Homework 4

Data Transformation & Visualization in R — Part II

Multiple Choice Questions

Question 1

When visualizing grouped data, which of the following best describes the difference between using the aes(color = ...) aesthetic and using facet_wrap(…)`?

color aesthetic mapping separates groups into different plots, while faceting overlays groups in one plot color aesthetic mapping differentiates groups within the same plot, while faceting displays each group in its own panel Both color aesthetic mapping and faceting always produce identical visualizations Faceting is only used for time-series data, while color aesthetic mapping is only used for categorical data

Show answer

Answer: color aesthetic mapping differentiates groups within the same plot, while faceting displays each group in its own panel

Explanation:

• aes(color = ...) keeps all groups in the same plot, distinguishing them by different colors.
• facet_wrap() separates each group into its own subplot, giving each its own panel and axes.
• Faceting is helpful when groups overlap too much in one plot or when per-group trends need to be seen clearly.

Question 2

In ggplot2, what does setting scales = "free_y" in facet_wrap() do?

Allows both the x and y axes to vary independently in each facet Allows the y-axis scale to vary by facet while the x-axis is shared across facets Allows the x-axis scale to vary by facet while the y-axis is shared across facets Keeps both axes fixed to the same scale across all facets (the default)

Show answer

Answer: Allows the y-axis scale to vary by facet while the x-axis is shared across facets

Explanation:

• scales = "free_y" means each facet gets its own y-axis scale, but all facets share the same x-axis.
• This is useful when groups differ greatly in magnitude on the y-axis, preventing small groups from appearing flat.

Question 3

Which of the following statements about data visualizations is true?

They always explain the underlying reasons behind data trends. They are sufficient on their own to drive data-informed decisions. They are useful for showing “what” is happening in the data. They eliminate the need for narratives in data storytelling.

Show answer

Answer: They are useful for showing “what” is happening in the data.

Explanation: Data visualizations are excellent tools for understanding “what” is happening, but they often do not explain “why” trends occur, which requires additional analysis or narratives.

Question 4

In the ggplot2 syntax ggplot(data, aes(x, y)) + geom_point(), what does aes() stand for?

Aesthetic mappings Arithmetic expressions Axis equal scaling Average error squares

Show answer

Answer: Aesthetic mappings

Explanation: The aes() function in ggplot2 stands for aesthetic mappings, linking data variables to visual properties like x, y, color, fill or shape.

Question 5

The equation \(\Delta\log(x) \approx \Delta x / x_{0}\) demonstrates that a small change in the natural logarithm of \(x\), from an initial value \(\log(x_0)\) to an ending value \(\log(x_{1})\), where the change in \(x\) is given by \(\Delta x = x_{1} - x_{0}\), can be approximated by:

The absolute change in \(x\) The percentage change in \(x\) The square of \(x\) The reciprocal of \(x\)

Show answer

Answer: The percentage change in \(x\)

Explanation: When \(x\) changes by a small amount, the natural logarithm of \(x\) changes approximately by the percentage change in \(x\).

Question 6

Which of the following statements about mapping and setting aesthetics in ggplot2 is FALSE?

Mapping aesthetics involves linking data variables to visual properties within aes() Setting aesthetics manually is done outside of aes() to set fixed visual properties. You can set aesthetics manually within aes() by assigning fixed values. Mapping aesthetics allows different categories to be represented by different colors or shapes.

Show answer

Answer: You can set aesthetics manually within aes() by assigning fixed values.

Explanation: Manually setting fixed aesthetics (e.g., color = "red") should be done outside of aes(). Inside aes(), values are mapped a variable in a data.frame.

Question 7

Which of the following is NOT a way to explicitly inform ggplot about the grouping structure in a line plot?

Using the group aesthetic. Using the color aesthetic. Using the linetype aesthetic. Using the size aesthetic.

Show answer

Answer: Using the size aesthetic.

Explanation: The size aesthetic does not explicitly inform ggplot about grouping in a line plot, whereas group, color, and linetype do.

Question 8

If you have a data frame with a date variable and want to plot a time series for each category in a variable called group_var, what is the minimal aesthetic mapping required in ggplot2 to correctly plot separate lines for each group?

mapping = aes(x = date_var, y = value_var) mapping = aes(x = date_var, y = value_var, color = group_var) mapping = aes(x = date_var, y = value_var, group = group_var) Both mapping = aes(x = date_var, y = value_var, color = group_var) and mapping = aes(x = date_var, y = value_var, group = group_var)

Show answer

Answer: Both mapping = aes(x = date_var, y = value_var, color = group_var) and mapping = aes(x = date_var, y = value_var, group = group_var)

Explanation: Both options correctly separate lines for each group. group ensures proper grouping, while color differentiates groups visually.

Question 9

When creating a vertical boxplot in ggplot2, which of the following mappings is correct?

Map both the x and y aesthetics to categorical variables. Map a numeric variable to x and a categorical variable to y. Map a categorical variable to x and a numeric variable to y. Map both the x and y aesthetics to numeric variables.

Show answer

Answer: Map a categorical variable to x and a numeric variable to y.

Explanation: In a vertical boxplot, the x-axis typically represents categories, while the y-axis represents the numeric variable whose distribution is being summarized.

Question 10

Which of the following functions is used to apply a color-blind friendly palette to the fill aesthetic in ggplot2?

scale_fill_manual() scale_fill_tableau() scale_color_gradient() scale_x_continuous()

Show answer

Answer: scale_fill_tableau() or scale_fill_manual()

Explanation: - The scale_fill_tableau() function, part of the ggthemes package, extends ggplot2 with Tableau-inspired themes and scales, including color-blind friendly palettes.

• The scale_fill_manual() function can used to apply a custom color palette, including color-blind friendly palettes, to the fill aesthetic in ggplot2.

Data Visualization with ggplot

The followings are the R packages for this homework assignment:

library(tidyverse)
library(skimr)
library(ggthemes)
library(gapminder)

Questions 11-17

Consider the following titanic data.frame for Questions 11-17:

titanic <- read_csv("https://bcdanl.github.io/data/titanic_cleaned.csv")

Question 11

How would you create the following data.frame, titanic_class_survival?

• The titanic_class_survival data.frame counts the number of passengers who survived and those who did not survive within each class in the titanic data.frame.

Complete the code by filling in the blanks.

__BLANK 1__ <- titanic |> 
  count(__BLANK 2__)

Show answer

titanic_class_survival <- titanic |> 
  count(class, survived)

Question 12

How would you describe the variation in the distribution of age across classes and genders?

Complete the code by filling in the blanks.

ggplot(data = __BLANK 1__,
       mapping = aes(x = gender,
                     __BLANK 2__ = age,
                     __BLANK 3__ = gender)) +
  __BLANK 4__(show.legend = F) +
  __BLANK 5__(~class) +
  scale_fill_tableau()

Show answer

ggplot(data = titanic,
       mapping = aes(x = gender,
                     y = age,
                     fill = gender)) +
  geom_boxplot(show.legend = F) +
  facet_wrap(~class) +
  scale_fill_tableau()

Question 13

Provide a comment on the variation in the distribution of age across classes and genders.

Show answer

• For both female and male groups, the ages of the first class passengers in the Titanic ranges wider than the second class and the third class.
• For both female and male groups,the median of the first class passengers’s ages is higher than that of the second class and the third class.
• The first quartile of female’s age is always lower than that of male’s across all classes. Particularly, the such gap is wider for the first class.

Question 14

How would you describe the variation in the distribution of survived across classes and genders?

Complete the code by filling in the blanks.

ggplot(data = __BLANK 1__,
       mapping = aes(__BLANK 2__ = class,
                     __BLANK 3__ = survived)) +
  __BLANK 4__() +
  __BLANK 5__(~gender) +
  labs(x = "Proportion") +
  scale_fill_tableau()

Show answer

ggplot(data = titanic,
       mapping = aes(y = class,
                     fill = survived)) +
  geom_bar() +
  facet_wrap(~gender) +
  labs(x = "Proportion") +
  scale_fill_tableau()

Question 15

How would you describe the variation in the distribution of survived across classes and genders?

Complete the code by filling in the blanks.

ggplot(data = __BLANK 1__,
       mapping = aes(__BLANK 2__ = class,
                     __BLANK 3__ = survived)) +
  __BLANK 4__(position = __BLANK 5__) +
  __BLANK 6__(~gender) +
  labs(x = "Proportion") +
  scale_fill_tableau()

Show answer

ggplot(data = titanic,
       mapping = aes(y = class,
                     fill = survived)) +
  geom_bar(position = "fill") +
  facet_wrap(~gender) +
  labs(x = "Proportion") +
  scale_fill_tableau()

Question 16

How would you describe the variation in the distribution of survived across classes and genders?

Complete the code by filling in the blanks.

ggplot(data = __BLANK 1__,
       mapping = aes(__BLANK 2__ = class,
                     __BLANK 3__ = survived)) +
  __BLANK 4__(position = __BLANK 5__) +
  __BLANK 6__(~gender) +
  scale_fill_tableau()

Show answer

ggplot(data = titanic,
       mapping = aes(y = class,
                     fill = survived)) +
  geom_bar(position = "dodge") +
  facet_wrap(~gender) +
  scale_fill_tableau()

Question 17

Provide a comment on the variation in the distribution of survived across classes and genders.

Show answer

• First-class passengers had the highest survival rate.
  • Survival rates decline progressively in second and third classes.
• Female passengers generally had a much higher survival rate compared to male passengers.
  • Female first-class passengers had the highest survival rates, followed by female second-class and third-class passengers.
  • Male survival rates were considerably lower in all classes, with third-class males experiencing the lowest survival likelihood.
• These patterns may be attributed to the influence of both socioeconomic status and gender norms prevalent in the early 1900s.

Questions 18-20

Consider the following nyc_dogs data.frame for Questions 18-20:

nyc_dogs <- read_csv("https://bcdanl.github.io/data/nyc_dogs_cleaned.csv")

• The nyc_dogs data.frame contains data on licensed dogs in New York city.

Question 18

How would you create the following data.frame, nyc_dogs_breeds?

• The nyc_dogs_breeds data.frame counts the number of occurrences for each value in the breed variable in the nyc_dogs data.frame.
  • The nyc_dogs_breeds data.frame keeps observations if
    1. The number of occurrences (n) is greater than or equal to 2000;
    2. The value of breed is not missing.
  • The observations in the nyc_dogs_breeds data.frame is arranged by n in descending order.

Complete the code by filling in the blanks.

__BLANK 1__ <- nyc_dogs |> 
  __BLANK 2__ |> 
  filter(__BLANK 3__(breed)) |> 
  filter(__BLANK 4__) |> 
  arrange(__BLANK 5__)

Show answer

nyc_dogs_breeds <- nyc_dogs |> 
  count(breed) |> 
  filter(!is.na(breed)) |> 
  filter(n >= 2000) |> 
  arrange(-n)  # or arrange(desc(n))

Question 19

How would you describe the distribution of breed using the nyc_dogs_breeds data.frame?

Complete the code by filling in the blanks.

ggplot(data = __BLANK 1__,
       mapping = aes(x = __BLANK 1__,
                     __BLANK 3__)) +
  __BLANK 4__()

Show answer

ggplot(data = nyc_dogs_breeds,
       mapping = aes(x = n,
                     y = breed)) +
  geom_col()

Question 20

How would you describe the distribution of breed using the nyc_dogs_breeds data.frame?

Complete the code by filling in the blanks.

ggplot(data = __BLANK 1__,
       mapping = aes(x = __BLANK 1__,
                     __BLANK 3__)) +
  __BLANK 4__() +
  labs(y = "Breed")

Show answer

ggplot(data = nyc_dogs_breeds,
       mapping = aes(x = n,
                     y = fct_reorder(breed, n))) +
  geom_col() +
  labs(y = "Breed")