Homework 1

homework
Due date

April 17, 2026

Modified

April 16, 2026

Due on Thursday 16 April at 11:59 PM

Read these instructions before starting your homework and follow them carefully. See the end of this assignment for a checklist of components that your assignment must have at minimum to qualify for revision/resubmission. Only submit the items in that list, in the order requested.

Part 1. Tasks

Note

You will not need to submit any materials for tasks, but you are expected to complete the material/steps.

Task 1. Read the class guidelines and policies on using AI

If you think you might use any AI tools (e.g. Copilot, ChatGPT, Claude), read the guidelines and policies before starting your assignment.

Task 2. Set up your folders and .Rproj file

a. Create a new folder for this homework assignment within your ENVS-193DS folder.

Within the ENVS-193DS folder you set up, create a new folder for Homework 1. Name it whatever you want (a logical name could be homework-01).

b. Download the files from Canvas

Download the homework files from Canvas into your homework folder on your computer. This includes:

  • the homework template
  • glacial_volume_loss.csv
  • glacial_volume_loss_copy.csv

c. Create an Rproject for this homework assignment

Create an Rproj file within your homework-01 folder. If you need help with this, watch the “Creating an Rproject” video on Canvas.

Task 3. Set up your code.

At the top of the template (or your own Quarto doc):

  • Load the tidyverse package. Make sure the messages will not show up in your final document.
library(tidyverse)
  • Read in the data as an object called glaciers.
glaciers <- read_csv("glacial_volume_loss_copy.csv")

Task 4. Read about metadata from NOAA’s National Centers for Environmental Information

Read the overview page on metadata here to understand what metadata is.

Then, click through to the “Introduction to Metadata” page (under “Learn”) and read the questions that you should be able to answer with metadata.

You are now ready to start your homework!

Part 2. Problems, code, and figures

Problem 1. Measures of central tendency and data spread

Snout-to-vent length (SVL, measured in mm) is a common characteristic to measure to characterize frog size. You measure the SVL for 7 Baja California treefrogs (Pseudacris hypochondriaca):

\[ 36, 33, 32, 28, 36, 31, 33 \]

  1. In one sentence, categorize this data set: what type of data did you collect, and why is it that type?
  2. Calculate the sample mean, showing your work with annotated code. In writing, express your answer with the correct units and round to the nearest whole number.
  3. Calculate the sample variance, showing your work with annotated code. In writing, express your answer with the correct units and round to the nearest whole number.
  4. Calculate the sample standard deviation, showing your work with annotated code. In writing, express your answer with the correct units and round to the nearest whole number.

Problem 2. Visualizing data

In this problem, you’ll work with data collected by the National Snow and Ice Data Center on glacial mass and sea level rise.

Before you start this problem, read about the data here.

Questions:

  1. Open up the two data files (glacial_volume_loss_copy.csv and glacial_volume_loss.csv) in Excel (or another program, if you use something different). Look at the files side-by-side. In one sentence, explain how the data files are different.

  2. Using the “Introduction to Metadata” information from Task 3, choose one question from the examples for “Who”, “What”, “Why”, “Where”, “When” and “How”. For example, there are 5 examples under “How”; you should choose one of those examples to address.

Answer the questions you chose using the information in the User Guide on the data page.

Each response should have the “question” (who, what, why, where, when, how), the example (“Who collected and processed the data?”), and your response. Format each as:

  • Who: Who collected and processed the data? [insert response here]
  • What: What are the data about? [insert response here]
  • Why: Why were the data collected? [insert response here]
  • Where: Where are the data located? [insert response here]

and so on. You should have one response for each question and example.

Not all the examples from Task 3 will be relevant to this dataset. Read the examples and choose the most relevant ones based on what you learn about the dataset from the metadata and the user guide from the National Snow and Ice Data Center.

On paper, sketch out the axes for the histogram and the scatterplot. Label the axes, and write down the columns in the data frame you will need to use to make the figures. Draw the bars of the histogram and the points of the scatterplot.

By doing this before you code up your figure, you’ll be able to gain some intuition for what your figure should look like. You can then check your work against what you thought based on your drawing.

  1. Create a histogram of annual sea level rise using ggplot(). Make sure you:
  • label the x- and y-axes
  • choose the correct number of bins
  • use a different fill from the default and make the borders of the columns black
  • use a different ggplot theme from the default
  1. Create a timeseries plot with points and a line representing cumulative sea level rise through time using ggplot(). Make sure you:
  • label the x- and y-axes
  • choose a different color from the default for the points and line
  • use a different ggplot theme from the default

Problem 3. Personal data

This quarter, you’ll collect data from your own life to see how data science concepts are part of your daily existence. For this homework assignment, you’ll plan your personal data project. The data you collect:

  • has to be something you can get at least 30 observations on between week 4 and week 9 (e.g. minutes to get from ENVS 193DS to your next class, not number of shark views per week)
  • has to be something that you could actually remember to write down (e.g. liters of water consumed in a day, not time spent on tiktok)
  • has to be be shaped by a question
  • has to include variables that would be appropriate to share with the class

For your idea, you should:

  1. articulate a question
  2. describe your response variable (i.e. your variable of interest) and its units
  3. describe your predictor variable and its units or levels
  4. describe what variables you should measure or record that indicate the time of the observation (for example: date or time of day)
  5. describe 4 additional variables you think you should measure or record that could also influence your response variable and what their effects might be
  6. describe what type of data all your variables are with units
  7. describe the sources of all your variables (e.g. phone step tracker, screen time tracker, self)
  8. describe when you would take down data for an observation
  9. design a data sheet with some example data: what are the columns and what are the rows?
  1. Question: Does my run length differ across types of run?
  2. My response variable is length of run, measured in miles.
  3. My predictor variable is run type (road, trail, or mix).
  4. I would record the date and time of day of each run.
  • duration: a longer time period spent running would lead to a longer distance run
  • average pace: a faster average pace might lead to a shorter run length because I am running faster but will likely get tired more quickly.
  • elevation: elevation would likely influence run length because the higher the elevation gain, the more strenuous the run. However, the run would likely be shorter because I would get tired.
  • run location: run location might influence how long I run for; if I run around the neighborhood, I might go for a shorter run than if I was at NCOS or in the front country because there are more places to explore.
  • date (yyyy-mm-dd): continuous
  • time of day (hh:mm, 24 hour time): continuous
  • duration (minutes and seconds, in length of time): continuous
  • length (miles): continuous
  • average pace (min/mile): continuous
  • elevation gain (feet): continuous
  • type of run (road, trail, mix): categorical
  • run location (neighborhood, front country, NCOS, other): categorical

  1. Date, time of day, duration, length, average pace, and elevation are all variables that I could get from Strava (a running tracking app). I would assign type of run and run location myself.

  2. I would record my data after every run.

Using ordinal scales

Lots of folks like to assign values to “productivity”, “restedness”, etc.

For example, you could assign a study session some number between 1-5 in terms of productivity, where 1 is not at all productive and 5 is very productive. Alternatively, you might assign character levels “high”, “medium”, and “low” to daily energy level.

If you plan on using a scale defined by numbers or characters, you need to clearly articulate in part f:

  • what each number/character means (e.g. from a scale from 1-5, what does 1, 2, 3, 4, 5, mean), and
  • how you would tell the difference between levels (e.g. how would you tell the difference between “high” and “low”)

Usually, this means you will have to determine what defines “productivity”. Is it number of pages you wrote in a paper? Does it mean the number of minutes spent in a word doc or R script? You have to decide what numerical tools you will use to assign a level to each “study session”.

You will receive feedback on your ideas after you turn in your assignment. That means that you should be able to start collecting data by the end of week 4, if not sooner.

Problem 4. Setting up statistical critique

Throughout the quarter, you’ll engage in a critique of statistical methods for a published paper. Some methods are appropriate for the data and research questions, and some are not. You’ll be the judge.

For this homework assignment, you will find 3 candidate papers for your critique. Find 3 papers that speak to your interests - the paper could be on human health, plant restoration, agroecology, or more. Anything you might be interested in within the realm of environmental studies is fair game. Not all 3 papers have to be on the same topic.

  1. For each paper, read the Abstract to get a general sense of what the paper is about. Then, read the Methods section, looking for information on statistical analysis. A paper is a good choice if it includes one of these terms (or something similar) in the analysis description:

    • t-test
    • Analysis of variance (ANOVA)
    • Mann-Whitney U
    • Kruskal-Wallis
    • Wilcoxon rank sum
    • Linear model or linear regression
    • Spearman correlation
    • Pearson correlation
    • logistic regression OR binomial model

  2. Once you’ve verified that your paper includes at least one of the above listed terms, find the digital object identifier (DOI), which is a unique identifier in the form of a URL for a paper. You will know it is a DOI if it has doi.org somewhere in the URL.

  3. Once you find the DOI for your paper, add it to the Google form. Repeat this for all three papers.

  4. In your homework document, list the papers in alphabetical order by author last name.

Your citations should take the form:
Last name, first name, et al. Year. “Paper title.” Journal title volume:issue. DOI.

Example:

Sanford, E., et al. 2019. “Widespread shifts in the coastal biota of northern California during the 2014–2016 marine heatwaves.” Scientific Reports 9:4216. https://doi.org/10.1038/s41598-019-40784-3.

Assignment checklist

Your assignment should:

Your responses should include:

Additionally, you should:

Lastly, check out the rubric on Canvas to see the component breakdown in more detail.

General formatting components

You will only receive full marks for annotations if you have:

  • meaningful comments on each line of visualization code and/or ggplot geom/theme call (not needed for each argument, though good to have)
  • meaningful comments on each function in any piping operations
  • meaningful comments in set up code to denote where packages and/or data are read in

You will only receive full marks for readability if:

  • all messages/warnings are hidden
  • all code is contained in code chunks (double check line breaks in comments once you render your document)
  • all text is where it’s supposed to be (all components like headers, main text, superscripts/subscripts, etc. show up correctly)
  • code includes carriage returns, spaces, etc. to make pipe operations and arguments clear
  • code includes consistent spacing and indents

Frequently Asked Questions

I’m having trouble rendering to PDF. What can I do?

Run quarto install tinytex in the Terminal (the tab next to the Console in RStudio).

I don’t know how to insert an image into a Quarto document. How do I do that?

Here is a resource for Quarto.

Where is the feedback for problem 4?

It is in the google sheet.

edited 2026-04-16: new link to google sheet