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1. Which of the following is a core technology behind language models like ChatGPT, as mentioned in the lecture?

Decision Trees Deep Learning Linear Regression All of the Above

2. What is the primary purpose of storyboarding in data storytelling?

To collect data from stakeholders To provide a visual outline for content structure To design complex data visualizations To perform statistical analysis

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.

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

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 \(x\)

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.

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.

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)

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.

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()

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__)

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()

Question 13

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

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()

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()

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()

Question 17

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

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__)

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__()

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")

Name, Email, and Session

Short-Answer Questions

Data Visualization with `ggplot`

Questions 11-17

Question 11

Question 12

Question 13

Question 14

Question 15

Question 16

Question 17

Questions 18-20

Question 18

Question 19

Question 20

Name, Email, and Session

Short-Answer Questions

Data Visualization with ggplot

Questions 11-17

Question 11

Question 12

Question 13

Question 14

Question 15

Question 16

Question 17

Questions 18-20

Question 18

Question 19

Question 20

Data Visualization with `ggplot`