Topic 4: Data Visualization and a Grammar of Graphics

Data Visualization

Through this workbook you’ll learn techniques for data visualization. You’ll think about how the type of plot chosen impacts what information is conveyed (or lost). Choosing the wrong plot type can result in a useless plot (at best) or even a plot which is misleading. Think about the types (numerical, categorical) of variables you are working with and how they dictate which plot type should be utilized – for example, a scatterplot is not appropriate in every scenario!

Objectives

Workbook Objectives: After completing this workbook you should be able to:

Read a plot, describe any interesting features, and accurately convey the story being told by the plot.
Identify and utilize the most appropriate plot types for your scenario by considering the types of variables you are working with.
Utilize color, size, and shape to show additional dimensions of data in a plot, but also recognize that adding more information to a plot negatively impacts our ability to read the graphic.
Use your new found knowledge of data visualization to generate informative plots which uncover a compelling (and truthful) story about the relationships that exist between variables.

Note, much of this notebook is inspired by Chapter 3 of R for Data Science by Hadley Wickham and Garrett Grolemund. This is a great freely-available resource. Please check it out.

The Grammar of Graphics

Grammar of Graphics: Jeffrey Gitomer said “Your grammar is a reflection of your image”. Here, we take the converse literally. Your image is a reflection of your grammar. Just like a well-written sentence follows the rules of grammar, so does an informative statistical graphic.

Graphics need a subject (an underlying dataset)
Graphics need verbs (a type of plotting structure, called a geom)
Graphics need adjectives (attributes attached to the visualized data, called aesthetics)
Graphics need context (a title, scales, axes, legends, etc.)

In this workbook we explore ggplot2 and think about graphics in terms of their layered grammar.

A Note on Plots: Choosing an appropriate plot is extremely important in data visualization. Some plots don’t make sense for certain variable types. For example, a scatterplot in the case of two-categorical predictors is quite a silly choice – the only thing this plot tells us is that every combination of fuel type and engine brand exists in our dataset. We have no idea which combinations are most or least popular.

The following are some recommended basic plot types under certain scenarios:

A single numerical variable: a histogram, boxplot, or density plot
A single categorical variable: a bar plot
Numerical versus numerical: a scatterplot or heatmap
Numerical versus categorical: side-by-side boxplots, stacked or overlaid densities, stacked or overlaid histograms
Categorical versus categorical: bar plot with fill color, mosaic plot, heatmap

Check Your Understanding: A Better Plot

Which of the following plot types would have been a better choice to display the engine brand and fuel type data? Select all that apply.

mutable ok_response = (response, n) => { return html`Loading...` };
viewof q1 = Inputs.checkbox(
  new Map([
    ["A histogram", 1],
    ["A barplot with fill color", 2],
    ["A heatmap", 3],
    ["A dotplot", 4],
    ["Side-by-side boxplots", 5]
  ]),
  {value: JSON.parse(localStorage.getItem("q1_selected") ?? "[]") ?? []}
);

{
  localStorage.setItem("q1_selected", JSON.stringify(q1));
  localStorage.setItem("q1_correct", "2,3");
  localStorage.setItem("q1_result", (!q1 || q1.length === 0) ? "unattempted" : (q1.toString() === "2,3" ? "correct" : "incorrect"));
}

ok_checkbox(q1, "2,3");

Getting Started: Exploring Data

Throughout this workbook, we will explore fuel efficiencies of multiple classes of vehicles using the mpg data frame.

Remember that a data frame is like an Excel spreadsheet. Explore the mpg data frame by typing mpg in the following code block and running it.

mpg

Notice that when you execute a line of code which just calls the name of a data frame, a snippet of that data frame is printed out. This is true for other objects (variables, vectors, functions, etc) in R as well.

Some Basic Exploratory Functions: It is useful to know more about your dataset when you first start working with data. R has a few exploratory functions which you should know about: mpg |> names() prints out a list of names of your data frame’s columns, mpg |> head() will show you the first six rows of the mpg dataset, mpg |> dim() will show you the number of rows and columns in the dataset, and mpg |> glimpse() will show you information about how R is treating the columns (int and num denote numerical values while chr and fct represent character strings and categorical variables respectively). You might also try mpg |> skim(), where the skim() function comes from the {skimr} package. The skim() function provides lots of high-level information about your dataset from just a single function call. Use the code block below to try each of these functions and use the output to answer the following questions:

Check Your Understanding: Variables

How many variables are contained in the mpg dataset?

viewof q2 = Inputs.radio(
  new Map([
    ["1", 1],
    ["11", 2],
    ["234", 3],
    ["245", 4],
    ["2574", 5]
  ]),
  {value: JSON.parse(localStorage.getItem("q2_selected") ?? "null")}
);

{
  localStorage.setItem("q2_selected", JSON.stringify(q2));
  localStorage.setItem("q2_correct", "2");
  localStorage.setItem("q2_result", q2 === null ? "unattempted" : (q2 == 2 ? "correct" : "incorrect"));
}

ok_response(q2, "2");

Check Your Understanding: Observations

How many observations (records) are contained in the mpg dataset?

viewof q3 = Inputs.radio(
  new Map([
    ["1", 1],
    ["11", 2],
    ["234", 3],
    ["245", 4],
    ["2574", 5]
  ]),
  {value: JSON.parse(localStorage.getItem("q3_selected") ?? "null")}
);

{
  localStorage.setItem("q3_selected", JSON.stringify(q3));
  localStorage.setItem("q3_correct", "3");
  localStorage.setItem("q3_result", q3 === null ? "unattempted" : (q3 == 3 ? "correct" : "incorrect"));
}

ok_response(q3, "3");

Check Your Understanding: Numerical Variables

Which of the variables is R interpreting as numerical? Select all that apply.

viewof q4 = Inputs.checkbox(
  new Map([
    ["manufacturer", 1],
    ["model", 2],
    ["displ (displacement)", 3],
    ["cyl (cylinders)", 4],
    ["trans (transmission type)", 5],
    ["year", 6],
    ["drv (drive type)", 7],
    ["cty (city fuel economy)", 8],
    ["hwy (highway fuel economy)", 9],
    ["fl (fuel type)", 10],
    ["class (vehicle class)", 11]
  ]),
  {value: JSON.parse(localStorage.getItem("q4_selected") ?? "[]") ?? []}
);

{
  localStorage.setItem("q4_selected", JSON.stringify(q4));
  localStorage.setItem("q4_correct", "3,4,6,8,9");
  localStorage.setItem("q4_result", (!q4 || q4.length === 0) ? "unattempted" : (q4.toString() === "3,4,6,8,9" ? "correct" : "incorrect"));
}

ok_checkbox(q4, "3,4,6,8,9");

The diamonds data frame is also available to you. See if you can get your basic exploratory functions to help you answer the same questions about the diamonds dataset using the code block below.

Were you able to obtain the information you were looking for? Be sure to ask a question if not!

Building and Interpreting Plots

The code below creates a plot of highway miles per gallon (hwy) against engine displacement displ (a measure of the size of an engine).

mpg |> 
  ggplot() + 
  geom_point(aes(x = displ, y = hwy))

Use the plot above to answer the following questions.

Check Your Understanding: Reading a Plot I

Which of the following is true?

viewof q5 = Inputs.radio(
  new Map([
    ["Highway miles per gallon and engine displacement are independent.", 1],
    ["Highway miles per gallon and engine displacement are associated.", 2],
    ["It is impossible to tell whether the variables are associated or independent.", 3],
    ["Highway miles per gallon and engine displacement are both independent and associated.", 4]
  ]),
  {value: JSON.parse(localStorage.getItem("q5_selected") ?? "null")}
);

{
  localStorage.setItem("q5_selected", JSON.stringify(q5));
  localStorage.setItem("q5_correct", "2");
  localStorage.setItem("q5_result", q5 === null ? "unattempted" : (q5 == 2 ? "correct" : "incorrect"));
}

ok_response(q5, "2");

Check Your Understanding: Reading a Plot II

Describe the relationship between highway miles per gallon and engine displacement.

viewof q6 = Inputs.radio(
  new Map([
    ["Engine displacement and highway miles per gallon share a negative, linear relationship.", 1],
    ["Engine displacement and highway miles per gallon share a positive, linear relationship.", 2],
    ["Engine displacement and highway miles per gallon share a negative, non-linear relationship.", 3],
    ["Engine displacement and highway miles per gallon share a positive, non-linear relationship.", 4]
  ]),
  {value: JSON.parse(localStorage.getItem("q6_selected") ?? "null")}
);

{
  localStorage.setItem("q6_selected", JSON.stringify(q6));
  localStorage.setItem("q6_correct", "3");
  localStorage.setItem("q6_result", q6 === null ? "unattempted" : (q6 == 3 ? "correct" : "incorrect"));
}

ok_response(q6, "3");

Check Your Understanding: Reading a Plot III

Which is the best practical interpretation of the plot?

viewof q7 = Inputs.radio(
  new Map([
    ["Generally, engines with higher displacement are associated with lower average highway miles per gallon ratings.", 1],
    ["Generally, engines with higher displacement are associated with higher average highway miles per gallon ratings.", 2],
    ["Higher engine displacement causes lower average highway miles per gallon ratings.", 3],
    ["Engine displacement causes higher average highway miles per gallon ratings.", 4]
  ]),
  {value: JSON.parse(localStorage.getItem("q7_selected") ?? "null")}
);

{
  localStorage.setItem("q7_selected", JSON.stringify(q7));
  localStorage.setItem("q7_correct", "1");
  localStorage.setItem("q7_result", q7 === null ? "unattempted" : (q7 == 1 ? "correct" : "incorrect"));
}

ok_response(q7, "1");

A Note on Plotting Structure: Using ggplot() to create plots in R is daunting at first, but with practice you will notice the structure is very consistent and convenient!

Our plot consists of at least two pieces (subject and verb): mpg |> ggplot() tells ggplot that the subject of our plot is the data contained in the mpg table, while the geom_point() verb tells ggplot that we want our data displayed as points (a scatterplot). The aes() inside of geom_point() tells ggplot some of the adjectives for each individual observation – here, just the location!
In general a simple plot takes the form below – (where the items in “ALL CAPS” are replaced by your data frame, desired geometry type, and aesthetic mappings).

DATA |>
  ggplot() + 
  geom_TYPE(aes(MAPPINGS))

Use the code block below to make a scatterplot of average city miles per gallon (cty) explained by engine displacement (displ).


mpg |>
  ggplot() + 
  geom_point(aes(x = displ, y = cty))

Now make a scatterplot of average highway miles per gallon (hwy) explained by number of cylinders (cyl).


mpg |>
  ggplot() + 
  geom_point(aes(x = cyl, y = hwy))

Notice that this plot isn’t very useful because the cylinders variable takes on very few levels. We may be better off if we treat cyl as if it were a categorical variable here. Check out some of the other geometry layers available to you in ggplot here. Try building a set of side-by-side boxplots. If you get an error, be sure to read it – R suspects that the plot produced wasn’t the one you wanted and provides a suggestion.

Hint 4

R is considering both the x- and y-variables to be numeric. The warning message suggests adding a group parameter inside of the aes() function. Which variable should be mapped to group?

mpg |>
  ggplot() + 
  geom_boxplot(aes(x = cyl, y = hwy, group = ___))

Hint 5 (Solved)

We want a boxplot for each cylinder “group.” Map cyl to the group aesthetic.

mpg |>
  ggplot() + 
  geom_boxplot(aes(
    x = cyl, 
    y = hwy,
    group = cyl))


mpg |>
  ggplot() + 
  geom_boxplot(aes(
    x = cyl, 
    y = hwy, 
    group = cyl))

Consider the variables for vehicle class (class) and drive type (drv) as you answer the following questions.

Check Your Understanding: Appropriate Plot Types I

Why would a scatterplot not be an appropriate visual to use with these two variables?

viewof q8 = Inputs.radio(
  new Map([
    ["Trick question -- a scatterplot is always appropriate.", 1],
    ["At least one of the two variables is not numerical.", 2],
    ["A scatterplot would be okay but there are better options.", 3],
    ["There isn't enough data for a scatterplot.", 4]
  ]),
  {value: JSON.parse(localStorage.getItem("q8_selected") ?? "null")}
);

{
  localStorage.setItem("q8_selected", JSON.stringify(q8));
  localStorage.setItem("q8_correct", "2");
  localStorage.setItem("q8_result", q8 === null ? "unattempted" : (q8 == 2 ? "correct" : "incorrect"));
}

ok_response(q8, "2");

Check Your Understanding: Appropriate Plot Types II

Which of the following plot types would be appropriate for building a visual with the class and drive type variables? Select all that apply.

viewof q9 = Inputs.checkbox(
  new Map([
    ["A histogram", 1],
    ["A side-by-side boxplot", 2],
    ["A bar graph with fill color", 3],
    ["A heatmap", 4],
    ["A mosaicplot", 5]
  ]),
  {value: JSON.parse(localStorage.getItem("q9_selected") ?? "[]") ?? []}
);

{
  localStorage.setItem("q9_selected", JSON.stringify(q9));
  localStorage.setItem("q9_correct", "3,4,5");
  localStorage.setItem("q9_result", (!q9 || q9.length === 0) ? "unattempted" : (q9.toString() === "3,4,5" ? "correct" : "incorrect"));
}

ok_checkbox(q9, "3,4,5");

Use the code block below to make a useful plot of class using geom_bar() with the aesthetics x = class and fill = drv. Think about the plot you create – what does it tell you?

Hint 3

Four things to be updated: change the geometry layer to geom_bar(), map class to x, remove the y aesthetic (bar heights are determined by counts automatically), and map drv to fill.

mpg |>
  ggplot() + 
  geom_point(aes(x = displ, y = hwy))


mpg |>
  ggplot() + 
  geom_bar(aes(x = class, fill = drv))

Note: There are many different geom types, and different aesthetic properties which can be passed to geoms. We will see examples throughout the rest of this activity, but reading the entirety of Chapter 3 in Hadley Wickham’s R for Data Science would be a great start for those of you who are more interested in data visualization.

Plotting More Than Two Variables at Once

Let’s go back to our original plot of hwy versus displ. Maybe we want to color the points in the scatterplot according to the class of the vehicle. Add a third aesthetic, color = class, to the code below and re-run the plot.


mpg |> 
  ggplot() + 
  geom_point(aes(
    x = displ, 
    y = hwy, 
    color = class))

There’s a lot going on here, and it is hard to read. Try copying the above plotting command, but appending facet_wrap(~ class, nrow = 2) as a new layer to the plot – notice that plot layers are added with the + symbol. Again, think about the resulting plot and how it compares to your previous colored plot.

Hint 3

Fill in the blank with the facet_wrap() layer. Check the prompt for how to use facet_wrap().

mpg |> 
  ggplot() + 
  geom_point(aes(
    x = displ, 
    y = hwy, 
    color = class)) + 
  facet_wrap(___)

Hint 4

The blank after the tilde (~) inside of facet_wrap() identifies the column whose categories should each get their own plot panel.

mpg |> 
  ggplot() + 
  geom_point(aes(
    x = displ, 
    y = hwy, 
    color = class)) + 
  facet_wrap(~___, nrow = 2)

Hint 5 (Solved)

We want an individual plot for each of the vehicle classes.

mpg |> 
  ggplot() + 
  geom_point(aes(
    x = displ, 
    y = hwy, 
    color = class)) + 
  facet_wrap(~ class, nrow = 2)


mpg |> 
  ggplot() + 
  geom_point(aes(
    x = displ, 
    y = hwy, 
    color = class)) + 
  facet_wrap(~ class, nrow = 2)

Check Your Understanding: Investigating Arguments

What does setting the argument nrow = 2 do? Don’t guess – think about how you can determine what the nrow argument here does.

viewof q10 = Inputs.radio(
  new Map([
    ["It is impossible to tell.", 1],
    ["It tells ggplot to only use half the data so that plots are less cluttered.", 2],
    ["It tells R to try arranging the plots into two rows.", 3],
    ["It tells R to include two plots per row.", 4],
    ["Nothing -- it is irrelevant and ignored.", 5]
  ]),
  {value: JSON.parse(localStorage.getItem("q10_selected") ?? "null")}
);

{
  localStorage.setItem("q10_selected", JSON.stringify(q10));
  localStorage.setItem("q10_correct", "3");
  localStorage.setItem("q10_result", q10 === null ? "unattempted" : (q10 == 3 ? "correct" : "incorrect"));
}

ok_response(q10, "3");

Categorical Variables

We’ve seen plots for categorical variables earlier in this workbook. Let’s revisit a barplot with fill, similar to the one we constructed between class and drv earlier and check two alternative plots which give different insights. We’ll explore the manual versus automatic transmissions.

mpg |>
  count(trans)

trans	n
auto(av)	5
auto(l3)	2
auto(l4)	83
auto(l5)	39
auto(l6)	6
auto(s4)	3
auto(s5)	3
auto(s6)	16
manual(m5)	58
manual(m6)	19

Unfortunately the trans variable contains more detailed transmission information than what we are after. Omitting the last four characters from levels in the trans column would leave us with the information we want. While this sort of data wrangling is beyond the scope of these notebooks (and you won’t be asked to do it on your own), I’ll still show you how we can remove those last four characters below.

We can achieve our objective using the str_sub() function – this function subsets a string. Again, you won’t need this function for our course but, because we’re using it here, I’ll explain how it works. The first argument is the name of the column whose strings we want to subset, the second argument is the position of the first character we’d like to keep and the third argument is the position of the final character we’d like to keep. Using a negative number here is shorthand for “from the end”. That is, we’d like to keep all of the characters starting with the first and stopping five characters from the end of the string.

Run the code below to see the plot.

From the plot above we can tell that there are about twice as many vehicles in our dataset with automatic transmissions as there are with manual transmissions. Create a similar plot below, which fills the bars by the drv variable.

Hint 2

Do you remember how to add a fill aesthetic?

mpg |>
  mutate(trans2 = str_sub(trans, 1, -5)) |>
  ggplot() + 
  geom_bar(aes(x = trans2)) +
  labs(x = "Transmission Type", 
       y = "Count",
       title = "Distribution of Transmission Types")

Hint 3 (Solved)

Map the drive variable (drv) to the fill aesthetic inside aes().

mpg |>
  mutate(trans2 = str_sub(trans, 1, -5)) |>
  ggplot() + 
  geom_bar(aes(x = trans2, fill = drv)) +
  labs(x = "Transmission Type", 
       y = "Count",
       title = "Distribution of Transmission Types")


mpg |>
  mutate(trans2 = str_sub(trans, 1, -5)) |>
  ggplot() + 
  geom_bar(aes(x = trans2, fill = drv)) +
  labs(x = "Transmission Type", 
       y = "Count",
       title = "Distribution of Transmission Types")

The problem now is the difficulty in comparing the proportions of drive types within each of these classes. We can fix this by using the position argument. Outside of aes() but still within geom_bar(), add an argument position = "fill" to the pre-built plot. You’ll need to include a comma after aes() since commas separate arguments. Think about what is gained and lost in this new plot.

Hint 2

Fill in the blank to set the position to "fill".

mpg |>
  mutate(trans2 = str_sub(trans, 1, -5)) |>
  ggplot() + 
  geom_bar(aes(
    x = trans2, 
    fill = drv),
    position = ___) +
  labs(x = "Transmission Type", 
       y = "Count",
       title = "Distribution of Transmission Types")

Hint 3 (Solved)

mpg |>
  mutate(trans2 = str_sub(trans, 1, -5)) |>
  ggplot() + 
  geom_bar(aes(
    x = trans2, 
    fill = drv), 
    position = "fill") +
  labs(x = "Transmission Type", 
       y = "Count",
       title = "Distribution of Transmission Types")


mpg |>
  mutate(trans2 = str_sub(trans, 1, -5)) |>
  ggplot() + 
  geom_bar(aes(
    x = trans2, 
    fill = drv), 
    position = "fill") +
  labs(x = "Transmission Type", 
       y = "Count",
       title = "Distribution of Transmission Types")

Great! But the problem now is that we’ve lost the idea that there are more automatic vehicles than there are manual vehicles in this dataset. As a third option, we can consider a mosaic plot.

mpg |>
  mutate(trans2 = str_sub(trans, 1, -5)) |>
  ggplot() + 
  geom_mosaic(aes(
    x = product(trans2), 
    fill = drv)) +
  labs(x = "Transmission Type", 
       y = "Drive Type",
       title = "")

The advantage to the mosaic plot is that it contains all of the information from the two separate plots above, but it is all combined into one plot! We’ve built a visualization that manages to convey that the proportion of automatic vehicles in our data set outweighs the proportion of manuals, and we can also easily compare the distribution of drive types within each of these two classes.

Submit

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Question Hash

The hash below encodes your responses to the multiple choice and checkbox questions in this activity.

function buildQuestionResults() {
  return {
    notebook: "Topic 4: Data Visualization",
    type: "questions",
    timestamp: new Date().toISOString(),
    questions: {
      q1_better_plot: {
        selected: q1,
        correct_answer: "2,3",
        result: (!q1 || q1.length === 0) ? "unattempted" : (q1.toString() === "2,3" ? "correct" : "incorrect")
      },
      q2_variables: {
        selected: q2,
        correct_answer: "2",
        result: q2 === null ? "unattempted" : (q2 == 2 ? "correct" : "incorrect")
      },
      q3_observations: {
        selected: q3,
        correct_answer: "3",
        result: q3 === null ? "unattempted" : (q3 == 3 ? "correct" : "incorrect")
      },
      q4_numerical_variables: {
        selected: q4,
        correct_answer: "3,4,6,8,9",
        result: (!q4 || q4.length === 0) ? "unattempted" : (q4.toString() === "3,4,6,8,9" ? "correct" : "incorrect")
      },
      q5_reading_plot_1: {
        selected: q5,
        correct_answer: "2",
        result: q5 === null ? "unattempted" : (q5 == 2 ? "correct" : "incorrect")
      },
      q6_reading_plot_2: {
        selected: q6,
        correct_answer: "3",
        result: q6 === null ? "unattempted" : (q6 == 3 ? "correct" : "incorrect")
      },
      q7_reading_plot_3: {
        selected: q7,
        correct_answer: "1",
        result: q7 === null ? "unattempted" : (q7 == 1 ? "correct" : "incorrect")
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      q8_appropriate_plots_1: {
        selected: q8,
        correct_answer: "2",
        result: q8 === null ? "unattempted" : (q8 == 2 ? "correct" : "incorrect")
      },
      q9_appropriate_plots_2: {
        selected: q9,
        correct_answer: "3,4,5",
        result: (!q9 || q9.length === 0) ? "unattempted" : (q9.toString() === "3,4,5" ? "correct" : "incorrect")
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      q10_nrow_argument: {
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        correct_answer: "3",
        result: q10 === null ? "unattempted" : (q10 == 3 ? "correct" : "incorrect")
      }
    }
  };
}

function toBase64(str) {
  return btoa(unescape(encodeURIComponent(str)));
}

question_hash = {
  q1; q2; q3; q4; q5; q6; q7; q8; q9; q10;
  return toBase64(JSON.stringify(buildQuestionResults()));
}

html`<div style="font-family: monospace; font-size: 0.85em; background: #f5f5f5; padding: 12px; border-radius: 6px; word-break: break-all; border: 1px solid #ddd; user-select: all; cursor: pointer;" onclick="navigator.clipboard.writeText(this.innerText)">
  ${question_hash}
</div>
<p style="margin-top: 8px; font-size: 0.9em; color: #555;">
  Click the box to copy to clipboard.
</p>`

Exercise Hash

Click the button below to generate your exercise submission code. This hash encodes your work on the graded code exercises in this activity.

You must have attempted the graded exercises before clicking — clicking generates a snapshot of your current results. If you have completed the activity over multiple sessions, please go back through and hit the Run Code button on each graded exercise before generating the hash below, to ensure your most recent results are recorded.

Summary

Main Takeaways

Variable types dictate plot types. Always consider whether your variables are numerical or categorical before choosing a plot. A scatterplot requires two numerical variables; a histogram, boxplot, or density plot give insight to the distribution of a single numerical variable; a bar plot is appropriate for a single categorical variable and adding a fill color accommodates a second categorical variable; side-by-side boxplots work for numerical vs. categorical comparisons.
Plotting with ggplot() follows a grammar of graphics and a layered structure, with + separating each layer. The basic structure is:

data_frame_name |>
  ggplot() +
  geom_TYPE(aes(MAPPINGS))

Aesthetics (aes()) map variables from your data to visual properties of the plot (position, color, fill, size, shape). Properties that don’t depend on the data go outside aes().
Additional dimensions can be added via color, fill, size, shape, or faceting — but more information in a single plot can make it harder to read. Sometimes separate faceted plots communicate more clearly than a single dense plot.
Mosaic plots are a powerful choice for two categorical variables — they simultaneously convey both the marginal distributions and the conditional distributions within each group.

Looking Ahead

This activity concludes our foundational and “R-onramping” work. Over the next several workbooks, we’ll transition to probability and working with probability distributions. We’ll start with basic, counting-based probability before moving to the binomial distribution and normal distribution.

Loaded Libraries

These interactive workbooks allow setup requirements to be hidden so that we can focus on the task at hand — in this workbook, plotting. When you make the switch to using a standalone R instance you’ll need to install and load libraries to expand R’s functionality. You can do this with the install.packages() and library() functions. You would need {ggplot2}, {dplyr}, {skimr}, and {ggmosaic} to run the code for this workbook in a local R session.