TDM 40100: Project 1 — 2023

Motivation: It’s been a long summer! Last year, you got some exposure command line tools, SQL, Python, and other fun topics like web scraping. This semester, we will continue to work primarily using Python with data. Topics will include things like: documentation using tools like sphinx, or pdoc, writing tests, sharing Python code using tools like pipenv, poetry, and git, interacting with and writing APIs, as well as containerization. Of course, like nearly every other project, we will be be wrestling with data the entire time.

We will start slowly, however, by learning about Jupyter Lab. In this project we are going to jump head first into The Data Mine. We will load datasets into the R environment, and introduce some core programming concepts like variables, vectors, types, etc. As we will be "living" primarily in an IDE called Jupyter Lab, we will take some time to learn how to connect to it, configure it, and run code.

Insider Knowledge

IDE stands for Integrated Developer Environment: software that helps us program cleanly and efficiently.

Context: This is our first project as a part of The Data Mine. We will get situated, configure the environment we will be using throughout our time with The Data Mine, and jump straight into working with data!

Scope: R, Jupyter Lab, Anvil

Learning Objectives
  • Read about and understand computational resources available to you.

  • Learn how to run R code in Jupyter Lab on Anvil.

  • Read and write basic (.csv) data using R.


The following questions will use the following dataset(s):

  • /anvil/projects/tdm/data/flights/subset/1991.csv

  • /anvil/projects/tdm/data/movies_and_tv/imdb.db

  • /anvil/projects/tdm/data/disney/flight_of_passage.csv

Setting Up to Work

This year we will be using Jupyter Lab on the Anvil cluster. Let’s begin by launching your own private instance of Jupyter Lab using a small portion of the compute cluster.

Navigate and login to using your ACCESS credentials (including 2-factor authentication using Duo Mobile). You will be met with a screen, with lots of options. Don’t worry, however, the next steps are very straightforward.

If you did not (yet) set up your 2-factor authentication credentials with Duo, you can set up the credentials here:

Towards the middle of the top menu, click on the item labeled My Interactive Sessions. (Depending on the size of your browser window, there might only be an icon; it is immediately to the right of the menu item for The Data Mine.) On the left-hand side of the screen you will be presented with a new menu. You will see that there are a few different sections: Bioinformatics, Interactive Apps, and The Data Mine. Under "The Data Mine" section, near the bottom of your screen, click on Jupyter Notebook. (Make sure that you choose the Jupyter Notebook from "The Data Mine" section.)

If everything was successful, you should see a screen similar to the following.

OnDemand Jupyter Lab
Figure 1. OnDemand Jupyter Lab

Make sure that your selection matches the selection in Figure 1. Once satisfied, click on Launch. Behind the scenes, OnDemand launches a job to run Jupyter Lab. This job has access to 1 CPU core and 1918 MB of memory.

As you can see in the screenshot above, each core is associated with 1918 MB of memory. If you know how much memory your project will need, you can use this value to choose how many cores you want. In this and most of the other projects in this class, 1-2 cores is generally enough.

Please use 4 cores for this project. This is almost always excessive, but for this project in question 3 you will be reading in a rather large dataset that will very likely crash your kernel without at least 3-4 cores.

We use the Anvil cluster because it provides a consistent, powerful environment for all of our students, and it enables us to easily share massive data sets with the entire Data Mine.

After a few seconds, your screen will update and a new button will appear labeled Connect to Jupyter. Click on this button to launch your Jupyter Lab instance. Upon a successful launch, you will be presented with a screen with a variety of kernel options. It will look similar to the following.

Kernel options
Figure 2. Kernel options

There are 2 primary options that you will need to know about.


The seminar kernel runs Python code but also has the ability to run R code or SQL queries in the same environment.

To learn more about how to run R code or SQL queries using this kernel, see our template page.


The seminar-r kernel is intended for projects that only use R code. When using this environment, you will not need to prepend %%R to the top of each code cell.

For now, let’s focus on the seminar kernel. Click on seminar, and a fresh notebook will be created for you.

The first step to starting any project should be to download and/or copy our project template (which can also be found on Anvil at /anvil/projects/tdm/etc/project_template.ipynb).

Open the project template and save it into your home directory, in a new notebook named firstname-lastname-project01.ipynb.

There are 2 main types of cells in a notebook: code cells (which contain code which you can run), and markdown cells (which contain comments about your work).

Fill out the project template, replacing the default text with your own information, and transferring all work you’ve done up until this point into your new notebook. If a category is not applicable to you (for example, if you did not work on this project with someone else), put N/A.

Make sure to read about and use the template found here, and the important information about projects submissions here.


Question 1 (1 pt)

  1. How many cores and how much memory (in GB) does Anvil’s sub-cluster A have? (0.5 pts)

  2. How many cores and how much memory (in GB) does your personal computer have?

For this course, projects will be solved using the Anvil computing cluster.

Each cluster is a collection of nodes. Each node is an individual machine, with a processor and memory (RAM). Use the information on the provided webpages to manually calculate how many cores and how much memory is available for Anvil’s "sub-cluster A".

Take a minute and figure out how many cores and how much memory is available on your own computer. If you do not have a computer of your own, work with a friend to see how many cores there are, and how much memory is available, on their computer.

Information about the core and memory capacity of Anvil "sub-clusters" can be found here.

Information about the core and memory capacity of your computer is typically found in the "About this PC" section of your computer’s settings.

Items to submit
  • A sentence (in a markdown cell) explaining how many cores and how much memory is available to Anvil sub-cluster A.

  • A sentence (in a markdown cell) explaining how many cores and how much memory is available, in total, for your own computer.

Question 2 (1 pt)

  1. Using Python, what is the name of the node on Anvil you are running on?

  2. Using Bash, what is the name of the node on Anvil you are running on?

  3. Using R, what is the name of the node on Anvil you are running on?

Our next step will be to test out our connection to the Anvil Computing Cluster! Run the following code snippets in a new cell. This code runs the hostname command and will reveal which node your Jupyter Lab instance is running on (in three different languages!). What is the name of the node on Anvil that you are running on?

import socket

system("hostname", intern=TRUE)


To run the code in a code cell, you can either press Ctrl+Enter on your keyboard or click the small "Play" button in the notebook menu.

Check the results of each code snippet to ensure they all return the same hostname. Do they match? You may notice that R prints some extra "junk" output, while bash and Python do not. This is nothing to be concerned about as different languages can handle output differently, but it is good to take note of.

Items to submit
  • Code used to solve this problem, along with the output of running that code.

Question 3 (1 pt)

  1. Run each of the example code snippets below, and include them and their output in your submission to get credit for this question.

Remember, in the upper right-hand corner of your notebook you will see the current kernel for the notebook, seminar. If you click on this name you will have the option to swap kernels out — no need to do this now, but it is good to know!

Practice running the following examples.

my_list = [1, 2, 3]
print(f'My list is: {my_list}')
%sql sqlite:////anvil/projects/tdm/data/movies_and_tv/imdb.db


awk -F, '{miles=miles+$19}END{print "Miles: " miles, "\nKilometers:" miles*1.609344}' /anvil/projects/tdm/data/flights/subset/1991.csv

In the above examples you will see lines such as %%R or %%sql. These are called "Line Magic". They allow you to run non-Python code in the seminar kernel. In order for line magic to work, it MUST be on the first line of the code cell it is being used in (before any comments or any code in that cell).

In the future, you will likely stick to using the kernel that matches the project language, but we wanted you to have a demonstration about "line magic" in Project 1. Line magic is a handy trick to know!

To learn more about how to run various types of code using the seminar kernel, see our template page.

Items to submit
  • Code from the examples above, and the outputs produced by running that code.

Question 4 (1 pt)

  1. Using Python, calculate how how much memory (in bytes) the A sub-cluster of Anvil has. Calculate how much memory (in TB) the A sub-cluster of Anvil has. (0.5 pts)

  2. Using R, calculate how how much memory (in bytes) the A sub-cluster of Anvil has. Calculate how much memory (in TB) the A sub-cluster of Anvil has. (0.5 pts)

"Comments" are text in code cells that are not "run" as code. They serve as helpful notes on how your code works. Always comment your code well enough that you can come back to it after a long amount of time and understand what you wrote. In R and Python, single-line comments can be made by putting # at the beginning of the line you want commented out.

Spacing in code is sometimes important, sometimes not. The two things you can do to find out what applies in your case are looking at documentation online and experimenting on your own, but we will also try to stress what spacing is mandatory and what is a style decision in our videos.

In question 1 we answered questions about cores and memory for the Anvil clusters. This time, we want you to convert your GB memory amount from question 1 into bytes and terabytes. Instead of using a calculator (or paper, or mental math for you good-at-mental-math folks), write these calculations using R and Python, in separate code cells.

A Gigabyte is 1,000,000,000 bytes. A Terabyte is 1,000 Gigabytes.

This link will point you to resources about how to use basic operators in R, and this one will teach you about basic operators in Python.

Items to submit
  • Python code to calculate the amount of memory in Anvil sub-cluster A in bytes and TB, along with the output from running that code.

  • R code to calculate the amount of memory in Anvil sub-cluster A in bytes and TB, along with the output from running that code.

Question 5 (1 pt)

  1. Load the "flight_of_passage.csv" data into an R dataframe called "dat".

  2. Take the head of "dat" to ensure your data loaded in correctly.

  3. Change the name of "dat" to "flight_of_passage", remove the reference to "dat", and then take the head of "dat" and "flight of passage" in order to ensure that your actions were successful.

In the previous question, we ran our first R and Python code (aside from provided code). In the fall semester, we will focus on learning R. In the spring semester, we will learn some Python. Throughout the year, we will always be focused on working with data, so we must learn how to load data into memory. Load your first dataset into R by running the following code.


dat <- read.csv("/anvil/projects/tdm/data/disney/flight_of_passage.csv")

Confirm that the dataset has been read in by passing the dataset, dat, to the head() function. The head function will return the first 5 rows of the dataset.



Remember — if you are in a new code cell on the , you’ll need to add %%R to the top of the code cell, otherwise, Jupyter will try to run your R code using the Python interpreter — that would be no good!

dat is a variable that contains our data! We can name this variable anything we want. We do not have to name it dat; we can name it my_data or my_data_set.

Run our code to read in our dataset, this time, instead of naming our resulting dataset dat, name it flight_of_passage. Place all of your code into a new cell. Be sure there is a level 2 header titled "Question 5", above your code cell.

In markdown, a level 2 header is any line starting with 2 hashtags. For example, Question X with two hashtags beforehand is a level 2 header. When rendered, this text will appear much larger. You can read more about markdown here.

We didn’t need to re-read in our data in this question to make our dataset be named flight_of_passage. We could have re-named dat to be flight_of_passage like this.

flight_of_passage <- dat

Some of you may think that this isn’t exactly what we want, because we are copying over our dataset. You are right, this is certainly not what we want! What if it was a 5GB dataset, that would be a lot of wasted space! Well, R does copy on modify. What this means is that until you modify either dat or flight_of_passage the dataset isn’t copied over. You can therefore run the following code to remove the other reference to our dataset.

Items to submit
  • Code to load the data into a dataframe called dat and take the head of that data, and the output of that code.

  • Code to change the name of dat to flight_of_passage and remove the variable dat, and to take the head of flight_of_passage to ensure the name-change worked.

Question 6 (2 pts)

Review your Python, R, and bash skills. For each language, choose at least 1 dataset from /anvil/projects/tdm/data, and analyze it. Both solutions should include at least 1 custom function, and at least 1 graphic output.

Your bash solution can be both plotless and without a custom function.

Make sure your code is complete, and well-commented. Include a markdown cell with your short analysis (1 sentence is fine), for each language.

Items to submit
  • Code used to solve this problem.

  • Output from running the code.

Question 7 (1 pt)

The module system, lmod, is extremely popular on HPC (High Performance Computing) systems. Anvil is no exception and uses lmod!

In a terminal, take a look at the modules available to you by default.

module avail

Notice that at the very top, you’ll have a list named: /anvil/projects/tdm/opt/lmod.

Now run the following commands:

module reset
module avail

Notice how the set of available modules changes! By default, we have it loaded up with some Datamine-specific modules. To manually load up those modules, run the following.

module use /anvil/projects/tdm/opt/core
module avail

Notice how at the very top, there is a new section named /anvil/projects/tdm/opt/core with a single option, tdm/default.

Go ahead and load up tdm/default.

module load tdm
module avail

It looks like we are (pretty much) back to where we started off! This is useful to know in case there is ever a situation where you’d like to SSH into Anvil and load up our version of Python with the packages we have ready-made for you to use.

To finish off this "question", run the following command and note in the result in the jupyter notebook.

which python3

Okay, now, load up our python/f2022-s2023 module and run which python3 once again. What is the result? Surprised by the result? Any ideas what this is doing? If you are curious, feel free to ask in Piazza! Otherwise, congratulations, you’ve made it through the first project!

Items to submit
  • The output from running which python3 before and after loading the python/f2022-s2023 module.

  • Any other comments you’d like to include.

Submitting your Work

Congratulations, you just finished your first assignment for this class! Now that we’ve written some code and added some markdown cells to explain what we did, we are ready to submit our assignment. For this course, we will turn in a variety of files, depending on the project.

We will always require a Jupyter Notebook file. Jupyter Notebook files end in .ipynb. This is our "source of truth" and what the graders will turn to first when grading.

You must double check your .ipynb after submitting it in gradescope. A very common mistake is to assume that your .ipynb file has been rendered properly and contains your code, markdown, and code output, when in fact it does not. Please take the time to double check your work. See here for instructions on how to double check this.

You will not receive full credit if your .ipynb file does not contain all of the information you expect it to, or it does not render properly in gradescope. Please ask a TA if you need help with this.

A .ipynb file is generated by first running every cell in the notebook (which can be done quickly by pressing the "double play" button along the top of the page), and then clicking the "Download" button from File  Download.

In addition to the .ipynb file, an additional file should be included for each programming language in the project containing all of the code from that langauge that is in the project. A full list of files required for the submission will be listed at the bottom of the project page.

Let’s practice. Take the R code from this project and copy and paste it into a text file with the .R extension. Call it firstname-lastname-project01.R. Do the same for each programming language, and ensure that all files in the submission requirements below are included. Once complete, submit all files as named and listed below to Gradescope.

Items to submit
  • firstname-lastname-project01.ipynb.

  • firstname-lastname-project01.R.


  • firstname-lastname-project01.sql.


Please make sure to double check that your submission is complete, and contains all of your code and output before submitting. If you are on a spotty internet connection, it is recommended to download your submission after submitting it to make sure what you think you submitted, was what you actually submitted.

In addition, please review our submission guidelines before submitting your project.

Here is the Zoom recording of the 4:30 PM discussion with students from 21 August 2023: