starpu_ab_prescribing_os.R

# # # # # # # # # # # # # # # # # # # # #
# This script:
# Generate a plot to show overall antibiotics prescribing rate by month after star-pu adjustment
# By practice, by month, per 1000 patient
# # # # # # # # # # # # # # # # # # # # #

## Import libraries---

library('tidyverse')
library("ggplot2")
library('dplyr')
library('lubridate')

# impoprt data
df <- read_csv(
  here::here("output", "measures", "measure_STARPU_antibiotics_os.csv"),
  col_types = cols_only(

  practice = col_double(),
      sex = col_character(),
      age_cat = col_character(),
      antibacterial_brit = col_double(),
      population = col_double(),
      value = col_double(),
    # Date
    date = col_date(format="%Y-%m-%d")
    
  ),
  na = character()
  )

df$date <- as.Date(df$date)
df$cal_mon <- month(df$date)
df$cal_year <- year(df$date)

starpu <- df %>% filter(practice >0)



measurestar <- starpu%>%mutate(adjustedvalue= case_when(age_cat=="0-4" ~ value*0.8,
                                                  age_cat =="5-14"& sex=="M"~value*0.3,
                                                  age_cat =="5-14"& sex=="F"~value*0.4,
                                                  age_cat =="15-24"& sex=="M"~value*0.3,
                                                  age_cat =="15-24"& sex=="F"~value*0.6,
                                                  age_cat =="25-34"& sex=="M"~value*0.2,
                                                  age_cat =="25-34"& sex=="F"~value*0.6,
                                                  age_cat =="35-44"& sex=="M"~value*0.3,
                                                  age_cat =="35-44"& sex=="F"~value*0.6,
                                                  age_cat =="45-54"& sex=="M"~value*0.3,
                                                  age_cat =="45-54"& sex=="F"~value*0.6,
                                                  age_cat =="55-64"& sex=="M"~value*0.4,
                                                  age_cat =="55-64"& sex=="F"~value*0.7,
                                                  age_cat =="65-74"& sex=="M"~value*0.7,
                                                  age_cat =="65-74"& sex=="F"~value*1.0,
                                                  age_cat =="75+"& sex=="M"~value*1.0,
                                                  age_cat =="75+"& sex=="F"~value*1.3,
                                                  TRUE ~ 0))

measurstarpu=measurestar%>%
  group_by(cal_mon,cal_year)%>%
  mutate(mean_prescrib=mean(adjustedvalue*1000,na.rm = TRUE))

starpu_line_os <- ggplot(measurstarpu, aes(x=date))+
  geom_line(aes(y=mean_prescrib),color="steelblue")+
  geom_point(aes(y=mean_prescrib),color="steelblue")+
  geom_vline(xintercept = as.numeric(as.Date("2019-12-31")))+
  geom_vline(xintercept = as.numeric(as.Date("2020-12-31")))+
  labs(
    title = "Star-pu adjusted antibiotics prescribing rate by month - OS",
    subtitle = "10/2020 - 10/2021",
    caption = "TPP Practices",
    x = "Time",
    y = "star-pu adjusted prescribing rate"
  )
starpu_line_os


ggsave(
  plot= starpu_line_os,
  filename="starpu_line_os.png", path=here::here("output"),
)
#star-pu boxplot                                        
starpu_box_os <- ggplot(measurestar, aes(group=date,x=date,y=adjustedvalue*1000)) + 
  geom_boxplot()+
  geom_vline(xintercept = as.numeric(as.Date("2019-12-31")))+
  geom_vline(xintercept = as.numeric(as.Date("2020-12-31")))+
  labs(
    title = "Star-pu adjusted antibiotics prescribing rate by practice - OS",
    subtitle = "10/2020 - 10/2021",
    caption = "TPP Practices",
    x = "Time",
    y = "star-pu adjusted prescribing rate"
  )
starpu_box_os

ggsave(
  plot= starpu_box_os,
  filename="starpu_box_os.png", path=here::here("output"),
)