Week 3 Challenge Problem

Preparation Instructions

• Reminder: Make sure to save this file (File > Save As …) in the folder you created for this class (NOT in the Downloads folder!).

• Download the images NYT-TimeByAge.png, NYT-Census2020.png, NHANES-Educ.png and NHANES-AgePulse.png from the Canvas page for this assignment and save these files to the folder where the RMD file is located.

• Knit your document to HTML frequently so you can more easily find your mistakes. [Things to consider when you knit your documents: Does everything look as you suspected? If not, try to figure out the problem and fix it.]

• Be sure to change the author in the YAML to your name. Remember to keep it inside the quotes.

• For questions asking about aesthetics, give both the type of aesthetic (e.g. x, color, etc.) as well as what it is representing (e.g. name of the variable)

EXERCISES

For Questions 1 to 2, you will be presented data visualizations from the New York Times newspaper and asked to identify or describe aspects of the plot.

1. Consider this graph from September 15, 2021.

1. Using the directory of visualizations from the book Fundamentals of Data Visualization, identify the type of data visualization used for this plot.

• Multiple time series using a line graph

2. What two geometric objects (or geom function from the {ggplot2}) were used to create the plot?

• geom_line()
• geom_point()

3. What were the aesthetics used in the plot, and what variable/constant was mapped/set to that aesthetic? [Note: Just focus on one of the plots to come up with your answer, as three plots are a product of faceting on a particular variable.]

• The X-axis aesthetic is mapped to the variable Year
• The Y-axis aesthetic is mapped to the variable Minutes per day
• The Color aesthetic is mapped to the variable Age group

4. What question do you think the authors were interested in when they created this data visualization? Also, provide an answer to their (assumed) question.

• Among different age groups, how did the time spent exercising, grooming, texting, making phone calls, and engaging in video chats change between 2019 and 2020 when not working or attending school?
• The authors found that across all age groups, people spent more time texting, making phone calls, and engaging in video chats, with the 15-24 age group experiencing the most significant increase. Additionally, they found that among those aged 15-24, the time spent exercising decreased, while for all other age groups, it increased. Furthermore, the authors found a slight increase in the amount of time spent grooming among the 15-24 age group, but a decrease in time among all other age groups, with those in the 45-64 age group experiencing the most significant decline.

2. Consider this graph from October 1, 2020.

1. Using the directory of visualizations from the book Fundamentals of Data Visualization, identify the types of data visualizations used for this plot.

• Small multiple line graphs with area under the curve filled

2. What two (or three) geometric objects (or geom function from the {ggplot2}) were used to create the plot? Hint: we are assuming this is not a density plot.

• geom_line()
• geom_hline()
• geom_area()

3. Give three aesthetics used in the plot and what variable/constant was mapped/set to that aesthetic. [Note: Just focus on one or two of the plots to come up with your answer.]

• The X-axis aesthetic is mapped to the variable Date
• The Y-axis aesthetic is mapped to the variable Percentage of uncounted addresses
• The fill aesthetic is mapped to the variable Proportion of uncounted addresses
• The line type aesthetic is mapped to the variable Reference line

4. What question do you think the authors were interested in when they created this data visualization? Also, provide an answer to their (assumed) question.

• Between September 10 and September 30, how many states are not on track to collect responses for uncounted addresses?
• The authors found that 37 states were not on track to collect responses for uncounted addresses.

Use for Questions 3 to 4

You will be presented with graphs using the NHANES dataset in the {NHANES} package and asked to recreate them using grammar of graphics framework. [Note: This is the same dataset we have used in class, but instead of importing the dataset using .csv file, you are going to access it via the {NHANES} package.]

3. Consider this graph exploring the variables of Education from the NHANES dataset.

1. Reconstruct the graph using {ggplot2} and its core components of the grammar of graphics (data, aesthetic properties, and geometric objects). [Hint: The aesthetic for coloring the bars is fill =].

ggplot(NHANES, aes(x = Education, fill = Education)) + 
  geom_bar()

2. Identify the major components (data, aesthetics, geometric object(s)) of the grammar of graphics that were used to create the plot as well as variables or values that went into the arguments for the component.

• Data: the NHANES dataset was used
• Aesthetics: the x-axis and fill, both mapped to the variable Education
• Geometric object: geom_bar()

3. Consider this graph exploring the variables of Age, Pulse, and Gender from the NHANES dataset.

1. Reconstruct the graph using {ggplot2} and its core components of the grammar of graphics (data, aesthetic properties, and geometric objects). [Hint: Notice that one of the geoms has transparency.]

ggplot(NHANES, aes(x = Age, y = Pulse, color= Gender)) +
  geom_point(alpha = 0.1, size = 0.5) +
  geom_smooth(se = FALSE, size = 0.5)

2. Identify the major components (data, aesthetics, geometric object(s)) of the grammar of graphics that were used to create the plot as well as variables or values that went into the arguments for the component.

• Data: the NHANES dataset was used
• Aesthetics:
  • the X-axis is mapped to Age
  • the Y-axis is mapped to Pulse
  • the Color is mapped to Gender
• Geometric object:
  • geom_point() : alpha = 0.1, size = 0.5
  • geom_smooth(): se = FALSE, size = 0.5

3. Rather than looking at Age as a continuous variable, let’s swap that out with a categorical one, AgeDecade. Create a data visualization that allows us to compare the Pulse across AgeDecade categories, and also by Gender.

ggplot(NHANES, aes(x = AgeDecade, y = Pulse, color= Gender)) +
  geom_boxplot()