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table_3.R
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table_3.R
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######################################
# This script
# - produces a table with the number of patients fully vaccinated (2 doses + 2 weeks) in
# selected clinical and demographic groups
# - saves table as html
######################################
# Preliminaries ----
## Import libraries
library('tidyverse')
library('here')
library('glue')
library('gt')
library('gtsummary')
## Import custom user functions
source(here("analysis", "functions.R"))
## Create output directory
fs::dir_create(here::here("output", "tables"))
## Import data
data_processed <- read_rds(here::here("output", "data", "data_all.rds"))
## Format data
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 3 ----
## All
## Calculate rates
rates0 <- data_processed %>%
mutate(time_since_2nd_dose = cut(follow_up_time_vax2,
breaks = c(14, 28, 42, 56, 84, Inf),
labels = c("2-4 weeks", "4-6 weeks", "6-8 weeks", "8-12 weeks", "12+ weeks"),
right = FALSE),
time_between_vaccinations = cut(tbv,
breaks = c(0, 42, 56, Inf),
labels = c("6 weeks or less", "6-8 weeks", "8 weeks or more"),
right = FALSE),
smoking_status = ifelse(is.na(smoking_status), "M", smoking_status)) %>%
select(sex,
bmi,
smoking_status,
ethnicity,
imd,
region,
asthma,
asplenia,
bpcat,
chd,
chronic_neuro_dis_inc_sig_learn_dis,
chronic_resp_dis,
chronic_kidney_disease,
end_stage_renal,
cld,
diabetes,
immunosuppression,
learning_disability,
sev_mental_ill,
organ_transplant,
time_since_2nd_dose,
time_between_vaccinations) %>%
tbl_summary()
rates0$inputs$data <- NULL
rates0 <- rates0$table_body %>%
select(group = variable, variable = label, count = stat_0) %>%
separate(count, c("count","perc"), sep = "([(])") %>%
# mutate(count = as.numeric(count),
# perc = gsub('.{2}$', '', perc)) %>%
mutate(count = gsub(" ", "", count),
count = as.numeric(gsub(",", "", count))) %>%
filter(!(is.na(count))) %>%
select(-perc)
rates1 <- calculate_rates(group = "covid_positive_test",
follow_up = "time_to_positive_test",
data = data_processed,
Y = 100000,
dig = 2,
variables = c("sex", "bmi", "smoking_status", "ethnicity",
"imd", "region", "asthma", "asplenia", "bpcat", "chd",
"chronic_neuro_dis_inc_sig_learn_dis", "chronic_resp_dis",
"chronic_kidney_disease", "end_stage_renal","cld",
"diabetes", "immunosuppression", "learning_disability",
"sev_mental_ill", "organ_transplant", "time_since_2nd_dose",
"time_between_vaccinations"))
table3_base <- left_join(rates0, rates1, by = c("group", "variable"))
colnames(table3_base) = c("Variable", "level",
"Fully vaccinated",
"covid_positive_test", "Rate1", "LCI1", "UCI1")
table3_base$group = 0
# Groups
table3 <- list()
for (i in 1:7){
## Filter on group
data_group <- data_processed %>%
filter(group == i)
## Calculate rates
rates0 <- data_group %>%
mutate(time_since_2nd_dose = cut(follow_up_time_vax2,
breaks = c(14, 28, 42, 56, 84, Inf),
labels = c("2-4 weeks", "4-6 weeks", "6-8 weeks", "8-12 weeks", "12+ weeks"),
right = FALSE),
time_between_vaccinations = cut(tbv,
breaks = c(0, 42, 56, Inf),
labels = c("6 weeks or less", "6-8 weeks", "8 weeks or more"),
right = FALSE),
smoking_status = ifelse(is.na(smoking_status), "M", smoking_status)) %>%
select(sex,
bmi,
smoking_status,
ethnicity,
imd,
region,
asthma,
asplenia,
bpcat,
chd,
chronic_neuro_dis_inc_sig_learn_dis,
chronic_resp_dis,
chronic_kidney_disease,
end_stage_renal,
cld,
diabetes,
immunosuppression,
learning_disability,
sev_mental_ill,
organ_transplant,
time_since_2nd_dose,
time_between_vaccinations) %>%
tbl_summary()
rates0$inputs$data <- NULL
rates0 <- rates0$table_body %>%
select(group = variable, variable = label, count = stat_0) %>%
separate(count, c("count","perc"), sep = "([(])") %>%
# mutate(count = as.numeric(count),
# perc = gsub('.{2}$', '', perc)) %>%
mutate(count = gsub(" ", "", count),
count = as.numeric(gsub(",", "", count))) %>%
filter(!(is.na(count))) %>%
select(-perc)
rates1 <- calculate_rates(group = "covid_positive_test",
follow_up = "time_to_positive_test",
data = data_group,
Y = 100000,
dig = 2,
variables = c("sex", "bmi", "smoking_status", "ethnicity",
"imd", "region", "asthma", "asplenia", "bpcat", "chd",
"chronic_neuro_dis_inc_sig_learn_dis", "chronic_resp_dis",
"chronic_kidney_disease", "end_stage_renal","cld",
"diabetes", "immunosuppression", "learning_disability",
"sev_mental_ill", "organ_transplant", "time_since_2nd_dose",
"time_between_vaccinations"))
table3_tmp <- left_join(rates0, rates1, by = c("group", "variable"))
colnames(table3_tmp) = c("Variable", "level",
"Fully vaccinated",
"covid_positive_test", "Rate1", "LCI1", "UCI1")
table3_tmp$group = i
table3 <- rbind(table3, table3_tmp)
}
table3 <- rbind(table3_base, table3)
# Redaction ----
## Redact values < 8
threshold = 8
table3_redacted <- table3 %>%
mutate(`Fully vaccinated` = ifelse(`Fully vaccinated` < threshold, NA, as.numeric(`Fully vaccinated`)),
covid_positive_test = ifelse(covid_positive_test < threshold, NA, covid_positive_test),
Rate1 = ifelse(is.na(covid_positive_test), NA, Rate1),
LCI1 = ifelse(is.na(covid_positive_test), NA, LCI1),
UCI1 = ifelse(is.na(covid_positive_test), NA, UCI1))
# ## Round to nearest 5
table3_redacted <- table3_redacted %>%
mutate(`Fully vaccinated` = plyr::round_any(`Fully vaccinated`, 5),
covid_positive_test = plyr::round_any(covid_positive_test, 5))
## Recalculate totals
## Replace na with [REDACTED]
# table3_redacted[[i]] <- table3_redacted[[i]] %>%
# replace(is.na(.), "[REDACTED]")
# Save as html ----
gt::gtsave(gt(table3), here::here("output","tables", "table3.html"))
gt::gtsave(gt(table3_redacted), here::here("output","tables", "table3_redacted.html"))