Classwork 11

Relationship Plots

Question 1. Ice Cream Sales and Drowning Incidents

Consider the data frame df, which records monthly ice cream sales and drowning incidents.

library(tidyverse)
df <- read_csv("http://bcdanl.github.io/data/icecream-drowning.csv")

Part A

• 🤖 Task 1: Fill in the blanks in the provided ggplot() code chunk.
  
• 💬 Task 2: Add a brief comment describing the relationship between ice cream sales (IceCreamSales) and drowning incidents (DrowningIncidents).

ggplot(data = __BLANK_1__,
       mapping = aes(x = __BLANK_2__,
                     y = __BLANK_3__)) +
  geom___BLANK_4__() +
  geom___BLANK_5__()

Part B

• ❓ Is the observed relationship one of correlation or causation? Explain your reasoning.
  • Consider the following monthly trends for IceCreamSales and DrowningIncidents:

Monthly Trend of IceCreamSales

Monthly Trend of DrowningIncidents

Question 2. NBC Show Data

The nbc_show dataset comes from NBC’s TV pilots, containing information about television shows, their viewership metrics, and audience engagement.

library(tidyverse)
nbc_show <- read_csv("https://bcdanl.github.io/data/nbc_show.csv")

• Gross Ratings Points (GRP):
  Measures the estimated total viewership of a show — an indicator of its broadcast marketability.
  • 📺 A higher GRP suggests broader exposure and a more marketable program.
• Projected Engagement (PE):
  Captures how attentive and engaged viewers were after watching a show — a more suitable measure of audience engagement.
  • 🧠 After viewing, audiences take a short quiz testing order and detail recall.
    
  • This reflects their level of attention and retention (for both the show and its ads).
    
  • High PE values indicate strong viewer engagement.

Tasks

• 🤖 Task 1: Fill in the blanks in the provided ggplot() code chunks.
  
• 💬 Task 2: Add a brief comment describing the relationship between GRP and PE.

(1) Scatterplot with a Non-Linear Fitted Line

ggplot(data = __BLANK_1__,
       mapping = aes(x = __BLANK_2__,
                     y = __BLANK_3__)) +
  geom_point() +
  geom___BLANK_4__()

(2) Scatterplot with a Linear Fitted Line

ggplot(data = __BLANK_1__,
       mapping = aes(x = __BLANK_2__,
                     y = __BLANK_3__)) +
  geom_point() +
  geom___BLANK_4__(method = __BLANK_5__)

Question 3. GDP per capita vs. Life Expectancy

For Question 3, please install the R package gapminder before starting:

install.packages("gapminder")
??gapminder

The gapminder package provides a built-in dataset named gapminder, which contains country-level data on life expectancy, GDP per capita, and population across time.

Let’s assign it to a new object called df_gapminder:

df_gapminder <- gapminder::gapminder

Tasks

• 🤖 Task 1: Fill in the blanks in the provided ggplot() code chunks.
  
• 💬 Task 2: Add a brief comment describing the relationship between GDP per capita (gdpPercap) and life expectancy (lifeExp).

(1) gdpPercap vs. lifeExp

ggplot(data = __BLANK_1__,
       mapping = aes(x = __BLANK_2__,
                     y = __BLANK_3__)) +
  geom_point(__BLANK_4__ = .1) + # Add transparency to reduce overplotting
  geom_smooth(__BLANK_5__ = "darkorange") +
  geom_smooth(__BLANK_6__)

(2) log(gdpPercap) vs. lifeExp

ggplot(data = __BLANK_1__,
       mapping = aes(x = __BLANK_2__,
                     y = __BLANK_3__)) +
  geom_point(__BLANK_4__ = .2) + # Add transparency to reduce overplotting
  geom_smooth(__BLANK_5__ = "darkorange") +
  geom_smooth(__BLANK_6__)