Problem Set 4 - More ggplot2

More Visualization Processes

Directions

The objective of this assignment is to complete and explain basic plots before moving on to more complicated ways to graph data.

• To ensure accuracy pay close attention to axes and labels, you will be evaluated based on the accuracy and expository nature of your graphics. Make sure your axis labels are easy to understand and are comprised of full words with units if necessary.

Each question is worth 5 points.

To submit this homework you will create the document in Rstudio, using the knitr package (button included in Rstudio) and then submit the document to your Rpubs account. Once uploaded you will submit the link to that document on Canvas. Please make sure that this link is hyper linked and that I can see the visualization and the code required to create it (echo=TRUE).

Questions

1. For The following questions use the Marriage data set from the mosaicData package.

• Create an informative and meaningful data graphic.
• Identify each of the visual cues that you are using, and describe how they are related to each variable.
• Create a data graphic with at least five variables (either quantitative or categorical). For the purposes of this exercise, do not worry about making your visualization meaningful—just try to encode five variables into one plot.

library(mosaicData)
head(Marriage)

##   bookpageID    appdate ceremonydate delay     officialTitle person        dob
## 1   B230p539 1996-10-29   1996-11-09    11    CIRCUIT JUDGE   Groom 1964-04-11
## 2   B230p677 1996-11-12   1996-11-12     0 MARRIAGE OFFICIAL  Groom 1964-08-06
## 3   B230p766 1996-11-19   1996-11-27     8 MARRIAGE OFFICIAL  Groom 1962-02-20
## 4   B230p892 1996-12-02   1996-12-07     5          MINISTER  Groom 1956-05-20
## 5   B230p994 1996-12-09   1996-12-14     5          MINISTER  Groom 1966-12-14
## 6  B230p1209 1996-12-26   1996-12-26     0 MARRIAGE OFFICIAL  Groom 1970-02-21
##        age     race prevcount prevconc hs college dayOfBirth        sign
## 1 32.60274    White         0     <NA> 12       7        102       Aries
## 2 32.29041    White         1  Divorce 12       0        219         Leo
## 3 34.79178 Hispanic         1  Divorce 12       3         51      Pisces
## 4 40.57808    Black         1  Divorce 12       4        141      Gemini
## 5 30.02192    White         0     <NA> 12       0        348 Saggitarius
## 6 26.86301    White         1     <NA> 12       0         52      Pisces

# Load the necessary library
library(ggplot2)

# Create the plot
ggplot(data = Marriage, aes(x = prevconc, y = age, color = person)) +
  geom_boxplot() +
  labs(title = "Marriage Ages with Respect to Previous Marriage and Person Type", x = "Previous Marriage Condition", y = "Age")

Answers: - Create an informative and meaningful data graphic. The above plot is able to effectively show the relationship between the age of individuals with respect to their previous marriage condition and person type. The plot shows the distribution of ages for each condition of the previous marriage, with each boxplot color-coded by the type of individual. This allows us to easily compare the age distributions across different conditions of the previous marriage and person types.

• Here are the visual cues being used in the code, along with how they are related to each variable:

x-axis: This visual cue represents the prevconc variable and shows the condition of the previous marriage. Each boxplot represents a different condition of the previous marriage. y-axis: This visual cue represents the age variable and shows the age of the individual. The height of each boxplot shows the distribution of ages for individuals with a particular condition of the previous marriage. color: This visual cue represents the person variable and distinguishes between the type of individual (husband or wife) in the plot. The color of each boxplot corresponds to the type of individual.

• Create a data graphic with at least five variables (either quantitative or categorical). For the purposes of this exercise, do not worry about making your visualization meaningful—just try to encode five variables into one plot.

ggplot(Marriage, aes(x = age, y = delay, color = officialTitle, shape = race)) +
  geom_jitter(alpha = 0.5) +
  facet_wrap(~ person) +
  labs(title = "Marriage Delay vs Age by Official Title and Race", 
       x = "Age", y = "Delay", color = "Official Title", shape = "Race")

The above code uses the geom_jitter() function to create a scatterplot with jittered points. The color aesthetic is used to encode the officialTitle variable, and the shape aesthetic is used to encode the race variable. The facet_wrap() function is used to create separate panels for each value of the person variable. Finally, the labs() function is used to add a title and axis labels to the plot.

Your objective for the next four questions will be write the code necessary to exactly recreate the provided graphics.

2. Boxplot Visualization

This boxplot was built using the mpg dataset. Notice the changes in axis labels.

ggplot(data = mpg, aes(x = hwy, y = manufacturer)) +
  geom_boxplot() +
  labs(title = "Highway Fuel Efficiency by Manufacturer", x = "Highway MPG", y = "Manufacturer") +
  coord_flip() +
  theme_classic()

3. Stacked Density Plot

This graphic is built with the diamonds dataset in the ggplot2 package.

ggplot(diamonds, aes(x=price, fill=cut)) +
  geom_density(alpha=0.5, color="black") +
  facet_wrap(~cut) +
  labs(x="Diamond Price (USD)", y="Density", fill="Cut", title="Diamond Price Density by Cut") +
  theme_minimal()

4. Sideways bar plot

This graphic uses the penguins dataset and shows the counts between males and females by species.

ggplot(penguins, aes(x = sex, fill = species)) +
  geom_bar() +
  facet_wrap(~species, ncol = 1) +
  scale_fill_manual(values = c("red","green","yellow"), guide = FALSE) +
  theme_minimal() +
  coord_flip() +
  labs(x = "Sex", y = "Count", fill = "Species")

## Warning: The `guide` argument in `scale_*()` cannot be `FALSE`. This was deprecated in
## ggplot2 3.3.4.
## ℹ Please use "none" instead.

5. Scatterplot

This figure examines the relationship between bill length and depth in the penguins dataset.

ggplot(penguins, aes(x = bill_length_mm, y = bill_depth_mm, color = species)) +
  geom_point(shape = 21, size = 3, fill = 'white', stroke = 0.5) +
  geom_smooth(method = 'lm', se = FALSE, color = 'black', size = 1) +
  scale_color_manual(values = c("red", "green", "yellow")) +
  labs(x = 'Bill Length (mm)', y = 'Bill Depth (mm)', color = 'Species') +
  theme_classic()