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library(tidyverse)
library(grid)
library(ggpubr)
library(patchwork)
p1 <- tibble(x = 1:10, y = 1:10) %>%
ggplot(aes(x, y)) +
geom_point() +
scale_y_reverse(breaks = seq(1, 10)) +
labs(y = NULL)
p2 <- tibble(ymin = c(0, 2.1, 7.1), ymax = c(1.9, 6.9, 10), fill = c("Gender", "Ethnicity", "CMV status")) %>%
ggplot() +
geom_rect(aes(xmin = 0, xmax = 1, ymin = ymin, ymax = ymax)) +
geom_text(aes(x = .5, y = (ymin + ymax) / 2, label = fill), angle = 90) +
scale_y_reverse(breaks = seq(1, 10), expand = expansion(mult = c(0, 0))) +
scale_x_continuous(breaks = c(0), expand = expansion(mult = c(0, 0))) +
guides(fill = FALSE) +
theme_void()
orig <- read_csv("../csv/donor_demo.csv")
data <- orig
tbl <- data %>%
mutate(`CMV status` = factor(
`CMV status`,
labels = c("CMV-", "CMV+", "CMV unknown")
)) %>%
na_if("NULL") %>%
replace_na(list(Ethnicity = "Unknown")) %>%
pivot_longer(
cols = c("Gender", "Ethnicity", "CMV status")
) %>%
group_by(
value, Response
) %>%
summarise(
count = n(),
) %>%
mutate(
total = if_else(Response == 1,
sum(data$Response == 1),
sum(data$Response == 0)
),
value = factor(value, levels = rev(c(
"Female",
"Male",
"Caucasian",
"Black or African American",
"Asian",
"Hispanic/Latino",
"Other",
"Unknown",
"CMV+",
"CMV-",
"CMV unknown"
))),
)
factors_demo <- tbl %>%
ggplot(aes(fill = value)) +
geom_bar(aes(x = count / total, y = value), stat = "identity", show.legend = F) +
theme_pubr() +
scale_x_continuous(labels = scales::percent_format(accuracy = 1)) +
labs(x = "Distribution (%)", y = "") +
facet_wrap(~ factor(Response,
labels =
c(
paste("Low responders (n=", sum(data$Response == 0), ")"),
paste("High responders (n=", sum(data$Response == 1), ")")
)
))
factors_demo <- p2 + factors_demo + plot_layout(widths= c(0.5,9))
data <- orig
tbl <- data %>%
select(Age, Response) %>%
mutate(
Response_text = factor(
Response,
labels = c("Low responders", "High responders")
)
) %>%
group_by(Age, Response_text) %>%
summarise(count = n(), Response = Response, Response_text = Response_text, Age = Age) %>%
mutate(
total = if_else(Response == 0, sum(data$Response == 0), sum(data$Response == 1)),
percentage = n() / total * 100
) %>%
ggplot(aes(x = Age, y = percentage, fill = factor(Response_text))) +
geom_polygon(show.legend = F) +
labs(x = "Age", y = "Percentage (%)") +
theme_pubr() +
facet_wrap(~Response_text)
age_responder <- tbl
figure <- ggarrange(
factors_demo,
age_responder,
ncol = 1,
nrow = 2,
labels = c("A", "B")
)
ggsave("../images/demographic.png", figure, width = 2 * 15, height = 19, units = "cm")
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