# 11-4 Exercise 2

# Load data file

patientdata<-read.csv("SP11/ThreeMonths.csv", header=TRUE)

# Sum the total number of patients

# Set up counter for patientTotal (NOT a loop counter)

patientTotal<-0

# Use FOR loop - every record is included - loop variable is i

for (i in patientdata$Attendances) 
  {
      patientTotal<-patientTotal+i
}
patientTotal

# Sum the number of patients treated across 'Scattered' locations

# Set up counter for scatteredTotal (NOT a loop counter)

scatteredTotal<-0

for (i in 1:nrow(patientdata)) 
{
  currentArea<-patientdata[i,"Area"]
  currentAttendances<-patientdata[i,"Attendances"]
  if (currentArea=="Scattered")
  {
    scatteredTotal<-scatteredTotal+currentAttendances
  }
}
scatteredTotal







# Identify when the 85000th attendance occurred

# The data is not ordered by month, so a simple way to approach this
# is to sum the attendances for each month, then check each month
# in turn to see if the cumulative total has reached 85000 yet

# Set up a data frame to show the month and the total attendances
# for the month (this could also be done with just the vectors)

monthlyTotals<-c(0,0,0) # Attendances for June, July, August
monthNames<-c("June","July","August")
summary<-data.frame(month=monthNames,total=monthlyTotals) # Data frame

# Go through each row of data and add the attendance to the monthly total
# Use a FOR loop to handle every row of the data frame

for (i in 1:nrow(patientdata))
{
  # Identify month and attendance for that month
  currentMonth<-patientdata[i,"Month"]
  currentAttendance<-patientdata[i,"Attendances"]
  
  # Increment the total for the correct month in the summary data frame
  # There are three possible conditions (June, July, August) so we must
  # use three seperate IF statements rather than an IFELSE
  # There is a SWITCH statement we will explore later in the module
  
  if (patientdata[i,"Month"]=="Jun-19")# Update June Total
  {
    summary$total[1]<-summary$total[1]+currentAttendance 
  }
  if (patientdata[i,"Month"]=="Jul-19") # Update July Total
  {
    summary$total[2]<-summary$total[2]+currentAttendance 
  }
  
  if (patientdata[i,"Month"]=="Aug-19") # Update August Total
  {
    summary$total[3]<-summary$total[3]+currentAttendance 
  }
  
} # End of FOR loop

summary # Display the summary data frame (really useful for debugging)

# Check to see if the total has passed 85000; if so, print the total

currentMonth<-1 # Reassign currentMonth because it's a useful name
runningTotal<-0 # Overall cumulative total
i<-1 # loop variable

# Keep incrementing until the running total reaches a certain value
# This needs a WHILE loop as we don't know if it needs to process
# every row in the summary data frame

while(runningTotal<=85000) # Only run until condition is met
{
  runningTotal<-runningTotal+summary$total[i] # Update cumulative total
  i<-i+1 # increment the counter
}

# Output the month
# Use i-1 to access the right row because we incremented i after
# updating the running total in the WHILE loop

print(paste("The 85000th attendance occurred in ",summary$month[i-1]))







# Identify any hospitals that failed to treat 95% of patients in 
# under 12 hours in more than 1 month

# It's easiest to do this by creating a data frame that records T/F
# for each hospital for each month, then filtering it to identify
# those hospitals which miss the target 2 or 3 times

# Set up a data frame to show the hospital, the three months, and
# the number of failing months

hospitals<-unique(patientdata$Hospital) # UNIQUE removes duplicates

# The target columns will be calculated later, so use NA
juneTarget<-NA # These will recycle to fill the full table
julyTarget<-NA
augustTarget<-NA

# The number of failing months will be calculated later, so use NA
failMonths<-NA

# Build these columns into a data frame monthlyTargets

monthlyTargets<-data.frame(hospital=hospitals,
                           june=juneTarget,
                           july=julyTarget,
                           august=augustTarget,
                           fails=failMonths) # Data frame

# Display the data frame for debugging

monthlyTargets

# Go through each row of data and chack to see if 12 hour attendance
# is less than 92%. If it is, set the month target to FALSE, otherwise
# set the month target to TRUE

# Use a FOR loop to handle every row of the data frame

for (i in 1:nrow(patientdata))
{
  # Identify month
  currentMonth<-patientdata[i,"Month"]
  
  # Identify hospital
  currentHospital<-patientdata[i,"Hospital"]

  # Identify rowID of currentHospital in monthlyTargets
  rowID<-0
  j<-1
  
  # Loop through monthlyTarget rows until monthlyTarget$hospital
  # is the same as current hospital
  while(rowID==0)
  {
    #print(monthlyTargets$hospital[j])
    if (monthlyTargets$hospital[j]==currentHospital) 
    {
      rowID<-j  # Set the value of rowID; this will exit the loop
    }
    j<-j+1 # Increment j to look at the next rown in monthlyTarget
  }

  # Check the 95% target for the current row's month
  # There are three possible conditions (June, July, August) so we must
  # use three seperate IF statements rather than an IFELSE

  if (patientdata[i,"Month"]=="Jun-19") # Set June flag
  {
    monthID<-2 # Column 2
    flag<-ifelse(patientdata[i,"Under12Percentage"]<92,FALSE,TRUE)
  }

  if (patientdata[i,"Month"]=="Jul-19") # Set July flag
  {
    monthID<-3 # Column 3
    flag<-ifelse(patientdata[i,"Under12Percentage"]<92,FALSE,TRUE)
  }

  if (patientdata[i,"Month"]=="Aug-19") # Set August flag
  {
    monthID<-4 # Column 4
    flag<-ifelse(patientdata[i,"Under12Percentage"]<92,FALSE,TRUE)
  }

  # Fill in the appropriate month / hospital cell in monthlyTotals
  # with the current value of flag
  
  monthlyTargets[rowID,monthID]<-flag

} # End of FOR loop

monthlyTargets # Display the summary data frame (really useful for debugging)

# Finally calculate the number of times the hospital missed the target

for (i in 1:nrow(monthlyTargets)) # For each row in monthlyTargets
{
  totalMissed<-0 # Counter for months missed

  # Loop through columns 2,3,4 - june, july, august
  for (j in 2:4)
  {
    # If the value is FALSE, increment the totalMissed counter
    if (monthlyTargets[i,j]==FALSE) totalMissed<-totalMissed+1
  }
  
  # Assign totalMissed to the fail column of the data frame
  monthlyTargets$fails[i]<-totalMissed
}

# Display the final data frame

monthlyTargets