4  Bivariate Viz

Learning Goals

Warm-up (together)

• Review univariate viz ideas and code.

Exercises (in groups)

• Explore how to build and interpret visualizations of bivariate, i.e. two variable, relationships.

Additional Resources

For more information about the topics covered in this chapter, refer to the resources below:

• ggplot demo (YouTube) by Lisa Lendway
• common ggplot mistakes (YouTube) by Lisa Lendway
• Visualizing amounts (html) by Wilke
• Visualizing many distributions at once (html) by Wilke

Instructions

General

• Be kind to yourself.
• Collaborate with and be kind to others. You are expected to work together as a group.
• Ask questions. Remember that we won’t discuss these exercises as a class.

Activity Specific

Help each other with the following:

• Create a new Quarto document in the activities folder of your portfolio project and do not forgot to include it in _quarto.yml file. Then click the </> Code link at the top right corner of this page and copy the code into the created Quarto document. This is where you’ll take notes. Remember that the portfolio is yours, so take notes in whatever way is best for you.
• Don’t start the Exercises section before we get there as a class. First, engage in class discussion and eventually collaborate with your group on the exercises!
• Go slow! Don’t worry about memorizing the code, but do slow down to examine it, even if you’re not asked explicitly about the code. Think about:
  • How the data variables are used in the plot.
  • What geom the plot uses.
  • How color and fill work for the geom you’re using.
• There are some coding “intuition checks” along the way. The goal here is to start connecting to and expanding upon the structure of our ggplot() code. Don’t spend more than a few minutes trying these out. If you don’t get it, no problem! You can move on and learn the code in the next exercise.
• The goal is for every student to finish the required exercises before we meet again. After that, choose your own adventure!
  • If you’re feeling like the required exercises gave you plenty to chew on, simply stop the activity. Go back and review the material you learned. You never need to think about the optional exercises.
  • If you’re feeling like your brain still has some space after completing the required exercises, try the optional ones. These exercises are either “bonus” visualizations or intuition checks about some material coming up.

4.1 Review

Let’s review some univariate concepts and code using our class survey data. If the answers aren’t at the top of your mind, don’t fret! We’ve barely started speaking this new language, and learned a ton of vocab last week, so you naturally won’t remember it all.

# Import data
survey <- read.csv("https://ajohns24.github.io/data/112/about_us_2024.csv")

# How many students have now filled out the survey?


# What type of variables do we have?

EXAMPLE 1: Hangout Preferences

Students were asked, in that moment, where they’d most like to spend time outside. How did they answer? Was there a lot of agreement or a lot of variability in answers? Build and interpret a plot that helps address these questions while reviewing:

• “code as communication”
• connecting with the components of a plot:
  • set up a frame
  • add a layer / geometric element
  • change the theme, e.g. axis labels, color, fill

# Attach a package needed to use the ggplot function


# Make a ggplot

EXAMPLE 2: Temperature Preferences

Students were asked about their ideal outdoor temperature, in degrees Celsius. How did they answer? What was the typical response? What was the range in responses? Were there any outliers? Build and interpret 2 plots that help address these questions.

Bar Charts vs. Histograms

Bar charts & histograms can appear pretty similar, but they do different things.

• Bar charts count up the number of observations of each outcome of a variable. They’re good for categorical variables, or quantitative variables with only a handful of possible outcomes.
• Histograms count up the number of observations that fall into different numerical ranges of variable. They’re good for quantitative variables, especially those with many different observed outcomes.

4.2 New stuff

Thus far, we’ve been studying one variable at a time, using univariate plots. But once we get a sense of how individual variables behave on their own, our questions often turn to relationships among variables. For example, in our hikes data:

• How much time does it take to complete a hike? ——> How is time related to a hike’s elevation? What about its length?
• How does difficult rating vary from hike to hike? ——-> How is difficulty rating related to a hike’s ascent?

4.2.1 Exploring relationships

Exploring univariate patterns often sparks follow-up questions about relationships between 2+ variables. Often, but not always, variables take on specific roles:

• response variable: the variable whose variability we would like to explain (time to complete a hike)
• predictors: variables that might explain some of the variability in the response (a hike’s elevation or length)

Visualizations can help explore:

• relationship trends (direction and form)
• relationship strength (degree of variability from the trend)
• outliers in the relationship

EXAMPLE 3

For each pair of variables below, sketch on paper a visualization of their relationship. Focus on general viz process, don’t worry about the exact details. The data here are totally made up.

1. 3pm temperature (response) vs 9am temperature (predictor)

data.frame(temp_3pm = c(24, 26, 20, 15, 15, 15), temp_9am = c(14, 18, 15, 13, 11, 11))

  temp_3pm temp_9am
1       24       14
2       26       18
3       20       15
4       15       13
5       15       11
6       15       11

2. 3pm temperature (response) vs location (predictor)

weather <- data.frame(temp_3pm = c(24, 26, 20, 15, 15, 0, 40, 60, 57, 44, 51, 75),
                      location = rep(c("A", "B"), each = 6))
weather

   temp_3pm location
1        24        A
2        26        A
3        20        A
4        15        A
5        15        A
6         0        A
7        40        B
8        60        B
9        57        B
10       44        B
11       51        B
12       75        B

Think: How might we modify the below density plot of temp_3pm to distinguish between locations?

ggplot(weather, aes(x = temp_3pm)) +
      geom_density()

3. rain_today (the response) and location (the predictor)

weather <- data.frame(rain_today = c("no", "no", "no", "no", "yes", "no", "yes", "no", "yes", "yes", "no", "yes"),
                        location = c(rep("A", 7), rep("B", 5)))
    weather

   rain_today location
1          no        A
2          no        A
3          no        A
4          no        A
5         yes        A
6          no        A
7         yes        A
8          no        B
9         yes        B
10        yes        B
11         no        B
12        yes        B

Think: How might we modify the below bar plot of location to distinguish between days on which it did or didn’t rain?

ggplot(weather, aes(x = location)) +
      geom_bar()

4.2.2 General guidance for building bivariate plots

As with univariate plots, an appropriate visualization for the relationship between 2 variables depends upon whether the variables are quantitative or categorical. In general:

• Each quantitative variable requires a new axis (or a quantitative scale if we run out of axes).
• Each categorical variable requires a new way to “group” the graphic (eg: using colors, shapes, separate facets, etc)
• For visualizations in which overlap in glyphs or plots obscures the patterns, try faceting or transparency.

4.3 Exercises (required)

Github user Tony McGovern has compiled and made available 2020/2016/2012 presidential election results for most of 3000+ U.S. counties, except Alaska. (Image: Wikimedia Commons)

A wrangled version of this data, is imported below, after being combined with:

• 2013 county-level demographics from the df_county_demographics data set from the choroplethr R package
• historical voting trends in the state in which the county falls (from https://www.270towin.com/content/blue-and-red-states):
  • red = consistently Republican
  • blue = consistently Democratic
  • purple = something in between

# Load data
elections <- read.csv("https://mac-stat.github.io/data/election_2020_county.csv")

# Check it out
head(elections)

  state_name state_abbr historical    county_name county_fips total_votes_20
1    Alabama         AL        red Autauga County        1001          27770
2    Alabama         AL        red Baldwin County        1003         109679
3    Alabama         AL        red Barbour County        1005          10518
4    Alabama         AL        red    Bibb County        1007           9595
5    Alabama         AL        red  Blount County        1009          27588
6    Alabama         AL        red Bullock County        1011           4613
  repub_pct_20 dem_pct_20 winner_20 total_votes_16 repub_pct_16 dem_pct_16
1        71.44      27.02     repub          24661        73.44      23.96
2        76.17      22.41     repub          94090        77.35      19.57
3        53.45      45.79     repub          10390        52.27      46.66
4        78.43      20.70     repub           8748        76.97      21.42
5        89.57       9.57     repub          25384        89.85       8.47
6        24.84      74.70       dem           4701        24.23      75.09
  winner_16 total_votes_12 repub_pct_12 dem_pct_12 winner_12 total_population
1     repub          23909        72.63      26.58     repub            54907
2     repub          84988        77.39      21.57     repub           187114
3     repub          11459        48.34      51.25       dem            27321
4     repub           8391        73.07      26.22     repub            22754
5     repub          23980        86.49      12.35     repub            57623
6       dem           5318        23.51      76.31       dem            10746
  percent_white percent_black percent_asian percent_hispanic per_capita_income
1            76            18             1                2             24571
2            83             9             1                4             26766
3            46            46             0                5             16829
4            75            22             0                2             17427
5            88             1             0                8             20730
6            22            71             0                6             18628
  median_rent median_age
1         668       37.5
2         693       41.5
3         382       38.3
4         351       39.4
5         403       39.6
6         276       39.6

We’ll use this data to explore voting outcomes within the U.S.’s 2-party system. Here’s a list of candidates by year:

year	Republican candidate	Democratic candidate
2020	Donald Trump	Joe Biden
2016	Donald Trump	Hillary Clinton
2012	Mitt Romney	Barack Obama

Exercise 0: Review

Part a

How many, or roughly what percent, of the 3000+ counties did the Republican candidate win in 2020?

• Take a guess.
• Then make a plot of the winner variable.
• Then discuss what follow-up questions you might have (and that our data might help us answer).

Part b

The repub_pct_20 variable provides more detail about the Republican support in each county. Construct a plot of repub_pct_20.

Notice that the distribution of Republican support from county to county is slightly left skewed or negatively skewed.

What follow-up questions do you have?

Exercise 1: Quantitative vs Quantitative Intuition Check

Be Quick

Don’t spend more than 3 minutes on this!

Below is a scatterplot of the Republican support in 2020 vs 2016. Notice that:

• both variables are quantitative, and get their own axes
• the response variable is on the y-axis, demonstrating how repub_pct_20 might be predicted by repub_pct_16, not vice versa

Try to replicate this using ggplot(). THINK:

• What info do you need to set up the canvas?
• What geometric layer (geom_???) might add these dots / points for each county? We haven’t learned this yet, just take some guesses.

Exercise 2: 2 Quantitiative Variables

Run each chunk below to build up a a scatterplot of repub_pct_20 vs repub_pct_16 with different glyphs representing each county. Address or think about any prompts in the comments (#).

# Set up the plotting frame
# How does this differ than the frame for our histogram of repub_pct_20 alone?
ggplot(elections, aes(y = repub_pct_20, x = repub_pct_16))

# Add a layer of points for each county
# Take note of the geom!
ggplot(elections, aes(y = repub_pct_20, x = repub_pct_16)) +
  geom_point()

# Change the shape of the points
# What happens if you change the shape to another number?
ggplot(elections, aes(y = repub_pct_20, x = repub_pct_16)) +
  geom_point(shape = 3)

# YOU TRY: Modify the code to make the points "orange"
# NOTE: Try to anticipate if "color" or "fill" will be useful here. Then try both.
ggplot(elections, aes(y = repub_pct_20, x = repub_pct_16)) +
  geom_point()

# Add a layer that represents each county by the state it's in
# Take note of the geom and the info it needs to run!
ggplot(elections, aes(y = repub_pct_20, x = repub_pct_16)) +
  geom_text(aes(label = state_abbr))

Exercise 3: Reflect

Summarize the relationship between the Republican support in 2020 and 2016. Be sure to comment on:

• the strength of the relationship (weak/moderate/strong)
  
• the direction of the relationship (positive/negative)
  
• outliers (in what state do counties deviate from the national trend? Any ideas why this might be the case?)

Exercise 4: Visualizing trend

The trend of the relationship between repub_pct_20 and repub_pct_16 is clearly positive and (mostly) linear. We can highlight this trend by adding a model “smooth” to the plot:

ggplot(elections, aes(y = repub_pct_20, x = repub_pct_16)) +
  geom_point() +
  geom_smooth()

Part a

Construct a new plot that contains the model smooth but does not include the individual point glyphs.

Part b

By default, geom_smooth() adds a smooth, localized model line. To examine the “best” linear model, we can specify method = "lm". It’s pretty similar in this example!

ggplot(elections, aes(y = repub_pct_20, x = repub_pct_16)) +
  geom_point() +
  geom_smooth(method = "lm")

Exercise 5: Your Turn

To examine how the 2020 results are related to some county demographics, construct scatterplots of repub_pct_20 vs median_rent, and repub_pct_20 vs median_age. Summarize the relationship between these two variables and comment on which is the better predictor of repub_pct_20, median_rent or median_age.

# Scatterplot of repub_pct_20 vs median_rent



# Scatterplot of repub_pct_20 vs median_age

Exercise 6: A Sad Scatterplot

Next, let’s explore the relationship between a county’s 2020 Republican support repub_pct_20 and the historical political trends in its state. In this case repub_pct_20 is quantitative, but historical is categorical. Explain why a scatterplot might not be an effective visualization for exploring this relationship. (What questions does / doesn’t it help answer?)

ggplot(elections, aes(y = repub_pct_20, x = historical)) +
  geom_point()

Exercise 7: Quantitative vs Categorical – Violins & Boxes

Though the above scatterplot did group the counties by historical category, it’s nearly impossible to pick out meaningful patterns in 2020 Republican support in each category. Let’s try adding 2 different geom layers to the frame:

# Side-by-side violin plots
ggplot(elections, aes(y = repub_pct_20, x = historical)) +
  geom_violin()

# Side-by-side boxplots (defined below)
ggplot(elections, aes(y = repub_pct_20, x = historical)) +
  geom_boxplot()

Box plots are constructed from five numbers - the minimum, 25th percentile, median, 75th percentile, and maximum value of a quantitative variable:

REFLECT:

Summarize what you’ve learned about the 2020 Republican county-level support within and between red/purple/blue states.

Exercise 8: Quantitative vs Categorical – Intuition Check

Be Quick

Don’t spend more than 3 minutes on this!

We can also visualize the relationship between repub_pct_20 and historical using our familiar density plots. In the plot below, notice that we simply created a separate density plot for each historical category. (The plot itself is “bad” but we’ll fix it below.) Try to adjust the code chunk below, which starts with a density plot of repub_pct_20 alone, to re-create this image.

ggplot(elections, aes(x = repub_pct_20)) +
  geom_density()

Exercise 9: Quantitative vs Categorical – Density Plots

Work through the chunks below and address the comments therein.

# Name two "bad" things about this plot
ggplot(elections, aes(x = repub_pct_20, fill = historical)) +
  geom_density()

# What does scale_fill_manual do?
ggplot(elections, aes(x = repub_pct_20, fill = historical)) +
  geom_density() +
  scale_fill_manual(values = c("blue", "purple", "red"))

# What does alpha = 0.5 do?
# Play around with different values of alpha, between 0 and 1
ggplot(elections, aes(x = repub_pct_20, fill = historical)) +
  geom_density(alpha = 0.5) +
  scale_fill_manual(values = c("blue", "purple", "red"))

# What does facet_wrap do?!
ggplot(elections, aes(x = repub_pct_20, fill = historical)) +
  geom_density() +
  scale_fill_manual(values = c("blue", "purple", "red")) +
  facet_wrap(~ historical)

# Let's try a similar grouping strategy with a histogram instead of density plot.
# Why is this terrible?
ggplot(elections, aes(x = repub_pct_20, fill = historical)) +
  geom_histogram(color = "white") +
  scale_fill_manual(values = c("blue", "purple", "red"))

Exercise 10

We’ve now learned 3 (of many) ways to visualize the relationship between a quantitative and categorical variable: side-by-side violins, boxplots, and density plots.

• Which do you like best?
• What is one pro of density plots relative to boxplots?
• What is one con of density plots relative to boxplots?

Exercise 11: Categorical vs Categorical – Intuition Check

Finally, let’s simply explore who won each county in 2020 (winner_20) and how this breaks down by historical voting trends in the state. That is, let’s explore the relationship between 2 categorical variables! Following the same themes as above, we can utilize grouping features such as fill/color or facets to distinguish between different categories of winner_20 and historical.

Be Quick

Spend at most 5 minutes on the following intuition check. Adjust the code below to recreate the following two plots.

# Plot 1: adjust this to recreate the top plot
ggplot(elections, aes(x = historical)) +
  geom_bar()

# Plot 2: adjust this to recreate the bottom plot
ggplot(elections, aes(x = winner_20)) +
  geom_bar()

Exercise 12: Categorical vs Categorical

Construct the following 4 bar plot visualizations.

# A stacked bar plot
# How are the "historical" and "winner_20" variables mapped to the plot, i.e. what roles do they play?
ggplot(elections, aes(x = historical, fill = winner_20)) +
  geom_bar()

# A faceted bar plot
ggplot(elections, aes(x = winner_20)) +
  geom_bar() +
  facet_wrap(~ historical)

# A side-by-side bar plot
# Note the new argument to geom_bar
ggplot(elections, aes(x = historical, fill = winner_20)) +
  geom_bar(position = "dodge")

# A proportional bar plot
# Note the new argument to geom_bar
ggplot(elections, aes(x = historical, fill = winner_20)) +
  geom_bar(position = "fill")

Part a

Name one pro and one con of using the “proportional bar plot” instead of one of the other three options.

Part b

What’s your favorite bar plot from part and why?

Exercise 13: Practice (now or later)

Decide

Decide what’s best for you:

• Try this extra practice now.
• Reflect on the above exercises and come back to this extra practice later (but before the next class).

Import some daily weather data from a few locations in Australia:

weather <- read.csv("https://mac-stat.github.io/data/weather_3_locations.csv")

Construct plots that address the research questions in each chunk. You might make multiple plots–there are many ways to do things!. However, don’t just throw spaghetti at the wall.

Reflect before doing anything. What types of variables are these? How might you plot just 1 of the variables, and then tweak the plot to incorporate the other?

# How do 3pm temperatures (temp3pm) differ by location?

# How might we predict the 3pm temperature (temp3pm) by the 9am temperature (temp9am)?

# How do the number of rainy days (raintoday) differ by location?

4.4 Exercises (optional)

The above visualizations are foundational and important. But they’re not the only way to visualize the variables in our dataset.

Optional Exercise 1: Many Categories

Suppose we wanted to better understand how the 2020 Republican support varied from county to county within each state. Since repub_pct_20 is quantitative and state_abbr is categorical, we could make a density plot of repub_pct_20 for each state. Reflect on why this is bad.

ggplot(elections, aes(x = repub_pct_20, fill = state_abbr)) + 
  geom_density(alpha = 0.5)

A facet wrap would also be bad!

ggplot(elections, aes(x = repub_pct_20)) + 
  geom_density(alpha = 0.5) + 
  facet_wrap(~ state_abbr)

When we want to compare the distribution of some quantitative outcome among many groups / categories, a ridgeline plot can be a good option. These are also called joy plots, named after the album cover for “Unknown Pleasures” by Joy Division. (Look it up!) To make a ridgeline plot, we can use the geom_density_ridges() function from the ggridges package.

# Install ggridges package
library(ggridges)

# Make our first joy plot
# THINK: What DON'T you like about this?
ggplot(elections, aes(x = repub_pct_20, y = state_abbr)) + 
  geom_density_ridges()

# Let's put the states in order by Republican support, not alphabet
# How do you think fct_reorder works? We'll learn about this later in the semester.
ggplot(elections, aes(x = repub_pct_20, y = fct_reorder(state_abbr, repub_pct_20))) + 
  geom_density_ridges(alpha = 0.5)

# YOUR TURN: color/fill the ridges according to a state's historical voting patterns 
# and add meaningful axis labels

Follow-up questions

• Which states tend to have the most variability in outcomes from county to county? The least?
• What other interesting patterns do you notice?
• Does this plot prompt any other questions?

Optional Exercise 2: Total Outcomes by State

Let’s import some new data and counts up the total votes (Republican and Democratic) by state, not county. This was wrangled from the elections data!

elections_by_state <- read.csv("https://mac-stat.github.io/data/election_2020_by_state.csv")

For example, we might make a scatterplot of the 2020 vs 2016 outcomes:

ggplot(elections_by_state, aes(y = repub_pct_20, x = repub_pct_16)) + 
  geom_point()

BUT this isn’t the easiest way to communicate or identify the changes from 1 year to the next.

# YOU TRY
# Start by creating a "scatterplot" of state_abbr (y-axis) by 2020 Republican support on the x-axis
# Color the points red
# Scroll to solutions below when you're ready

# Check it out
ggplot(elections_by_state, aes(x = repub_pct_20, y = state_abbr)) + 
  geom_point(color = "red")

# YOU TRY
# Reorder the states in terms of their 2020 Republican support (not alphabet)
# Scroll to solutions below when you're ready

# Check it out
ggplot(elections_by_state, aes(x = repub_pct_20, y = fct_reorder(state_abbr, repub_pct_20))) + 
  geom_point(color = "red")

# Finally, add ANOTHER layer of points for the 2016 outcomes
# What info does this new geom_point() layer need to run?
ggplot(elections_by_state, aes(x = repub_pct_20, y = fct_reorder(state_abbr, repub_pct_20))) + 
  geom_point(color = "red") + 
  geom_point(aes(x = repub_pct_16, y = state_abbr))

Reflect on the following

• What do you think this plot needs? Try it! You might need to do some digging online.
• Summarize the main takeaways from the plots. Which states changed the most from 2016 to 2020? The least? Where did the Republican support increase? Where did it decrease?
• What other questions are you left with?

4.5 Solutions

Click for Solutions

# Import data
survey <- read.csv("https://ajohns24.github.io/data/112/about_us_2024.csv")

# How many students have now filled out the survey?
nrow(survey)

[1] 28

# What type of variables do we have?
str(survey)

'data.frame':   28 obs. of  4 variables:
 $ cafe_mac         : chr  "Cheesecake" "Cheese pizza" "udon noodles" "egg rolls" ...
 $ minutes_to_campus: int  15 10 4 7 5 35 5 15 7 20 ...
 $ fave_temp        : num  18 24 18 10 18 7 75 24 13 16 ...
 $ hangout          : chr  "the mountains" "a beach" "the mountains" "a beach" ...

EXAMPLE 1: Hangout preferences

# Attach a package needed to use the ggplot function
library(tidyverse)

# Make a ggplot
ggplot(survey, aes(x = hangout)) + 
  geom_bar()

EXAMPLE 2: Temperature preferences

ggplot(survey, aes(x = fave_temp)) + 
  geom_histogram(color = "white", binwidth = 5)

ggplot(survey, aes(x = fave_temp)) + 
  geom_density()

4.5.1 Exercise 0:

ggplot(elections, aes(x = winner_20)) + 
  geom_bar()

ggplot(elections, aes(x = repub_pct_20)) + 
  geom_histogram(color = "white")

ggplot(elections, aes(x = repub_pct_20)) + 
  geom_density()

Exercise 1: quantitative vs quantitative intuition check

See next exercise.

Exercise 2: 2 quantitiative variables

# Set up the plotting frame
# How does this differ than the frame for our histogram of repub_pct_20 alone?
# ANSWER: we added a y-axis variable
ggplot(elections, aes(y = repub_pct_20, x = repub_pct_16))

# Add a layer of points for each county
# Take note of the geom: geom_point
ggplot(elections, aes(y = repub_pct_20, x = repub_pct_16)) +
  geom_point()

# Change the shape of the points
# What happens if you change the shape to another number?
ggplot(elections, aes(y = repub_pct_20, x = repub_pct_16)) +
  geom_point(shape = 3)

# YOU TRY: Modify the code to make the points "orange"
# NOTE: Try to anticipate if "color" or "fill" will be useful here. Then try both.
ggplot(elections, aes(y = repub_pct_20, x = repub_pct_16)) +
  geom_point(color = "orange")

# Add a layer that represents each county by the state it's in
# Take note of the geom and the info it needs to run!
ggplot(elections, aes(y = repub_pct_20, x = repub_pct_16)) +
  geom_text(aes(label = state_abbr))

Exercise 3: Reflect

There’s a strong, positive association – the higher the Republican support in 2016, the higher it was in 2020. There are some counties in Texas and Utah where the R support in 2020 was disproportionately higher than in 2016.

Exercise 4: Visualizing trend

ggplot(elections, aes(y = repub_pct_20, x = repub_pct_16)) +
  geom_point() +
  geom_smooth()

Part a

ggplot(elections, aes(y = repub_pct_20, x = repub_pct_16)) +
  geom_smooth()

Part b

ggplot(elections, aes(y = repub_pct_20, x = repub_pct_16)) +
  geom_point() +
  geom_smooth(method = "lm")

Exercise 5: Your turn

There’s a moderate, positive association between R support and median age – the older the average age in a county, the higher the R support tends to be. However, there’s a stronger, negative association between R support and median rent – the higher the rent (a proxy for cost of living), the lower the R support tends to be.

# Scatterplot of repub_pct_20 vs median_rent
ggplot(elections, aes(y = repub_pct_20, x = median_rent)) +
  geom_point() 

# Scatterplot of repub_pct_20 vs median_age
ggplot(elections, aes(y = repub_pct_20, x = median_age)) +
  geom_point() 

Exercise 6: A sad scatterplot

See next exercise.

Exercise 7: quantitative vs categorical – violins & boxes

# Side-by-side violin plots
ggplot(elections, aes(y = repub_pct_20, x = historical)) +
  geom_violin()

# Side-by-side boxplots (defined below)
ggplot(elections, aes(y = repub_pct_20, x = historical)) +
  geom_boxplot()

REFLECT:

There’s quite a bit of range in county-level R support within blue, purple, and red states. However, R support tends to be higher in red states and lower in blue states.

Exercise 8: quantitative vs categorical – intuition check

See next exercise.

Exercise 9: quantitative vs categorical – density plots

# The colors used don't match up with the blue, purple, red labels
# The density plots are on top of each other
ggplot(elections, aes(x = repub_pct_20, fill = historical)) +
  geom_density()

# scale_fill_manual "hard codes" or defines what colors to use for the fill categories
ggplot(elections, aes(x = repub_pct_20, fill = historical)) +
  geom_density() +
  scale_fill_manual(values = c("blue", "purple", "red"))

# alpha = 0.5 adds transparency
# the closer alpha is to 0, the more transparent.
# the closer alpha is to 1, the more opaque.
ggplot(elections, aes(x = repub_pct_20, fill = historical)) +
  geom_density(alpha = 0.5) +
  scale_fill_manual(values = c("blue", "purple", "red"))

# facet_wrap separates the density plots into "facets" for each historical group
ggplot(elections, aes(x = repub_pct_20, fill = historical)) +
  geom_density() +
  scale_fill_manual(values = c("blue", "purple", "red")) +
  facet_wrap(~ historical)

# Let's try a similar grouping strategy with a histogram instead of density plot.
# Why is this terrible?
ggplot(elections, aes(x = repub_pct_20, fill = historical)) +
  geom_histogram(color = "white") +
  scale_fill_manual(values = c("blue", "purple", "red"))

Exercise 10

• One pro of density plots relative to boxplots: doesn’t oversimplify the data / boil the data down to just 5 numbers.

• Name one con of density plots relative to boxplots: boxplots can be easier to interpret

Exercise 11: categorical vs categorical intuition check

see exercise below

Exercise 12: categorical vs categorical

# A stacked bar plot
# historical = x axis / bar categories
# winner_20 = fills the bars
ggplot(elections, aes(x = historical, fill = winner_20)) +
  geom_bar()

# A faceted bar plot
ggplot(elections, aes(x = winner_20)) +
  geom_bar() +
  facet_wrap(~ historical)

# A side-by-side bar plot
# Note the new argument to geom_bar
ggplot(elections, aes(x = historical, fill = winner_20)) +
  geom_bar(position = "dodge")

# A proportional bar plot
# Note the new argument to geom_bar
ggplot(elections, aes(x = historical, fill = winner_20)) +
  geom_bar(position = "fill")

Part a

pro = easier to compare the relative outcomes in blue vs purple vs red states con = lose track of how many counties fall into blue vs purple vs red states

Exercise 13: Practice (now or later)

weather <- read.csv("https://mac-stat.github.io/data/weather_3_locations.csv")

# How do 3pm temperatures (temp3pm) differ by location?
ggplot(weather, aes(x = temp3pm, fill = location)) + 
  geom_density(alpha = 0.5)

ggplot(weather, aes(y = temp3pm, x = location)) + 
  geom_boxplot()

# How might we predict the 3pm temperature (temp3pm) by the 9am temperature (temp9am)?
ggplot(weather, aes(y = temp3pm, x = temp9am)) + 
  geom_point()

# How do the number of rainy days (raintoday) differ by location?
ggplot(weather, aes(x = location, fill = raintoday)) + 
  geom_bar()

Optional exercise 1: Dealing with lots of categories

# Install ggridges package
library(ggridges)

# Make our first joy plot
# THINK: What DON'T you like about this?
ggplot(elections, aes(x = repub_pct_20, y = state_abbr)) + 
  geom_density_ridges()

# Let's put the states in order by Republican support, not alphabet
# How do you think fct_reorder works? We'll learn about this later in the semester.
ggplot(elections, aes(x = repub_pct_20, y = fct_reorder(state_abbr, repub_pct_20))) + 
  geom_density_ridges(alpha = 0.5)

# YOUR TURN: color/fill the ridges according to a state's historical voting patterns 
# and add meaningful axis labels
ggplot(elections, aes(x = repub_pct_20, y = fct_reorder(state_abbr, repub_pct_20), fill = historical)) + 
  geom_density_ridges(alpha = 0.5) + 
  labs(y = "state", x = "2020 Republican support (%)") + 
  scale_fill_manual(values = c("blue", "purple", "red"))

Optional exercise 2: total outcomes by state

elections_by_state <- read.csv("https://mac-stat.github.io/data/election_2020_by_state.csv")

ggplot(elections_by_state, aes(y = repub_pct_20, x = repub_pct_16)) + 
  geom_point()

# YOU TRY
# Start by creating a "scatterplot" of state_abbr (y-axis) by 2020 Republican support on the x-axis
# Color the points red
ggplot(elections_by_state, aes(x = repub_pct_20, y = state_abbr)) + 
  geom_point(color = "red")

# YOU TRY
# Reorder the states in terms of their 2020 Republican support (not alphabet)
ggplot(elections_by_state, aes(x = repub_pct_20, y = fct_reorder(state_abbr, repub_pct_20))) + 
  geom_point(color = "red")

# Finally, add ANOTHER layer of points for the 2016 outcomes
# What info does this new geom_point() layer need to run?
ggplot(elections_by_state, aes(x = repub_pct_20, y = fct_reorder(state_abbr, repub_pct_20))) + 
  geom_point(color = "red") + 
  geom_point(aes(x = repub_pct_16, y = state_abbr))