Society of Actuaries (SOA) PA Practice Exam

Which R code snippet is used to generate boxplots for factor variables effectively?

1. var.boxplot(data, "sale_price")

2. ggplot(data, aes(...))

3. boxplot(data$sale_price ~ data$factor_variable)

4. ggplot(data, aes(x = factor(variable), y = sale_price))

The correct answer is: ggplot(data, aes(x = factor(variable), y = sale_price))

The selected answer is effective for generating boxplots for factor variables in R using the ggplot2 package. In this code, the function ggplot is employed to create a visually appealing and customizable boxplot. By using aes(x = factor(variable), y = sale_price), the code specifies that the x-axis will represent a categorical (factor) variable while the y-axis will reflect the continuous variable, sale_price. Converting the variable to a factor ensures that it is treated as a discrete category rather than a continuous numeric variable when plotting. This is crucial because a boxplot visualizes the distribution of a continuous variable across different levels of a categorical variable, clearly illustrating medians, quartiles, and potential outliers for each category. In the context of the other options, this choice stands out because it leverages the advanced plotting capabilities of ggplot2, which allows for enhanced data visualization and customization compared to base R plotting functions. The first option does not provide sufficient context for generating boxplots. The second option lacks specifics about the variables used, making it incomplete. The third option employs a base R syntax that could work, but it does not offer the same level of customization and flexibility as ggplot2.