i == 3, sum(time_to_itu),
sum(time_to_covid_death))))
) %>%
ungroup() %>%
mutate(rate = n_postest/person_time,
lower = ifelse(rate - qnorm(0.975)*(sqrt(n_postest/(person_time^2))) < 0, 0,
rate - qnorm(0.975)*(sqrt(n_postest/(person_time^2)))),
upper = ifelse(rate + qnorm(0.975)*(sqrt(n_postest/(person_time^2))) < 0, 0,
rate + qnorm(0.975)*(sqrt(n_postest/(person_time^2)))),
Rate_py = round(rate/365.25*Y, digits = 2),
lower_py = round(lower/365.25*Y, digits = 2),
upper_py = round(upper/365.25*Y, digits = 2)) %>%
select(Rate_py, lower_py, upper_py)
results.table[2:8,(5*i):(5*i + 2)] <- data %>%
group_by(group, .drop=FALSE) %>%
summarise(
n_postest = ifelse(i == 1, sum(covid_positive_test),
ifelse(i == 2, sum(covid_hospital_admission),
ifelse(i == 3, sum(covid_hospitalisation_critical_care),
sum(covid_death)))),
person_time = ifelse(i == 1, sum(time_to_positive_test),
ifelse(i == 2, sum(time_to_hospitalisation),
ifelse(i == 3, sum(time_to_itu),
sum(time_to_covid_death))))
) %>%
ungroup() %>%
mutate(rate = n_postest/person_time,
lower = ifelse(rate - qnorm(0.975)*(sqrt(n_postest/(person_time^2))) < 0, 0,
rate - qnorm(0.975)*(sqrt(n_postest/(person_time^2)))),
upper = ifelse(rate + qnorm(0.975)*(sqrt(n_postest/(person_time^2))) < 0, 0,
rate + qnorm(0.975)*(sqrt(n_postest/(person_time^2)))),
Rate_py = round(rate/365.25*Y, digits = 2),
lower_py = round(lower/365.25*Y, digits = 2),
upper_py = round(upper/365.25*Y, digits = 2)) %>%
select(Rate_py, lower_py, upper_py)
}
# Redaction ----
## Redact values < 8
threshold = 8
results.table_redacted <- results.table%>%
select(Group, "Fully vaccinated",
"Positive COVID test", "count_1", "rate_1", "lci_1", "uci_1",
"Hospitalised with COVID", "count_2", "rate_2", "lci_2", "uci_2",
"COVID Deaths", "count_4", "rate_4", "lci_4", "uci_4") %>%
mutate(`COVID Deaths` = ifelse(`COVID Deaths` < threshold, NA, `COVID Deaths`),
count_4 = ifelse(is.na(`COVID Deaths`), NA, count_4),
rate_4 = ifelse(is.na(`COVID Deaths`), NA, rate_4),
lci_4 = ifelse(is.na(`COVID Deaths`), NA, lci_4),
uci_4 = ifelse(is.na(`COVID Deaths`), NA, uci_4)) %>%
mutate(Group = c("All",
"Care home (priority group 1)",
"80+ (priority group 2)",
"Health / care workers (priority group 1-2)",
"70-79 (priority group 3-4)",
"Shielding (age 16-69) (priority group 4)",
"50-69 (priority groups 5-9)",
"Others not in the above groups"))
## Round to nearest 5
results.table_redacted <- results.table_redacted %>%
mutate(`Fully vaccinated` = plyr::round_any(`Fully vaccinated`, 5),
`Positive COVID test` = plyr::round_any(`Positive COVID test`, 5),
`Hospitalised with COVID` = plyr::round_any(`Hospitalised with COVID`, 5),
`COVID Deaths` = plyr::round_any(`COVID Deaths`, 5))
## Recalculate column totals
results.table_redacted[1, "Fully vaccinated"] <- sum(results.table_redacted[-1,]$`Fully vaccinated`, na.rm = T)
results.table_redacted[1, "Positive COVID test"] <- sum(results.table_redacted[-1,]$`Positive COVID test`, na.rm = T)
results.table_redacted[1, "Hospitalised with COVID"] <- sum(results.table_redacted[-1,]$`Hospitalised with COVID`, na.rm = T)
results.table_redacted[1, "COVID Deaths"] <- sum(results.table_redacted[-1,]$`COVID Deaths`, na.rm = T)
## Replace na with [REDACTED]
results.table_redacted <- results.table_redacted %>%
replace(is.na(.), "[REDACTED]")
# Save as html ----
gt::gtsave(gt(results.table), here::here("output","tables", "table1.html"))
gt::gtsave(gt(results.table_redacted), here::here("output","tables", "table1_redacted.html"))