We will start by creating a simple scatterplot and use that to introduce aesthetic mappings and geometric objects -- the fundamental building blocks of ggplot2.
We will then walk you through visualizing distributions of single variables as well as visualizing relationships between two or more variables.
We'll finish off with saving your plots and troubleshooting tips.
That one line of code loads the core tidyverse; the packages that you will use in almost every data analysis.
It also tells you which functions from the tidyverse conflict with functions in base R (or from other packages you might have loaded)[^data-visualize-1].
[^data-visualize-1]: You can eliminate that message and force conflict resolution to happen on demand by using the conflicted package, which becomes more important as you load more packages.
You can learn more about conflicted at <https://conflicted.r-lib.org>.
If you run this code and get the error message `there is no package called 'tidyverse'`, you'll need to first install it, then run `library()` once again.
In addition to tidyverse, we will also use the **palmerpenguins** package, which includes the `penguins` dataset containing body measurements for penguins on three islands in the Palmer Archipelago, and the ggthemes package, which offers a colorblind safe color palette.
`penguins` contains `r nrow(penguins)` observations collected and made available by Dr. Kristen Gorman and the Palmer Station, Antarctica LTER[^data-visualize-2]. In this context, a variable refers to an attribute of all the penguins, and an observation refers to all the attributes of a single penguin.
Our ultimate goal in this chapter is to recreate the following visualization displaying the relationship between flipper lengths and body masses of these penguins, taking into consideration the species of the penguin.
Next, we need to tell `ggplot()` the variables from this data frame that we want to map to visual properties (**aesthetics**) of the plot.
The `mapping` argument of the `ggplot()` function defines how variables in your dataset are mapped to visual properties of your plot.
The `mapping` argument is always paired with the `aes()` function, and the `x` and `y` arguments of `aes()` specify which variables to map to the x and y axes.
For now, we will only map flipper length to the `x` aesthetic and body mass to the `y` aesthetic.
ggplot2 looks for the mapped variables in the `data` argument, in this case, `penguins`.
The following plots show the result of adding these mappings, one at a time.
Our empty canvas now has more structure -- it's clear where flipper lengths will be displayed (on the x-axis) and where body masses will be displayed (on the y-axis).
But the penguins themselves are not yet on the plot.
This is because we have not yet articulated, in our code, how to represent the observations from our data frame on our plot.
To do so, we need to define a **geom**: the geometrical object that a plot uses to represent data.
These geometric objects are made available in ggplot2 with functions that start with `geom_`.
People often describe plots by the type of geom that the plot uses.
For example, bar charts use bar geoms (`geom_bar()`), line charts use line geoms (`geom_line()`), boxplots use boxplot geoms (`geom_boxplot()`), and so on.
Scatterplots break the trend; they use the point geom: `geom_point()`.
It doesn't yet match our "ultimate goal" plot, but using this plot we can start answering the question that motivated our exploration: "What does the relationship between flipper length and body mass look like?" The relationship appears to be positive (as flipper length increases, so does body mass), fairly linear (the points are clustered around a line instead of a curve), and moderately strong (there isn't too much scatter around such a line).
We're seeing this message because there are two penguins in our dataset with missing body mass and flipper length values and ggplot2 has no way of representing them on the plot.
You don't need to worry about understanding the following code yet (you will learn about it in @sec-data-transform), but it's one way of identifying the observations with `NA`s for either body mass or flipper length.
Like R, ggplot2 subscribes to the philosophy that missing values should never silently go missing.
This type of warning is probably one of the most common types of warnings you will see when working with real data -- missing values are a very common issue and you'll learn more about them throughout the book, particularly in @sec-missing-values.
For the remaining plots in this chapter we will suppress this warning so it's not printed alongside every single plot we make.
Scatterplots are useful for displaying the relationship between two variables, but it's always a good idea to be skeptical of any apparent relationship between two variables and ask if there may be other variables that explain or change the nature of this apparent relationship.
When a variable is mapped to an aesthetic, ggplot2 will automatically assign a unique value of the aesthetic (here a unique color) to each unique level of the variable (each of the three species), a process known as **scaling**.
ggplot2 will also add a legend that explains which values correspond to which levels.
We have successfully added smooth curves, but this plot doesn't look like the plot from @sec-ultimate-goal, which only has one curve for the entire dataset as opposed to separate curves for each of the penguin species.
When aesthetic mappings are defined in `ggplot()`, at the *global* level, they're inherited by each of the subsequent geom layers of the plot.
However, each geom function in ggplot2 can also take a `mapping` argument, which allows for aesthetic mappings at the *local* level.
Since we want points to be colored based on species but don't want the smooth curves to be separated out for them, we should specify `color = species` for `geom_point()` only.
It's generally not a good idea to represent information using only colors on a plot, as people perceive colors differently due to color blindness or other color vision differences.
Therefore, in addition to color, we can also map `species` to the `shape` aesthetic.
Some of the arguments to `labs()` might be self explanatory: `title` adds a title and `subtitle` adds a subtitle to the plot.
Other arguments match the aesthetic mappings, `x` is the x-axis label, `y` is the y-axis label, and `color` and `shape` define the label for the legend.
7. Add the following caption to the plot you made in the previous exercise: "Data come from the palmerpenguins package." Hint: Take a look at the documentation for `labs()`.
In bar plots of categorical variables with non-ordered levels, like the penguin `species` above, it's often preferable to reorder the bars based on their frequencies.
Doing so requires transforming the variable to a factor (how R handles categorical data) and then reordering the levels of that factor.
A histogram divides the x-axis into equally spaced bins and then uses the height of a bar to display the number of observations that fall in each bin.
In the graph above, the tallest bar shows that 39 observations have a `body_mass_g` value between 3,500 and 3,700 grams, which are the left and right edges of the bar.
You can set the width of the intervals in a histogram with the binwidth argument, which is measured in the units of the `x` variable.
You should always explore a variety of binwidths when working with histograms, as different binwidths can reveal different patterns.
In the plots below a binwidth of 20 is too narrow, resulting in too many bars, making it difficult to determine the shape of the distribution.
Similarly, a binwidth of 2,000 is too high, resulting in all data being binned into only three bars, and also making it difficult to determine the shape of the distribution.
An alternative visualization for distributions of numerical variables is a density plot.
A density plot is a smoothed-out version of a histogram and a practical alternative, particularly for continuous data that comes from an underlying smooth distribution.
We won't go into how `geom_density()` estimates the density (you can read more about that in the function documentation), but let's explain how the density curve is drawn with an analogy.
Imagine a histogram made out of wooden blocks.
Then, imagine that you drop a cooked spaghetti string over it.
The shape the spaghetti will take draped over blocks can be thought of as the shape of the density curve.
It shows fewer details than a histogram but can make it easier to quickly glean the shape of the distribution, particularly with respect to modes and skewness.
```{r}
#| fig-alt: >
#| A density plot of body masses of penguins. The distribution is unimodal
#| and right skewed, ranging between approximately 2500 to 6500 grams.
To visualize a relationship we need to have at least two variables mapped to aesthetics of a plot.
In the following sections you will learn about commonly used plots for visualizing relationships between two or more variables and the geoms used for creating them.
A **boxplot** is a type of visual shorthand for measures of position (percentiles) that describe a distribution that are commonly used in statistical analysis of data.
- A box that indicates the range of the middle half of the data, a distance known as the interquartile range (IQR), stretching from the 25th percentile of the distribution to the 75th percentile.
In the middle of the box is a line that displays the median, i.e. 50th percentile, of the distribution.
These three lines give you a sense of the spread of the distribution and whether or not the distribution is symmetric about the median or skewed to one side.
Alternatively, we can make frequency polygons with `geom_freqpoly()`.
`geom_freqpoly()` performs the same calculation as `geom_histogram()`, but instead of displaying the counts with bars, it uses lines instead.
It's much easier to understand overlapping lines than bars of `geom_histogram()`.
There are a few challenges with this type of plot, which we will come back to in @sec-cat-num on exploring the covariation between a categorical and a numerical variable.
We can also use overlaid density plots, with `species` mapped to both `color` and `fill` aesthetics and using the `alpha` aesthetic to add transparency to the filled density curves.
This aesthetic takes values between 0 (completely transparent) and 1 (completely opaque).
We can use segmented bar plots to visualize the distribution between two categorical variables.
In creating this bar chart, we map the variable we want to divide the data into first to the `x` aesthetic and the variable we then further want to divide each group into to the `fill` aesthetic.
Below are two segmented bar plots, both displaying the relationship between `island` and `species`, or specifically, visualizing the distribution of `species` within each island.
The plot on the left shows the frequencies of each species of penguins on each island and the plot on the right shows the relative frequencies (proportions) of each species within each island (despite the incorrectly labeled y-axis that says "count").
The relative frequency plot, created by setting `position = "fill"` in the geom is more useful for comparing species distributions across islands since it's not affected by the unequal numbers of penguins across the islands.
Based on the plot on the left, we can see that Gentoo penguins all live on Biscoe island and make up roughly 75% of the penguins on that island, Chinstrap all live on Dream island and make up roughly 50% of the penguins on that island, and Adelie live on all three islands and make up all of the penguins on Torgersen.
So far you've learned about scatterplots (created with `geom_point()`) and smooth curves (created with `geom_smooth()`) for visualizing the relationship between two numerical variables.
A scatterplot is probably the most commonly used plot for visualizing the relationship between two variables.
However adding too many aesthetic mappings to a plot makes it cluttered and difficult to make sense of.
Another way, which is particularly useful for categorical variables, is to split your plot into **facets**, subplots that each display one subset of the data.
Generally, however, we recommend that you assemble your final reports using Quarto, a reproducible authoring system that allows you to interleave your code and your prose and automatically include your plots in your write-ups.
Start by carefully comparing the code that you're running to the code in the book.
R is extremely picky, and a misplaced character can make all the difference.
Make sure that every `(` is matched with a `)` and every `"` is paired with another `"`.
Sometimes you'll run the code and nothing happens.
Check the left-hand of your console: if it's a `+`, it means that R doesn't think you've typed a complete expression and it's waiting for you to finish it.
In this case, it's usually easy to start from scratch again by pressing ESCAPE to abort processing the current command.
We started with the basic idea that underpins ggplot2: a visualization is a mapping from variables in your data to aesthetic properties like position, color, size and shape.
You then learned about increasing the complexity and improving the presentation of your plots layer-by-layer.
You also learned about commonly used plots for visualizing the distribution of a single variable as well as for visualizing relationships between two or more variables, by levering additional aesthetic mappings and/or splitting your plot into small multiples using faceting.
We'll use visualizations again and again through out this book, introducing new techniques as we need them as well as do a deeper dive into creating visualizations with ggplot2 in @sec-layers through @sec-exploratory-data-analysis.
With the basics of visualization under your belt, in the next chapter we're going to switch gears a little and give you some practical workflow advice.
We intersperse workflow advice with data science tools throughout this part of the book because it'll help you stay organized as you write increasing amounts of R code.
