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library(tidyverse)
library(knitr)
library(dlookr)
library(xlsx)
library(ggpubr)
library(corrplot)
data <- read_csv("../csv/visits_all.csv")
class_correct <- data %>%
na_if("NULL") %>%
drop_na(vaccine_resp) %>%
group_by(donor_id, visit_year) %>%
summarise(
post = if_else(any(visit_type_hai == 'post'), TRUE, FALSE),
internal = visit_internal_id,
hai_type = visit_type_hai,
gmt = geo_mean,
fold_change = as.numeric(d_geo_mean),
response = vaccine_resp,
)
post_gmt_vec <- class_correct %>%
group_map(
~ {
if (any(.$hai_type == 'post')) {
rep(.$gmt[.$hai_type == 'post'] >= 40, nrow(.))
} else {
rep(NA, nrow(.))
}
}
)
post_gmt_vec
post_gmt_vec <- unlist(post_gmt_vec)
class_correct <- class_correct %>%
ungroup() %>%
mutate(
check_post = if_else(post, 'Post visit', 'No post visit'),
post_gmt = post_gmt_vec,
response = recode(factor(response), '0'='Low', '1'='High'),
check_correct = if_else(
(!is.na(post_gmt) & (!post_gmt | fold_change < 4) & response == 'Low') |
(!is.na(post_gmt) & (post_gmt & fold_change >= 4) & response == 'High'),
'Correct clasification',
'False classification'
),
)
seasonal_classification_data <- class_correct %>%
group_by(donor_id, visit_year) %>%
summarise(
response=response,
check_correct=check_correct,
check_post
) %>%
ungroup() %>%
distinct()
seasonal_classification_plot <- seasonal_classification_data %>%
ggplot(aes(response, fill = response)) +
geom_bar() +
geom_text(stat='count', aes(label=paste("n = (",stat(count),")")), vjust= +1) +
facet_grid(factor(check_correct) ~ factor(check_post)) +
theme_pubr() +
labs(title="Seasonal classification of donors by correctness and post visit")
seasonal_classification_plot
ggsave("../images/season_classification.png", seasonal_classification_plot, width = 2 * 15, height = 19, units = "cm")
incorrect_cases <- class_correct %>%
filter(check_correct == "False classification")
write.xlsx(incorrect_cases, "../incorrect_visits.xlsx", sheetName="sheet1", col.names=T, row.names=T, append=F)
desc <- data %>%
na_if("NULL") %>%
mutate(across(everything(), as.numeric)) %>%
mutate(geo_mean = na_if(geo_mean, 0)) %>%
select(age, cmv_status, ebv_status, bmi, vaccine, geo_mean, d_geo_mean, vaccine_resp, total_data) %>%
describe() %>%
column_to_rownames("variable") %>%
t() %>%
as_tibble(rownames="stat") %>%
slice(1:8) %>%
mutate(across(2:10, round, 1)) %>%
kable(format = "latex", booktabs = TRUE)
clip <- pipe("xclip -selection clipboard", "w")
write(desc, file = clip)
close(clip)
corr_pre <- data %>%
select(visit_type_hai, age, vaccine, geo_mean, d_geo_mean, vaccine_resp, total_data) %>%
na_if("NULL") %>%
mutate(across(2:length(.), as.numeric)) %>%
filter(!is.na(vaccine_resp) & !is.na(vaccine) & !is.na(d_geo_mean) & !is.na(vaccine_resp) & visit_type_hai == "pre") %>%
select(-visit_type_hai)
corr_pre
library(gridGraphics)
grab_grob <- function(){
grid.echo()
grid.grab()
}
corr_post <- data %>%
select(visit_type_hai, age, vaccine, geo_mean, d_geo_mean, vaccine_resp, total_data) %>%
na_if("NULL") %>%
mutate(across(2:length(.), as.numeric)) %>%
filter(!is.na(vaccine_resp) & !is.na(vaccine) & !is.na(d_geo_mean) & !is.na(vaccine_resp) & visit_type_hai == "post") %>%
select(-visit_type_hai)
corr_post
png(file = "../images/corr_plot_visits_pre_complete.png",
height = 1.5 * 19,
width = 2 * 15,
res = 1200,
units = "cm",
)
par(mfrow=c(2,1))
M <- cor(corr_pre)
corrplot.mixed(M)
M <- cor(corr_post)
corrplot.mixed(M)
dev.off()
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