Assignment 2

For this assignment we will be working with real TTC Streetcar delay data coming from the City of Toronto. Details about the data can be found on the TTC Streetcar Delay Data page.

The data is stored in the zip file located here: https://pages.scinet.utoronto.ca/~afedosee/ttc-streetcar-delay-data-2014-2025.zip. This file contains Comma Separated Values (CSV) files.

To download and uncompress the data set, use the following commands at the Linux command line:

user@scinet assignment2 $ curl -O https://pages.scinet.utoronto.ca/~afedosee/ttc-streetcar-delay-data-2014-2025.zip
user@scinet assignment2 $ ls
ttc-streetcar-delay-data-2014-2025.zip
user@scinet assignment2 $ unzip ttc-streetcar-delay-data-2014-2025.zip
user@scinet assignment2 $ ls
data/                                   ttc-streetcar-delay-data-2014-2025.zip
user@scinet assignment2 $ ls data/
ttc-streetcar-delay-data-2014.csv ttc-streetcar-delay-data-2018.csv ttc-streetcar-delay-data-2022.csv
ttc-streetcar-delay-data-2015.csv ttc-streetcar-delay-data-2019.csv ttc-streetcar-delay-data-2023.csv
ttc-streetcar-delay-data-2016.csv ttc-streetcar-delay-data-2020.csv ttc-streetcar-delay-data-2024.csv
ttc-streetcar-delay-data-2017.csv ttc-streetcar-delay-data-2021.csv ttc-streetcar-delay-data-2025.csv
user@scinet assignment2 $

The files contain the TTC Streetcar Delay Data for the years 2014 to 2025. Each file contains the data corresponding to the year specified in its name, eg. ttc-streetcar-delay-data-2014.csv, ..., ttc-streetcar-delay-data-2025.csv. For consistency, please put your scripts in your assignment2 directory, but leave the data in the data directory.

Note that it is a good idea to do some initial exploration of the data (read the data in, use str() to examine the names of the columns) before you proceed to the next section.

Part 1

Write an R script, called processTTC.R, which performs the following steps.

1. Receives a file path as a command line argument. When running the script from the terminal, pass the file path as an argument: Rscript processTTC.R data/ttc-streetcar-delay-data-2014.csv. Inside the R script, use commandArgs(trailingOnly = TRUE) to read the argument, then use read.csv() to load the data into a dataframe. Example:

   args <- commandArgs(trailingOnly = TRUE)
ttc.data <- read.csv(args[1])

2. Prints which file is being processed.
3. Calculates and prints the total number of delays per incident type. For this you will need to find a way to automatically identify the different types of reported incidents (do not hard-code the incidents!), and loop over them to compute the total number for each incident. A useful function to assist with this is unique(). Use help() and example() to learn how to use it.
4. Calculates and prints the average minimum delay of streetcars due to a mechanical incident, ignoring unreported data.
5. Calculates and prints the route with the most delays in February. For this question, depending on your strategy, functions which might be helpful include as.character() (to convert variables to strings), substr() (to cut substrings out of strings), table() (to perform a frequency analysis on data), sort() (to sort things), and names() (to get the names from your table).

Your script should output the following message, when run from the shell terminal:

user@scinet assignment2 $ Rscript process.TTC.R data/ttc-streetcar-delay-data-2014.csv
Processing data from file:  data/ttc-streetcar-delay-data-2014.csv
Total number of delays per incident type:
	 Late Leaving Garage  --  1143
	 Utilized Off Route  --  516
	 Held By  --  1493
	 Investigation  --  1530
	 Mechanical  --  5107
	 General Delay  --  829
	 Emergency Services  --  272
	 Diversion  --  137
The average minimum delay of the streetcars due to a mechanical incident,
ignoring unreported data, is 7.838621 minutes.
The route with the most delays in February was route 504
----------------------------------------------------------------------------

Note that part c) is the only part that should have a loop. All other questions should be answered using slicing.

Make a note of the following code, which may inspire your answers for some of the above sections:

>
> a <- 1:10
>
> a > 7
[1] FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE TRUE TRUE
>
> sum(a > 7)
[1] 3
>
 

Part 2

Write a shell script named processALLyears.sh that loops over all CSV files in your directory and calls the previous R script so that all the years are processed sequentially. The following is the skeleton of a for loop in bash. This code should inspire your shell script.

for filename in data/*csv
do
    echo $filename
done

Start with this, remove and add the necessary commands so that this script executes your R script for all the data/ttc-streetcar-delay-data-20XX.csv files. You should assume that all the CSV files are in the data directory, the R script and the shell script in the directory one level above the data.

Be sure to comment your code, indent your code blocks, and use meaningful variable names.

Submit your processTTC.R and processALLyears.sh scripts.

Assignments will be graded on 10 points basis.
Due date is January 29th, 2026 at 11:59 p.m., with 0.5 point penalty per day for late submission until the cut-off date of February 5th, 2026 at 9:00 a.m.