Slide 14 - daily emergency access performance, Jan to July 2015

gistfile1.txt

library(tidyverse)
library(lubridate)

data <- read.csv("df.csv", header = TRUE)

data$X <- NULL

#######################################################

# Code that converts all the columns names to lowercase 

df_tolower <-  function(x) { 
  colnames(x) <- tolower(colnames(x)) 
  x
  
}

#####################################################

df <- df_tolower(data )


################################################################################################################
# subseting data set ###########################################################################################
################################################################################################################
dt_rmds<- df[c("pat_code","start_datetime", "end_datetime", "ward_code", "spell.number", "episode.order")]

colname_ct <- function(df, colname){
  df[,colname] = as.POSIXct(df[,colname])
  df
}


dt_rmds_ct <- dt_rmds %>%
  colname_ct("start_datetime") %>%
  colname_ct("end_datetime")

str(dt_rmds_ct)


#############################
# selecting the jan - july ##
#############################

dt_jan_jul <- subset(dt_rmds_ct, (start_datetime >= "2015-01-01 00:00:00" & start_datetime <= "2015-07-27 23:59:00") | 
                     (end_datetime >= "2015-01-01 00:00:00" & end_datetime <= "2015-07-27 23:59:00"))


##########################################################
##########################################################
# calculating total time by using difftime    ############
# length of stay for the emergency department ############
##########################################################
##########################################################
# creating a new variable - under 4 hrs and above 4 hrs ##
##########################################################



dt_los <- dt_jan_jul %>%
  filter(ward_code == "A&E") %>%
  mutate(Los = difftime(end_datetime, start_datetime, units = c("min"))) %>%
  mutate(Time = as.Date(end_datetime)) %>%
  mutate(One_week = lubridate::round_date(dt_jan_jul$Time, "7 days")) %>%
  select(pat_code, Los, Time, One_Week)


dt_categ_4hr_perf <- dt_los %>%
  mutate(hr_perf = case_when(
  Los <= 240 ~ "under_4hrs", 
  Los >= 240 ~ "above_4hrs"))  # https://dplyr.tidyverse.org/reference/case_when.html 
                                 # This function allows you to vectorise multiple if and else if statements. 
                                   # It is an R equivalent of the SQL CASE WHEN statement.
                                   # 240 equivalent of 4 hrs -> creating a new variable with 4 hrs or above 4 performance



sum_4hrs_perf <- dt_categ_4hr_perf %>%
  group_by(Time, hr_perf) %>%
  dplyr::summarise(Count = n()) %>%
  spread(hr_perf, Count) %>%
  mutate(N = under_4hrs + above_4hrs)
  
#########################
# Applying the qicharts #
#########################


#Readm,
plot3 <- qicharts2::qic(Time, under_4hrs, 
                        n = N,
                        data = tail(sum_4hrs_perf,100),
                        chart = 'p',
                        ylab = "percent",
                        show.grid = TRUE,
                        #freeze = 1,
                        #part = 11,
                        multiply= 100,
                        #x.format = "%d-%m-%Y", 
                        x.angle = 45,
                        xlab = " Compliance with 4hr emergency care standard, %",
                        title = "daily 4 hr emergency access performance, January to July 2015") 

plot3