Graphics with ggplot2


Goal: By the end of this lab, you will be able to use ggplot2 to build different data graphics

I encourage you to create an RMarkdown document to use for this lab. This way you have a record of all your code, and you can also easily take notes on what you’re doing as you do it to refer back to later. At the very least you should create an R script; don’t just type everything at the console, or you won’t be able to refer to it later!

Setting up

Remember Before we can use a library like ggplot2, we have to load it. In this case, we load the tidyverse package, which automatically loads ggplot2 for us (since it depends on it).

Note: Remember, you shouldn’t copy and paste code directly from the web. Type it out yourself so that you slow yourself down a bit to process what you’re reading, and to develop your muscle memory.

(Click the “Code” button on the right to show the code)


Why ggplot2?

Advantages of ggplot2:

  • Consistent underlying ** grammar of graphics** (Wilkinson, 2005)
  • Is a mature and complete graphics system
  • Plot specification is at a high level of abstraction
  • Flexible
  • Has a theme system to polish plot appearance (more on this later)
  • Used by many, many people

What is The Grammar Of Graphics?

The big idea: independenly specify plot building blocks and combine them to create just about any kind of graphical display you want. Building blocks of a graph include:

  • data (data=)
  • aesthetic mappings (aes())
  • geometric objects (geom_*())
  • statistical transformations
  • scales
  • coordinate systems
  • position adjustments
  • faceting

Using ggplot2, we can specify different parts of the plot, and combine them together using the + operator. [Note that the + operator is similar to the %>% pipe operator, but is not interchangeable]

Example: Housing prices

Let’s start by looking at some data on housing prices:

housing <- read.csv("")
## Observations: 7,803
## Variables: 11
## $ State            <fctr> AK, AK, AK, AK, AK, AK, AK, AK, AK, AK, AK, ...
## $ region           <fctr> West, West, West, West, West, West, West, We...
## $ Date             <dbl> 2010.25, 2010.50, 2009.75, 2010.00, 2008.00, ...
## $ Home.Value       <int> 224952, 225511, 225820, 224994, 234590, 23371...
## $ Structure.Cost   <int> 160599, 160252, 163791, 161787, 155400, 15745...
## $ Land.Value       <int> 64352, 65259, 62029, 63207, 79190, 76256, 729...
## $ Land.Share..Pct. <dbl> 28.6, 28.9, 27.5, 28.1, 33.8, 32.6, 31.3, 29....
## $ Home.Price.Index <dbl> 1.481, 1.484, 1.486, 1.481, 1.544, 1.538, 1.5...
## $ Land.Price.Index <dbl> 1.552, 1.576, 1.494, 1.524, 1.885, 1.817, 1.7...
## $ Year             <int> 2010, 2010, 2009, 2009, 2007, 2008, 2008, 200...
## $ Qrtr             <int> 1, 2, 3, 4, 4, 1, 2, 3, 4, 1, 2, 2, 3, 4, 1, ...

(Data originally from, via Jordan Crouser at Smith College)

Geometric Objects and Aesthetics

Geometric Objects (geom)

Geometric objects or geoms are the actual marks we put on a plot. Examples include:

  • points (geom_point(), for scatter plots, dot plots, etc.)
  • lines (geom_line(), for time series, trend lines, etc.)
  • boxplot (geom_boxplot(), for, um…)

among others

A plot must have at least one geom, but you can combine multiple geoms in a single plot. Remember that you can add elements to an existing plot using the + operator (elements can be chained together in a single command, or intermediate plots can be assigned to a variable and added to later).

You can see a list of the geom_*() functions in ggplot2 using the following command:"geom_", package = "ggplot2")

In RStudio, if you simply type geom_ and then press the tab key, you will see a dropdown list of possible ways to complete the text. This is a useful trick generally, to save repetitive typing. Once you have completed a function name and typed the open paren (, tab will also show you a list of valid argument names for that function.

Aesthetic Mappings (aes)

In ggplot2, aesthetic means “something you can see”. Each aesthetic is a mapping between a visual cue and a variable. For example, we can map variables to the following cues:

  • position (i.e., on the x and y axes)
  • color (the “outside” color of a geometric object)
  • fill (the “inside” color of a geometric object)
  • shape (of points)
  • line type
  • size

Each type of geom accepts only a subset of all aesthetics — refer to the help pages of individual geom_() functions to see what mappings each geom accepts. Aesthetic mappings are set with the aes() function.


Now that we know about geometric objects and aesthetic mapping, we’re ready to make out first ggplot: a scatterplot. We’ll use geom_point to do this, which requires aes mappings for x and y. Other mappings (such as color) are optional.


## Get a subset of the data (more on filter() later)
hp2013Q1 <- housing %>% filter(Date == 2013.25)

ggplot(hp2013Q1, aes(y = Structure.Cost, x = Land.Value)) +
  1. Create a scatterplot of the value of each home in the first quarter of 2013 as a function of the value of the land.

Possible Solution

ggplot(hp2013Q1, aes(y = Home.Value, x = Land.Value)) +

Plot objects

The output of the ggplot() function is an object. Since we want to modify the plot that we created above, it’s helpful to store the plot object in a named variable.

base_plot <- ggplot(hp2013Q1, aes(y = Structure.Cost, x = Land.Value))

To actually show the plot, we just print it, as we would print the value of a numeric value or a data frame.


Notice that although the axes are set up and labeled, there’s no data being depicted. That’s because we haven’t specified any geoms – in other words, we haven’t told R what we actually want it to draw. However, the aesthetic mapping is defined, and if we take this base plot and add geoms to it, the resulting plots will use the mapping that we defined in base_plot.

Let’s add some points!

base_plot + geom_point()
  1. We have a lovely scatterplot now, but we haven’t stored it. Store the scatterplot you created in the previous exercise as an object called home_value_plot.

Possible Solution

home_value_plot <- ggplot(hp2013Q1, aes(y = Home.Value, x = Land.Value)) +


A plot constructed with ggplot can have more than one geom. For example, we could connect all of the points using geom_line(). By default, the aesthetic mapping defined in the base plot is carried over to any new geoms that we add. Note that now we see both points and lines.

base_plot + geom_point() + geom_line()
  1. Does it make sense to connect the observations with geom_line() in this case? Do the lines help us understand the connections between the observations? What do the lines represent?


Not all geometric objects are simple shapes – geom_smooth() includes both a line and a ribbon, where the line is a “smoothed” moving average of the y variable, and the band is a 95% confidence band, represent our uncertainty about what the moving average actually would be if we had infinite data.

base_plot +
  geom_point() + 

Other smoothing methods and band definitions are available too. You can find out more about the various options by looking at the documentation page for geom_smooth().


Each geom accepts a particular set of aesthetics (i.e., mappings) – for example, geom_text() accepts a labels mapping. This mapping wasn’t defined in the base plot, so we can add it here.

Note that in the following plot we are not using geom_point() or geom_line() – we have only geom_text() since we only want the state labels to be drawn, not points or lines.

base_plot + 
  geom_text(aes(label = State), size = 3)

Aesthetic Mapping vs. Assignment

Note that the variables are mapped to aesthetics with the aes() function, while fixed visual cues are set outside the aes() call. This sometimes leads to confusion, as in this example:

base_plot +
  geom_point(aes(size = 2), # not what you want, because 2 is not a variable
             color = 'red') # this just turns all points red)

The aes() function cal also be used outside of a call to a geom. Here we update the base_plot with an additional mapping, assigning the color cue to the home value variable.

base_plot <- base_plot +
  aes(color = Home.Value)
  1. In your home_value_plot, map color to the cost of the structure, and show your scatterplot.

Sample solution

home_value_plot + 
  aes(color = Structure.Cost) +

Mapping Variables to Other Aesthetics

Other aesthetics are mapped in the same way as x and y ni the previous example.

base_plot +
  geom_point(aes(shape = region))

Scales: Controlling Aesthetic Mapping

Aesthetic mapping (i.e., with aes()) only says that a variable should be mapped to an aesthetic. It doesn’t say how that should happen. For example, when mapping a variable (say, z) to shape with aes(shape = z), you don’t say what shapes should be used. Similarly, aes(color = z) doesn’t say what colors should be used. Describing what colors/shapes/sizes, etc. to use is done by modifying the corresponding scale. In ggplot2, scales include:

  • position
  • color, fill, and alpha (these control the “outer” and “inner” colors and the opacity (from transparent at 0 to opaque at 1), respectively, of the geometric objects)
  • size
  • shape
  • linetype

Scales are modified with a series of functions using a scale_<aesthetic>_<type> naming template. Try typing scale_ followed by the tab key to see a list of scale modification functions.

Common Scale Arguments

The following arguments are common to most scales in ggplot2:

  • name: the first argument specifies the axis or legend title
  • limits: the minimum and maximum of the scale
  • breaks: the points along the scale where labels should appear
  • labels: the text that appears at each break

Specific scale functions may have additional arguments; for example, the scale_color_continuous() function has arguments low and high for setting the colors at the low and high end of the scale.

Scale Modification Examples

Start by constructing a dot plot to show the distribution of home values by Date and State.

home_plot <- ggplot(housing, aes(y = State, x = Home.Price.Index)) +
  geom_point(aes(color = Date),
             alpha = 0.3,
             size = 1.5,
             position = position_jitter(width = 0, height = 0.25))

First, let’s change the label on the vertical axis.

home_plot <- home_plot +
  scale_y_discrete(name = "State Abbreviation")

Now let’s modify the breaks and labels for the x axis and color scales:

home_plot +
  scale_color_continuous(breaks = c(1975.25, 1994.25, 2013.25),
                         labels = c(1971, 1994, 2013))

Now let’s change the low and high values to blue and red for a plot that’s a bit more dramatic:

home_plot <- home_plot +
    breaks = c(1975.25, 1994.25, 2013.25),
    labels = c(1971, 1994, 2013),
    low = "blue", high = "red")

Using different color scales

ggplot2 has a wide variety of color scales; here is an example using scale_color_gradient2() to interpolate between three different colors:

home_plot +
    breaks = c(1975.25, 1994.25, 2013.25),
    labels = c(1971, 1994, 2013),
    low = "blue", high = "red", mid = "gray60",
    midpoint = 1994.25)
## Scale for 'colour' is already present. Adding another scale for
## 'colour', which will replace the existing scale.
  1. Since a home price index of 1 is an important benchmark, it is worth highlighting as a contextual reference in our plot. Use geom_vline() to add a dotted, black, vertical line to the plot we created above.

Possible solution

home_plot +
    aes(xintercept = 1), 
    linetype = 3, 
    color = "black") + 
    breaks = c(1975.25, 1994.25, 2013.25),
    labels = c(1971, 1994, 2013),
    low = "blue", high = "red", mid = "gray60",
    midpoint = 1994.25)
## Scale for 'colour' is already present. Adding another scale for
## 'colour', which will replace the existing scale.
  1. Recall that layers in ggplot2 are added sequentially. How would you put the dotted vertical line you created in the previous exercise behind the data values?

Available Scales

Note: In RStudio, you can type scale_ followed by TAB to get the whole list of available scales.


The idea behind faceting is to create separate graphs for subsets of the data, and tile those graphs in a manner that makes it easy to visually compare them.

ggplot2 offers two functions for creating facets:

  • facet_wrap(): define subsets as the levels of a single grouping variable, and tile the resulting plots in one dimension, “wrapping around” as needed.
  • facet_grid(): define subsets as the crossing of two grouping variables

By splitting data into separate subplots it is possible to keep the amount of clutter in a single plot under control, while keeping all the information in easy visual proximity to facilitate comparison among plots.

Example: What is the trend in housing prices in each state?

Let’s start by using a technique we already know: map State to color:

state_plot <- ggplot(housing, aes(x = Date, y = Home.Value))

state_plot +
  geom_line(aes(color = State))

This plot is horrendous. There are two problems: the distinctions among colors are too fine-grained to be able to see them, and the lines obscure each other.

Faceting to the rescue?

We can fix the previous plot by faceting by State rather than mapping State to color:

state_plot +
  geom_line() +
  facet_wrap(~State, ncol = 10)

Notice the tilde (~) syntax before the variable name. This is a convention borrowed from the syntax R uses to define regression models, and which the lattice graphics package (as well as the mosaic package) use to define plots.

The facet_grid() function can be used to create facets that vary according to two grouping variables. Its syntax is

facet_grid(y ~ x)

where y and x are the names of grouping variables that define the rows (vertical) and columns (horizontal) of the faceted grid, respectively.

  1. Use a facet_wrap and/or facet_grid to create a data graphic of your choice that illustrates something interesting about home prices.

Getting credit

Please respond to the following prompt on Slack as a Direct Message to me. Tag your response with the hashtag #lab2:

Post the command and the resulting image that you created in Exercise 7. You can assume that I’ve read in the data as housing, but do not rely on any other stored variables.

This lab is based on the “Introduction to R Graphics with ggplot2” workshop, which is a product of the Data Science Services team Harvard University. The original source is released under a Creative Commons Attribution-ShareAlike 4.0 Unported. This lab was adapted for SDS192: and Introduction to Data Science in Spring 2017 by R. Jordan Crouser at Smith College, and further adapted for STAT209: Data Computing and Visualization in Spring 2018 by Colin Dawson at Oberlin College.