gabrielburcea

hashed_id <- function(x, salt){
  y <- paste(x, salt)
  y <- sapply(y, function(X) digest(X, algo = "sha1"))
  as.character(y)
}

data$PseudoID <- hashed_id(data$PseudoID, "somesalt1234")

data$Postcode.Sector <- hashed_id(data$Postcode.Sector, "somesalt1234")

gabrielburcea

plt_month <-ggplot(plt_wkd_mean, aes(x = Weekday, y = Average))                             
plt_month + geom_bar(stat = "identity", position = "identity", alpha = 0.5, width = 0.4,  fill= "red")  +
  #geom_line()+
  #geom_point()+
  scale_y_continuous(expand = c(0, 0), limits = c(0, 200)) +
  xlim("Mon", "Tue", "Wed", "Thu", "Fri", "Sat", "Sun") +
  labs(title = "Chelsea: Montly Occupancy, 1st of Jan to 31st of Dec 2013",
       subtitle = "Average Occupancy (medical patients) by Days of the Week.
Note: results are intended for management information only",
       y = "Average - Occupancy", x = "Day of the Week",  caption = "CLAHRC - NIHR") + #caption = "Source: Imperial Data Dive - Hackaton") +

gabrielburcea

library(tidyverse)
library(lubridate)

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

data$X <- NULL

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

# Code that converts all the columns names to lowercase 

gabrielburcea

###################################################
# Arrivals vs Occupancy -  #####################
# for one month - from 01-03-2015 to 27-04-2015 ###
###################################################

##################################################
# loading the libraries 
##################################################

library(tidyverse)

gabrielburcea

###################################################
# A&E - arrivals - March to April, 27th, 2013 ##
###################################################

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

gabrielburcea

##############################
# Admissions and Discharges 
##############################

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

head(data, 10)

gabrielburcea

####################################
# Readmission_7days ################
####################################

library(tidyverse)
library(lubridate)

data <- read.csv("/df.csv")

data$X <- NULL

gabrielburcea

four_hrs_perf_data_clean <- function(df) {
  
  colname_ct <- function(df, colname){
    df[,colname] = as.POSIXct(df[,colname])
    df
  }
  
  
  df %>%
    dplyr::select("PAT_CODE", "START_DATETIME", "END_DATETIME", "WARD_CODE", "episode.order", "spell.number") %>%

gabrielburcea

# Creating a new variable out of two 

data_indices <- df_subs %>%
   mutate(sex_patient_class = case_when(
     sex == "Female" & patient.class == "Not_Admitted"  ~ "female_not_admitted",
     sex == "Female" & patient.class == "ORDINARY ADMISSION" ~ "female_admitted",
     sex == "Male" & patient.class == "Not_Admitted" ~ "male_not_admitted",
     sex == "Male" & patient.class == "ORDINARY ADMISSION"  ~ "male_admitted"
     ))

gabrielburcea

df_subset <- df %>% 
  filter(spell.type == "Emergency" &  sex != "Not Specified", 
         end_datetime >= "2012-09-29" & end_datetime <= "2013-09-29") %>%
  mutate(as.Date(end_datetime)) %>%
  replace_na(list(patient.class = "Not_Admitted")) %>%
  select(pat_code, end_datetime, age_band, sex, patient.class, spell.type)