Lab 1: Foundations of a Data Workflow

Instructions

Welcome to your first lab! The goal of this assignment is to move beyond basic syntax and begin practicing a reproducible data analysis workflow.

Please complete the exercises below. Create a new .Rmd file and include the following at the top:

---
title: "Lab 1: Foundations of a Data Workflow"
author: "Your Name Here"
date: "`r Sys.Date()`"
output: html_document
editor_options: 
  chunk_output_type: console
  markdown: 
    wrap: 72
---

You should then be able to copy/paste everything below into your document.

When you are finished, click the Knit button to turn your work into an HTML document. You will submit both this .Rmd file and the final .html file.

---

Exercise 1: Loading Packages & Exploring Data

A major strength of R is its ecosystem of packages that add new functionality. We will use the tidyverse package in almost every analysis we do. The ggplot2 package, which is part of the tidyverse, contains a dataset called msleep about mammal sleep patterns.

# Task 1: Load the tidyverse package.
# Write your code here:


# Task 2: The `msleep` dataset is available after loading the tidyverse.
# Use the `glimpse()` function to get a quick overview of the `msleep` dataset.
# Write your code here:


# Task 3: Now use the `summary()` function on the `msleep` dataset.
# Write your code here:

❓Question 1: Based on the output of glimpse(), how many rows (observations) and columns (variables) are in the msleep dataset?

Your Answer: [Type your answer here]

❓Question 2: What is one key difference between the information provided by glimpse() and the information provided by summary() for a variable like sleep_total?

Your Answer: [Type your answer here]

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Exercise 2: Data Wrangling

Let’s say we are only interested in herbivores. We can use functions from the dplyr package (part of the tidyverse) to create a new, sorted dataset.

# Task 1: Create a new object called `herbivores` that contains only the animals
# from the `msleep` dataset where the `vore` column is equal to "herbi".
# Hint: The syntax for filtering is: new_object <- old_object %>% filter(column_name == "value")
# Write your code here:


# Task 2: Now, sort this new `herbivores` dataset by total sleep time, from highest to lowest.
# You can overwrite the `herbivores` object with the newly sorted version.
# Hint: Use the `arrange()` function with `desc()` for descending order.
# The syntax is: object <- object %>% arrange(desc(column_to_sort_by))
# Write your code here:


# Now, print the new, sorted `herbivores` object to see the result.

❓Question: After sorting, which herbivore sleeps the most? How many hours does it sleep?

Your Answer: [Type your answer here]

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Exercise 3: First Data Visualization

Data visualization is a critical part of understanding data. Let’s create a scatterplot to see if there is a relationship between how long a herbivore sleeps and how much time it spends dreaming.

# Task: Create a scatterplot using ggplot().
# We want to plot the `sleep_rem` (dreaming sleep) on the y-axis and `sleep_total` on the x-axis,
# using only our `herbivores` dataset.
# Fill in the blanks in the code below.

ggplot(data = ________, aes(x = _______, y = _________)) +
  geom_????? +
  labs(title = "Total Sleep vs. REM Sleep in Herbivores",
       x = "Total Sleep (hours)",
       y = "REM Sleep (hours)")

❓Question 1: Look at the plot you created. In one or two sentences, describe the relationship you see between total sleep and REM sleep for these animals. Is the relationship positive, negative, or is there no clear relationship?

Your Answer: [Type your answer here]

❓Question 2: Are there any animals that seem unusual or stand out from the general pattern? Briefly describe one.

Your Answer: [Type your answer here]

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Exercise 4: Calculating Summary Statistics

Often, we want to calculate a single value to summarize our data. The summarise() function is perfect for this.

# Task: Calculate the average (mean) total sleep time for ALL mammals in the original `msleep` dataset.
# Hint: The syntax is: dataset %>% summarise(new_variable_name = mean(column_name, na.rm = TRUE))
# The `na.rm = TRUE` part is important because it tells R to ignore any missing values.
# Write your code here:

❓Question: What is the mean total sleep time for all mammals in the dataset?

Your Answer: [Type your answer here]

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End of Lab 1. Don’t forget to Knit! 🧶