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figure_for_se_65.R
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figure_for_se_65.R
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### This script is for preparing the variables for broad spectrum antibiotics model ###
library("dplyr")
library("tidyverse")
library("lubridate")
library('stringr')
library("data.table")
library("ggpubr")
library("finalfit")
rm(list=ls())
setwd(here::here("output", "measures"))
df <- readRDS("cohort_1.rds")
df <- df %>% filter (age_group == "65+")
### prepare var ### without Amoxicillin ###
broadtype_1 <- c("Amoxicillin","Ampicillin","Co-amoxiclav","Moxifloxacin","Cefaclor","Cefadroxil",
"Cefuroxime", "Cefalexin","Cefazolin","Cefixime","Cefotaxime","Cefoxitin","Cefradine",
"Cefpirome","Ceftazidime","Ceftriaxone", "Cefprozil","Ciprofloxacin","Co-fluampicil",
"Doripenem","Ertapenem", "Cilastatin","Cefamandole","Levofloxacin" ,
"Meropenem" ,"Nalidixic acid","Norfloxacin", "Ofloxacin","Cefpodoxime","Cefepime")
broadtype_2 <- c("Ampicillin","Co-amoxiclav","Moxifloxacin","Cefaclor","Cefadroxil",
"Cefuroxime", "Cefalexin","Cefazolin","Cefixime","Cefotaxime","Cefoxitin","Cefradine",
"Cefpirome","Ceftazidime","Ceftriaxone", "Cefprozil","Ciprofloxacin","Co-fluampicil",
"Doripenem","Ertapenem", "Cilastatin","Cefamandole","Levofloxacin" ,
"Meropenem" ,"Nalidixic acid","Norfloxacin", "Ofloxacin","Cefpodoxime","Cefepime")
broadtype_3 <- c("Co-amoxiclav","Cefaclor","Cefadroxil","Cefixime","Cefotaxime","Ceftriaxone","Ceftazidime",
"Cefuroxime", "Cefalexin","Cefradine","Ciprofloxacin","Ofloxacin","Levofloxacin","Moxifloxacin")
### Broad spectrum 1
df$cal_year <- year(df$date)
df$cal_mon <- month(df$date)
df$time <- as.numeric(df$cal_mon+(df$cal_year-2019)*12)
df.broad <- df %>% filter(type %in% broadtype_1 )
df.broad_total <- df.broad %>% group_by(time) %>% summarise(
numOutcome = n(),
)
df.all <- df %>% group_by(time) %>% summarise(
numEligible = n(),
)
df.model <- merge(df.broad_total,df.all,by=c("time"))
df.model <- df.model %>% mutate(mon = case_when(time>=1 & time<=12 ~ time,
time>=13 & time<=24 ~ time-12,
time>=24 & time<=36 ~ time-24,
time>36 ~ time-36)) %>%
mutate(year = case_when(time>=1 & time<=12 ~ 2019,
time>=13 & time<=24 ~ 2020,
time>=24 & time<=36 ~ 2021,
time>36 ~ 2022)) %>%
mutate(day = 1)
df.model$monPlot <- as.Date(with(df.model,paste(year,mon,day,sep="-")),"%Y-%m-%d")
df.model <- df.model %>% mutate(value = numOutcome/numEligible)
df.model$value <- round(df.model$value,digits = 4)
df.model$numOutcome <- plyr::round_any(df.model$numOutcome, 5)
df.model$numEligible <- plyr::round_any(df.model$numEligible, 5)
p_1 <- ggplot(df.model, aes(y=value, x=monPlot)) +
geom_line(color = "coral2", size = 0.5)+
scale_y_continuous(labels = scales::percent,breaks=seq(0, 0.15, by = 0.0025))+
annotate(geom = "rect", xmin = as.Date("2021-01-01"),xmax = as.Date("2021-04-01"),ymin = -Inf, ymax = Inf,fill="grey80", alpha=0.5)+
annotate(geom = "rect", xmin = as.Date("2020-11-01"),xmax = as.Date("2020-12-01"),ymin = -Inf, ymax = Inf,fill="grey80", alpha=0.5)+
annotate(geom = "rect", xmin = as.Date("2020-03-01"),xmax = as.Date("2020-06-01"),ymin = -Inf, ymax = Inf,fill="grey80", alpha=0.5)+
labs(
title = "Broad-spectrum percentage_65+_1",
caption = "National lockdown time in grey background. ",
y = "Percentage",
x=""
)+
theme(axis.text.x=element_text(angle=60,hjust=1))+
scale_x_date(date_labels = "%m-%Y", date_breaks = "1 month")
ggsave(
plot= p_1,
filename="figure_broad_65_1.jpeg", path=here::here("output"),
)
df.model$value <- df.model$numOutcome/df.model$numEligible
write_csv(df.model, here::here("output", "figure_broad_65_1_table.csv"))
rm(df.broad_total,df.all,df.model,df.broad)
### Broad spectrum 2
df.broad <- df %>% filter(type %in% broadtype_2 )
df.broad_total <- df.broad %>% group_by(time) %>% summarise(
numOutcome = n(),
)
df.all <- df %>% group_by(time) %>% summarise(
numEligible = n(),
)
df.model <- merge(df.broad_total,df.all,by=c("time"))
df.model <- df.model %>% mutate(mon = case_when(time>=1 & time<=12 ~ time,
time>=13 & time<=24 ~ time-12,
time>=24 & time<=36 ~ time-24,
time>36 ~ time-36)) %>%
mutate(year = case_when(time>=1 & time<=12 ~ 2019,
time>=13 & time<=24 ~ 2020,
time>=24 & time<=36 ~ 2021,
time>36 ~ 2022)) %>%
mutate(day = 1)
df.model$monPlot <- as.Date(with(df.model,paste(year,mon,day,sep="-")),"%Y-%m-%d")
df.model <- df.model %>% mutate(value = numOutcome/numEligible)
df.model$value <- round(df.model$value,digits = 4)
df.model$numOutcome <- plyr::round_any(df.model$numOutcome, 5)
df.model$numEligible <- plyr::round_any(df.model$numEligible, 5)
p_1 <- ggplot(df.model, aes(y=value, x=monPlot)) +
geom_line(color = "coral2", size = 0.5)+
scale_y_continuous(labels = scales::percent,breaks=seq(0, 0.05, by = 0.0005))+
annotate(geom = "rect", xmin = as.Date("2021-01-01"),xmax = as.Date("2021-04-01"),ymin = -Inf, ymax = Inf,fill="grey80", alpha=0.5)+
annotate(geom = "rect", xmin = as.Date("2020-11-01"),xmax = as.Date("2020-12-01"),ymin = -Inf, ymax = Inf,fill="grey80", alpha=0.5)+
annotate(geom = "rect", xmin = as.Date("2020-03-01"),xmax = as.Date("2020-06-01"),ymin = -Inf, ymax = Inf,fill="grey80", alpha=0.5)+
labs(
title = "Broad-spectrum percentage_65+_2",
caption = "National lockdown time in grey background. ",
y = "Percentage",
x=""
)+
theme(axis.text.x=element_text(angle=60,hjust=1))+
scale_x_date(date_labels = "%m-%Y", date_breaks = "1 month")
ggsave(
plot= p_1,
filename="figure_broad_65_2.jpeg", path=here::here("output"),
)
df.model$value <- df.model$numOutcome/df.model$numEligible
write_csv(df.model, here::here("output", "figure_broad_65_2_table.csv"))
rm(df.broad_total,df.all,df.model,df.broad)
### Broad spectrum 3
df.broad <- df %>% filter(type %in% broadtype_3 )
df.broad_total <- df.broad %>% group_by(time) %>% summarise(
numOutcome = n(),
)
df.all <- df %>% group_by(time) %>% summarise(
numEligible = n(),
)
df.model <- merge(df.broad_total,df.all,by=c("time"))
df.model <- df.model %>% mutate(mon = case_when(time>=1 & time<=12 ~ time,
time>=13 & time<=24 ~ time-12,
time>=24 & time<=36 ~ time-24,
time>36 ~ time-36)) %>%
mutate(year = case_when(time>=1 & time<=12 ~ 2019,
time>=13 & time<=24 ~ 2020,
time>=24 & time<=36 ~ 2021,
time>36 ~ 2022)) %>%
mutate(day = 1)
df.model$monPlot <- as.Date(with(df.model,paste(year,mon,day,sep="-")),"%Y-%m-%d")
df.model <- df.model %>% mutate(value = numOutcome/numEligible)
df.model$value <- round(df.model$value,digits = 4)
df.model$numOutcome <- plyr::round_any(df.model$numOutcome, 5)
df.model$numEligible <- plyr::round_any(df.model$numEligible, 5)
p_1 <- ggplot(df.model, aes(y=value, x=monPlot)) +
geom_line(color = "coral2", size = 0.5)+
scale_y_continuous(labels = scales::percent,breaks=seq(0, 0.05, by = 0.0005))+
annotate(geom = "rect", xmin = as.Date("2021-01-01"),xmax = as.Date("2021-04-01"),ymin = -Inf, ymax = Inf,fill="grey80", alpha=0.5)+
annotate(geom = "rect", xmin = as.Date("2020-11-01"),xmax = as.Date("2020-12-01"),ymin = -Inf, ymax = Inf,fill="grey80", alpha=0.5)+
annotate(geom = "rect", xmin = as.Date("2020-03-01"),xmax = as.Date("2020-06-01"),ymin = -Inf, ymax = Inf,fill="grey80", alpha=0.5)+
labs(
title = "Broad-spectrum percentage_65+_3",
caption = "National lockdown time in grey background. ",
y = "Percentage",
x=""
)+
theme(axis.text.x=element_text(angle=60,hjust=1))+
scale_x_date(date_labels = "%m-%Y", date_breaks = "1 month")
ggsave(
plot= p_1,
filename="figure_broad_65_3.jpeg", path=here::here("output"),
)
df.model$value <- df.model$numOutcome/df.model$numEligible
write_csv(df.model, here::here("output", "figure_broad_65_3_table.csv"))
rm(df.broad_total,df.all,df.model,df.broad)