Lab 2

Author

Your Name

Published

Invalid Date

Before your start the lab notebook

Update the header - put your name in the author argument and put today’s date in the date argument.
Click the “Render” button in RStudio and then open the rendered 2-lab2.html page.
Then go back and try changing the theme argument in the header to something else - you can see other available themes here. Notice the difference when you render now!

Overview of Lab2

There are two parts of this lab notebook. We will be using the same example datasets covered in class: penguins and lyme. In first part, we will practice basic chart types for visualizing quantitative information and trend over time. In the second part, we will practice chart types for visualizing distribution and relationships between two variables.

Skills practiced:

Load, clean, and filter data using dplyr functions (filter(), mutate(), summarise())
Create bar charts and dot plots to compare counts and averages across categories
Generate histograms and boxplots to visualize distributions of continuous variables
Use line charts to show trends over time for different groups
Build scatterplots to explore relationships between two numeric variables
Customize plots using ggplot2 elements such as color, facet_wrap(), and geom_smooth()
Practice reproducible coding and visualization in a Quarto notebook

Load Required Packages and Datasets

Code

library(tidyverse)
library(palmerpenguins)
penguins_clean <- penguins %>%
  filter(!is.na(species), !is.na(flipper_length_mm), !is.na(body_mass_g), !is.na(island), !is.na(sex))

lyme_rates <- read_csv("data/2023_CaseIncid-Lyme-Disease-Rates-by-State-or-Locality.csv") %>%
  janitor::clean_names() %>%
  mutate(state = str_replace(state, "\x86", "")) %>%
  rename(rates_per_100k = rates)

lyme_cases <- read_csv("data/2023_CaseIncid-Lyme-Disease-Cases-by-State-or-Locality.csv") %>%
  janitor::clean_names() %>%
  pivot_longer(
    cols = -c(state),
    names_to = "year",
    values_to = "cases"
  ) %>%
  mutate(
    state = str_replace(state, "\x86", ""),
    year = str_replace(year, "x", ""),
    year = as.numeric(year)) %>%
  filter(year >= 2010)

lyme <- lyme_cases %>%
  left_join(lyme_rates, by = c("state", "year"))

Part 1: Visualizing quantitative information and trend over time

1.1. How many penguins were observed on each island?

Use a bar chart to show the number of penguins by island.

Code

# Your code here

✏️ Q: Which island had the most penguins observed?

1.2. Compare penguin counts by sex

Use a dot plot to compare the number of male and female penguins, add labels to show the data

Code

# Your code here

✏️ Q: Are there more male or female penguins in the dataset?

1.3. Lyme disease cases over time

Choose four states and plot Lyme case counts from 2010–2023 using a line chart.

Code

# Your code here

✏️ Q: Which state shows a rising trend over time?

Part 2: Visualizing distribution and relationships between two numerical values

2.1. Compare penguin body mass by island

Use boxplots or violin plots to compare penguin body mass by island. Add individual data points to the plot.

Code

# Your code here

✏️ Q: Which island has the heaviest penguins?

2.2. Distribution of Lyme cases across states in 2020

Use a histogram or density plot to explore the variation in state-level case counts.

Code

# Your code here

✏️ Q: Are most states low-burden or high-burden?

2.3. Bill dimensions

Create a scatterplot of bill length vs. bill depth, colored by species.

Code

# Your code here

✏️ Q: Which species tends to have longer bills?

2.4. Add faceting

Facet the same scatterplot by island.

Code

# Your code here

✏️ Q: Do the relationships differ across islands?

Save and Push Your Work

Remember to save your .qmd and render the HTML output before committing to GitHub.

Code

git add 2-lab2.qmd 2-lab2.html
git commit -m "Complete Lab 2"
git push