The R function below is a partial solution to assignment 3 part 2. I simplified the data file and renamed the outcome columns to HeartAttack, HeartFailure and Pneumonia. I also ignored the requirement to sort hospital names in the situation where there is a tie for best hospital. 

The first problem to solve is how to fail the function in the case where one or both of the parameters is invalid. You are required to use the stop() function. This can be done as:

best <- function(state, outcome){
  fullData <- read.csv("outcome-of-care-measures-short.csv")
  validStates <- c("AL", "TX")
  if (state %in% validStates){
     #body of function
  }else{stop("invalid state")}
}

I have simplified the code by only including two states in my list of valid states. If a different state or an invalid state such as 'BB' is entered the stop() function throws an error and prints out the message: 'invalid state', for example the following :

> bt <- best("AZ", "HeartAttack")
Error in best("AZ", "HeartAttack") : invalid state

I have not included an outcome check but it can be dealt with in a similar way with the addition of a nested if.

The main body of the function:

best <- function(state, outcome){
  fullData <- read.csv("outcome-of-care-measures-short.csv")
  validStates <- c("AL", "TX")
  if (state %in% validStates){
     stateData <- fullData[fullData$State == state,]
     if(outcome == "HeartAttack"){
       finalData <- stateData[stateData$HeartAttack != "Not Available",]
       ordered <- finalData[order(finalData$HeartAttack, decreasing=F),]
     }
     if(outcome == "HeartFailure"){
       finalData <- stateData[stateData$HeartFailure != "Not Available",]
       ordered <- finalData[order(finalData$HeartFailure, decreasing=F),]
     }
     if(outcome == "Pneumonia"){
       finalData <- stateData[stateData$Pneumonia != "Not Available",]
       ordered <- finalData[order(finalData$Pneumonia, decreasing=F),]
     }
     bestHospital <- head(ordered, 1)
     return(bestHospital[1])
  }else{stop("invalid state")}
}

The following was inspired by the book Python for Informatics by Charles Severance. The mbox file is available here. Information on mbox files is available online. I used notepad++ to write the code on a Windows 8.1 laptop, interaction with the code is via the command line. The following Youtube video explains how to set up a development environment.
Before writing any code it is always good to have a clear idea what the code needs to do. In this case I want to do the following: count the number of emails included in the file, create a list of the email addresses of all the unique senders, write the information to an output text file.

1. import re
2. fhand = open('mbox.txt')
3. names = []
4. fout = open('output.txt', 'w')
5. count = 0
6. for line in fhand:
#count the number of emails sent
7.   words = line.split()
8.   if len(words) > 0 :
9.      if words[0] == 'From':
10.         count = count + 1
#extract email addresses of all unique senders 
11.   line = line.rstrip()
12.   x = re.findall('From [a-zA-Z0-9]\S*@\S*[a-zA-Z]', line)
13.   if len(x) > 0: 
14.      if x not in names :
15.        names.append(x)
16.        sender = str(x) + '\n'
17.        fout.write(sender)
18. fout.write('There are: ' + str(len(names)) + ' unique senders in the file \n')
19. fout.write(str(count) + ' sent emails are included in the file')
20. fout.close()
21. print ('The output has been written to the file: output.txt')

explanation:
line 1: the code will use a regular expression to extract data from the file so need to import re
line 2: open the mbox file
line 3: create an empty list
line 4: open the output file for writing
line 5: initialise a variable which will be incremented for each email found in the mbox file
line 6 to line 10 counts the number of emails in the file, this part relies on the fact that mbox files always start each email message with the 'from line' i.e. a line which has the word From (uppercase F followed by lower case r, o, and m) followed by one space followed by the address of the sender.
line 11 to line 17 extracts the senders' addresses and writes them to the output file
lines 18 and 19 write some summary statistics to the output file
line 20: it is good practice to close the file after writing
line 21: the code is run from the command line, the final print statement informs the user that code execution has completed successfully and the output file has been generated.

The raw data (for Leeds City Centre) is available here. A network of CCTV cameras is used to track the number of people passing through an area per hour. The data is provided in the form of csv files. As an example I am taking one file for September 2014. I wrote and ran the R commands on the command line rather than in RStudio.

The first step is to read the data into memory. Since I saved the data file in my working directory (you can get this using the function getwd()) I can just use the read.csv() function and pass in the name of the file:
myData <- read.csv("monthly-data-feed-sept-2014-20141009.csv")
The next step is to extract the data just for one location, in this case the data for Briggate
brigData <- myData[myData$LocationName == "Briggate",]
Then I can narrow this data down to Monday
brigData_Mon <- brigData[brigData$Weekday == "Monday",]
The data can now be plotted:
plot(brigData_Mon$Hour, brigData_Mon$InCount)

I produced similar charts for the other days of the week. R can also produce summary statistics for the data

Summary data such as max and min values for particular days can be obtained from:
max(brigData_Mon$InCount) and min(brigData_Mon$InCount)