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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 <- read_rds(here::here("output", "data", "data_processed.rds"))
data_trial <- read_rds(here::here("output", "data", "data_processed_trial.rds"))
## Format groups
data <- data %>%
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_by(patient_id) %>%
mutate(follow_up_time = min((follow_up_time_vax2 - 14),
time_to_positive_test,
time_to_hospitalisation,
time_to_covid_death)) %>%
ungroup()
data_trial <- data_trial %>%
mutate(group = 8) %>%
group_by(patient_id) %>%
mutate(follow_up_time = min((follow_up_time_vax2 - 14),
time_to_positive_test,
time_to_hospitalisation,
time_to_covid_death)) %>%
ungroup()
data_processed = rbind(data, data_trial) %>%
mutate(group = as.factor(group))
# Table 1 shell ----
results.table <- data.frame(matrix(nrow = 9, ncol = 22))
colnames(results.table) <- c("Group","Fully vaccinated",
"Positive COVID test", "PYs_1", "rate_1", "lci_1", "uci_1",
"Hospitalised with COVID", "PYs_2", "rate_2", "lci_2", "uci_2",
"Critical care with COVID", "PYs_3", "rate_3", "lci_3", "uci_3",
"COVID Deaths", "PYs_4", "rate_4", "lci_4", "uci_4")
results.table[1:9,1] <- 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",
"Trial")
# Fill in table ----
## 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))
results.table[9,2] <- nrow(data_processed %>% filter(group == 8))
## Other outcomes
for (i in 1:4) {
# Counts and rates
Y = 1000
dig = 2
results.table[1,((5*i - 2):(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(person_time = person_time/365.25,
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*Y, digits = 2),
lower_py = round(lower*Y, digits = 2),
upper_py = round(upper*Y, digits = 2),
person_time = round(person_time, digits = 0)) %>%
select(n_postest, person_time, Rate_py, lower_py, upper_py)
results.table[2:9,(5*i - 2):(5*i + 2)] <- data_processed %>%
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(person_time = person_time/365.25,
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*Y, digits = 2),
lower_py = round(lower*Y, digits = 2),
upper_py = round(upper*Y, digits = 2),
person_time = round(person_time, digits = 0)) %>%
select(n_postest, person_time, Rate_py, lower_py, upper_py)
}
## Counts of tests and positivity rate
test_counts_all <- data_processed %>%
select(patient_id, group, tests_conducted_any, tests_conducted_positive) %>%
summarise(n = n(),
test_0 = sum(is.na(tests_conducted_any)),
test_1 = sum(tests_conducted_any == 1, na.rm = T),
test_2 = sum(tests_conducted_any == 2, na.rm = T),
test_3 = sum(tests_conducted_any > 3, na.rm = T),
tests_conducted_any = sum(tests_conducted_any, na.rm = TRUE),
tests_conducted_positive = sum(tests_conducted_positive, na.rm = TRUE)) %>%
ungroup() %>%
mutate(positivy = tests_conducted_positive/tests_conducted_any*100) %>%
mutate(group = 0)
test_counts_groups <- data_processed %>%
select(patient_id, group, tests_conducted_any, tests_conducted_positive) %>%
group_by(group) %>%
summarise(n = n(),
test_0 = sum(is.na(tests_conducted_any)),
test_1 = sum(tests_conducted_any == 1, na.rm = T),
test_2 = sum(tests_conducted_any == 2, na.rm = T),
test_3 = sum(tests_conducted_any > 2, na.rm = T),
tests_conducted_any = sum(tests_conducted_any, na.rm = TRUE),
tests_conducted_positive = sum(tests_conducted_positive, na.rm = TRUE)) %>%
ungroup() %>%
mutate(positivy = tests_conducted_positive/tests_conducted_any*100)
test_counts <- rbind(test_counts_all, test_counts_groups) %>%
mutate(Group = results.table$Group) %>%
select(Group, test_0, test_1, test_2, test_3, tests_conducted_any, positivy)
## Follow-up time
follow_up_all <- data_processed %>%
summarise(quantile = scales::percent(c(0.25, 0.5, 0.75)),
fu = quantile(follow_up_time, c(0.25, 0.5, 0.75))) %>%
mutate(fu = round(fu, digits = 0)) %>%
pivot_wider(names_from = quantile, values_from = fu) %>%
mutate(group = 0,
fu = paste(`50%`, " (", `25%`, "-", `75%`, ")", sep = ""))
follow_up_groups <- data_processed %>%
group_by(group) %>%
summarise(quantile = scales::percent(c(0.25, 0.5, 0.75)),
fu = quantile(follow_up_time, c(0.25, 0.5, 0.75))) %>%
mutate(fu = round(fu, digits = 0)) %>%
pivot_wider(id_cols = c("group"), names_from = quantile, values_from = fu) %>%
mutate(fu = paste(`50%`, " (", `25%`, "-", `75%`, ")", sep = ""))
follow_up <- rbind(follow_up_all, follow_up_groups) %>%
mutate(Group = results.table$Group) %>%
select(Group, fu)
## Combine tables
table1 <- left_join(results.table, test_counts, by = "Group") %>%
left_join(follow_up) %>%
mutate(test_0 = round(test_0/`Fully vaccinated`*100, digits = 0),
test_1 = round(test_1/`Fully vaccinated`*100, digits = 0),
test_2 = round(test_2/`Fully vaccinated`*100, digits = 0),
test_3 = round(test_3/`Fully vaccinated`*100, digits = 0),
positivy = round(positivy, digits = 2)) %>%
select("Group", "Fully vaccinated", "fu", "test_0", "test_1", "test_2", "test_3",
"Positive COVID test", "positivy", "PYs_1", "rate_1", "lci_1", "uci_1",
"Hospitalised with COVID", "PYs_2", "rate_2", "lci_2", "uci_2",
"COVID Deaths", "PYs_4", "rate_4", "lci_4", "uci_4")
# Redaction ----
## Redact values < 8
threshold = 8
results.table_redacted <- table1 %>%
mutate(`Fully vaccinated` = ifelse(`Fully vaccinated` < threshold, NA, as.numeric(`Fully vaccinated`)),
fu = ifelse(is.na(`Fully vaccinated`), NA, fu),
test_0 = ifelse(is.na(`Fully vaccinated`), NA, test_0),
test_1 = ifelse(is.na(`Fully vaccinated`), NA, test_1),
test_2 = ifelse(is.na(`Fully vaccinated`), NA, test_2),
test_3 = ifelse(is.na(`Fully vaccinated`), NA, test_3),
`Positive COVID test` = ifelse(`Positive COVID test` < threshold, NA, `Positive COVID test`),
positivy = ifelse(`Positive COVID test` < threshold, NA, positivy),
PYs_1 = ifelse(`Positive COVID test` < threshold, NA, PYs_1),
rate_1 = ifelse(`Positive COVID test` < threshold, NA, rate_1),
lci_1 = ifelse(`Positive COVID test` < threshold, NA, lci_1),
uci_1 = ifelse(`Positive COVID test` < threshold, NA, uci_1),
`Hospitalised with COVID` = ifelse(`Hospitalised with COVID` < threshold, NA, `Hospitalised with COVID`),
PYs_2 = ifelse(`Hospitalised with COVID` < threshold, NA, PYs_2),
rate_2 = ifelse(`Hospitalised with COVID` < threshold, NA, rate_2),
lci_2 = ifelse(`Hospitalised with COVID` < threshold, NA, lci_2),
uci_2 = ifelse(`Hospitalised with COVID` < threshold, NA, uci_2),
`COVID Deaths` = ifelse(`COVID Deaths` < threshold, NA, `COVID Deaths`),
PYs_4 = ifelse(is.na(`COVID Deaths`), NA, PYs_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",
"Trial")) %>%
mutate(test_0 = ifelse(test_0/100*`Fully vaccinated` < threshold, NA, test_0),
test_1 = ifelse(test_1/100*`Fully vaccinated` < threshold, NA, test_1),
test_2 = ifelse(test_2/100*`Fully vaccinated` < threshold, NA, test_2),
test_3 = ifelse(test_3/100*`Fully vaccinated` < threshold, NA, test_3))
## 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]")
## Formatting
results.table_redacted <- results.table_redacted %>%
mutate(Fully_vaccinated_count =`Fully vaccinated`,
Positive_test_count = paste(`Positive COVID test`, " (", PYs_1, ")", sep = ""),
Positive_test_rate = paste(rate_1, " (", lci_1, "-", uci_1, ")", sep = ""),
Hospitalised_count = paste(`Hospitalised with COVID`, " (", PYs_2, ")", sep = ""),
Hospitalised_rate = paste(rate_2, " (", lci_2, "-", uci_2, ")", sep = ""),
Death_count = paste(`COVID Deaths`, " (", PYs_4, ")", sep = ""),
Death_rate = paste(rate_4, " (", lci_4, "-", uci_4, ")", sep = "")) %>%
select(Group, Fully_vaccinated_count, Follow_up = fu, test_0, test_1, test_2, test_3, Positive_test_count, Positivy = positivy, Positive_test_rate, Hospitalised_count, Hospitalised_rate,
Death_count, Death_rate)
# Save as html ----
gt::gtsave(gt(table1), here::here("output","tables", "table1.html"))
gt::gtsave(gt(results.table_redacted), here::here("output","tables", "table1_redacted.html"))