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check_fu.R
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check_fu.R
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library(tidyverse)
library(glue)
library(lubridate)
library(RColorBrewer)
################################################################################
# output directories
fs::dir_create(here::here("output", "tte", "images"))
################################################################################
study_parameters <- readr::read_rds(
here::here("analysis", "lib", "study_parameters.rds"))
K <- study_parameters$K
################################################################################
# redaction functions
source(here::here("analysis", "functions", "redaction_functions.R"))
# function to be applied in dplyr::filter
occurs_after_start_date <- function(cov_date, index_date) {
is.na(cov_date) | index_date < cov_date
}
################################################################################
# read data
data_tte <- readr::read_rds(
here::here("output", "data", "data_all.rds")) %>%
select(patient_id, subgroup, arm, split, death_date, dereg_date, subsequent_vax_date,
starts_with(c("start", "end"))) %>%
pivot_longer(
cols = c(starts_with(c("start","end"))),
names_to = c(".value", "k"),
names_pattern = "(.*)_(.*)_date"
) %>%
mutate(across(k, as.integer)) %>%
filter(
is.na(split) |
((k %% 2) == 0 & split == "even") |
((k %% 2) != 0 & split == "odd")
) %>%
select(-split) %>%
filter_at(
vars(str_c(unique(c("subsequent_vax", "dereg", "death")), "_date")),
all_vars(occurs_after_start_date(cov_date = ., index_date = start))
) %>%
filter(start <= as.Date(study_parameters$end_date)) %>%
mutate(across(ends_with("date"), ~if_else(start <= .x & .x <= end, .x, as.Date(NA_character_)))) %>%
mutate(across(end, ~pmin(end, death_date, dereg_date, subsequent_vax_date, as.Date(study_parameters$end_date), na.rm = TRUE))) %>%
select(patient_id, subgroup, arm, k, start, end) %>%
mutate(across(k, factor, levels = 1:K))
min_max <- data_tte %>%
group_by(subgroup) %>%
summarise(min = min(start), max = max(end)) %>%
ungroup()
# define variant dates
delta_start <- as.Date("2021-06-01")
omicron_start <- as.Date("2021-12-01")
delta_end <- as.Date("2021-12-15")
col_palette <- brewer.pal(n = 3, name = "Dark2")
for (s in levels(min_max$subgroup)) {
# earliest and latest fu dates
min_date <- min_max[["min"]][min_max$subgroup == s]
max_date <- min_max[["max"]][min_max$subgroup == s]
# prepare data
data_tte_long <- data_tte %>%
filter(subgroup == s) %>%
mutate(
tstart = as.integer(start - min_date),
tstop = as.integer(end - min_date)
) %>%
select(-start, -end) %>%
group_by(patient_id, k) %>%
nest() %>%
mutate(
date = map(data, ~.x$tstart:.x$tstop)
) %>%
unnest(cols = c(data, date)) %>%
ungroup() %>%
mutate(date = min_date + days(date)) %>%
group_by(k, date) %>%
count() %>%
ungroup() %>%
# round up to nearest 7
# also divide by 1000 to keep the plot tidy (will add note to y-axis)
mutate(across(n, ~ceiling_any(.x, to = 7)/1000))
# save data for output checking
capture.output(
data_tte_long %>%
mutate(across(n, ~.x*1000)) %>%
kableExtra::kable("pipe"),
file = here::here("output", "tte", "images", glue("check_fu_{s}.txt")),
append = FALSE
)
xintercepts <- Date()
names_xintercepts <- character()
n_mult <- numeric()
k_print <- integer()
index <- integer()
if (min_date <= delta_start) {
xintercepts <- c(xintercepts, delta_start)
names_xintercepts <- "Delta became\ndominant"
n_mult <- c(n_mult, 0.75)
k_print <- c(k_print, 6)
index <- c(index, 1)
}
if (omicron_start <= max_date) {
xintercepts <- c(xintercepts, omicron_start)
names_xintercepts <- c(names_xintercepts, "First cases of\nOmicron detected")
n_mult <- c(n_mult, 0.75)
k_print <- c(k_print, 1)
index <- c(index, 2)
}
if (delta_end <= max_date) {
xintercepts <- c(xintercepts, delta_end)
names_xintercepts <- c(names_xintercepts, "50% of cases\nOmicron")
n_mult <- c(n_mult, 0.25)
k_print <- c(k_print, 1)
index <- c(index, 3)
}
names(xintercepts) <- names_xintercepts
# xlabels <- c(min_date, xintercepts, max_date)
ann_text <- tibble(
date = xintercepts,
n = n_mult*max(data_tte_long$n),
k = factor(k_print, levels = 1:K),
lab = names(xintercepts),
lab_col = col_palette[1:length(xintercepts)]
)
p <- data_tte_long %>%
ggplot(aes(x = date, y = n)) +
geom_bar(stat = "identity", alpha = 0.5, width=1) +
geom_vline(
data = bind_rows(
lapply(
1:max(as.integer(as.character(data_tte_long$k))),
function(x)
ann_text %>% mutate(k=x)
)
) %>%
mutate(across(k, factor)),
aes(xintercept = date, colour = lab_col),
linetype = "dashed") +
labs(
x = "Date of follow-up (during 2021)",
y = "Number of individuals followed up (x 1000)"
) +
facet_grid(k~.) +
geom_label(
data = ann_text,
aes(label = lab, colour = lab_col),
size = 3
) +
scale_color_manual(
values = col_palette[index]
) +
scale_x_date(
date_breaks = "1 month",
date_labels = "%d %b",
limits = c(min_date - days(14), max_date + days(14))
) +
theme_bw() +
theme(
axis.text.x = element_text(angle = 45, vjust = 0.5),
axis.title.x = element_text(
size=10,
margin = margin(t = 10, r = 0, b = 0, l = 0)
),
axis.title.y = element_text(size=10, margin = margin(t = 0, r = 10, b = 0, l = 0)),
legend.position = "none"
)
ggsave(p,
filename = here::here("output", "tte", "images", glue("check_fu_{s}.png")),
width=15, height=20, units="cm")
}