The ggplot2 plotting system: ggplot()

An overview of the ggplot2 plotting system in R with ggplot()
module 3
week 3
data viz

Department of Biostatistics, Johns Hopkins


September 15, 2022

Pre-lecture materials

Read ahead

Read ahead

Before class, you can prepare by reading the following materials:



Material for this lecture was borrowed and adopted from

Learning objectives

Learning objectives

At the end of this lesson you will:

  • Be able to build up layers of graphics using ggplot()
  • Be able to modify properties of a ggplot() including layers and labels

The ggplot2 Plotting System

In this lesson, we will get into a little more of the nitty gritty of how ggplot2 builds plots and how you can customize various aspects of any plot.

Previously, we used the qplot() function to quickly put points on a page.

  • The qplot() function’s syntax is very similar to that of the plot() function in base graphics so for those switching over, it makes for an easy transition.

But it is worth knowing the underlying details of how ggplot2 works so that you can really exploit its power.

Basic components of a ggplot2 plot

Key components

A ggplot2 plot consists of a number of key components.

  • A data frame: stores all of the data that will be displayed on the plot

  • aesthetic mappings: describe how data are mapped to color, size, shape, location

  • geoms: geometric objects like points, lines, shapes

  • facets: describes how conditional/panel plots should be constructed

  • stats: statistical transformations like binning, quantiles, smoothing

  • scales: what scale an aesthetic map uses (example: left-handed = red, right-handed = blue)

  • coordinate system: describes the system in which the locations of the geoms will be drawn

It is essential to organize your data into a data frame before you start with ggplot2 (and all the appropriate metadata so that your data frame is self-describing and your plots will be self-documenting).

When building plots in ggplot2 (rather than using qplot()), the “artist’s palette” model may be the closest analogy.

Essentially, you start with some raw data, and then you gradually add bits and pieces to it to create a plot.


Plots are built up in layers, with the typically ordering being

  1. Plot the data
  2. Overlay a summary
  3. Add metadata and annotation

For quick exploratory plots you may not get past step 1.

Example: BMI, PM2.5, Asthma

To demonstrate the various pieces of ggplot2 we will use a running example from the Mouse Allergen and Asthma Cohort Study (MAACS). Here, the question we are interested in is

“Are overweight individuals, as measured by body mass index (BMI), more susceptible than normal weight individuals to the harmful effects of PM2.5 on asthma symptoms?”

There is a suggestion that overweight individuals may be more susceptible to the negative effects of inhaling PM2.5.

This would suggest that increases in PM2.5 exposure in the home of an overweight child would be more deleterious to his/her asthma symptoms than they would be in the home of a normal weight child.

We want to see if we can see that difference in the data from MAACS.


Because the individual-level data for this study are protected by various U.S. privacy laws, we cannot make those data available.

For the purposes of this lesson, we have simulated data that share many of the same features of the original data, but do not contain any of the actual measurements or values contained in the original dataset.


We can look at the data quickly by reading it in as a tibble with read_csv() in the tidyverse package.

maacs <- read_csv(here("data", "bmi_pm25_no2_sim.csv"),
                  col_types = "nnci")
# A tibble: 517 × 4
   logpm25 logno2_new bmicat        NocturnalSympt
     <dbl>      <dbl> <chr>                  <int>
 1   1.25       1.18  normal weight              1
 2   1.12       1.55  overweight                 0
 3   1.93       1.43  normal weight              0
 4   1.37       1.77  overweight                 2
 5   0.775      0.765 normal weight              0
 6   1.49       1.11  normal weight              0
 7   2.16       1.43  normal weight              0
 8   1.65       1.40  normal weight              0
 9   1.55       1.81  normal weight              0
10   2.04       1.35  overweight                 3
# … with 507 more rows

The outcome we will look at here (NocturnalSymp) is the number of days in the past 2 weeks where the child experienced asthma symptoms (e.g. coughing, wheezing) while sleeping.

The other key variables are:

  • logpm25: average level of PM2.5 over the course of 7 days (micrograms per cubic meter) on the log scale

  • logno2_new: exhaled nitric oxide on the log scale

  • bmicat: categorical variable with BMI status

Building up in layers

First, we can create a ggplot object that stores the dataset and the basic aesthetics for mapping the x- and y-coordinates for the plot.


Here, we will eventually be plotting the log of PM2.5 and NocturnalSymp variable.

g <- ggplot(maacs, aes(x = logpm25, 
                       y = NocturnalSympt))
data: logpm25, logno2_new, bmicat, NocturnalSympt [517x4]
mapping:  x = ~logpm25, y = ~NocturnalSympt
faceting: <ggproto object: Class FacetNull, Facet, gg>
    compute_layout: function
    draw_back: function
    draw_front: function
    draw_labels: function
    draw_panels: function
    finish_data: function
    init_scales: function
    map_data: function
    params: list
    setup_data: function
    setup_params: function
    shrink: TRUE
    train_scales: function
    vars: function
    super:  <ggproto object: Class FacetNull, Facet, gg>
[1] "gg"     "ggplot"

You can see above that the object g contains the dataset maacs and the mappings.

Now, normally if you were to print() a ggplot object a plot would appear on the plot device, however, our object g actually does not contain enough information to make a plot yet.

g <- maacs %>%
        ggplot(aes(logpm25, NocturnalSympt))

Nothing to see here!

First plot with point layer

To make a scatter plot, we need add at least one geom, such as points.

Here, we add the geom_point() function to create a traditional scatter plot.

g <- maacs %>%
        ggplot(aes(logpm25, NocturnalSympt))
g + geom_point()

Scatterplot of PM2.5 and days with nocturnal symptoms

How does ggplot know what points to plot? In this case, it can grab them from the data frame maacs that served as the input into the ggplot() function.

Adding more layers


Because the data appear rather noisy, it might be better if we added a smoother on top of the points to see if there is a trend in the data with PM2.5.

g + 
  geom_point() + 

Scatterplot with smoother

The default smoother is a loess smoother, which is flexible and nonparametric but might be too flexible for our purposes. Perhaps we’d prefer a simple linear regression line to highlight any first order trends. We can do this by specifying method = "lm" to geom_smooth().

g + 
  geom_point() + 
  geom_smooth(method = "lm")

Scatterplot with linear regression line

Here, we can see there appears to be a slight increasing trend, suggesting that higher levels of PM2.5 are associated with increased days with nocturnal symptoms.


Let’s use the ggplot() function with our palmerpenguins dataset example and make a scatter plot with flipper_length_mm on the x-axis, bill_length_mm on the y-axis, colored by species, and a smoother by adding a linear regression.

# try it yourself

# A tibble: 344 × 8
   species island    bill_length_mm bill_depth_mm flipper_…¹ body_…² sex    year
   <fct>   <fct>              <dbl>         <dbl>      <int>   <int> <fct> <int>
 1 Adelie  Torgersen           39.1          18.7        181    3750 male   2007
 2 Adelie  Torgersen           39.5          17.4        186    3800 fema…  2007
 3 Adelie  Torgersen           40.3          18          195    3250 fema…  2007
 4 Adelie  Torgersen           NA            NA           NA      NA <NA>   2007
 5 Adelie  Torgersen           36.7          19.3        193    3450 fema…  2007
 6 Adelie  Torgersen           39.3          20.6        190    3650 male   2007
 7 Adelie  Torgersen           38.9          17.8        181    3625 fema…  2007
 8 Adelie  Torgersen           39.2          19.6        195    4675 male   2007
 9 Adelie  Torgersen           34.1          18.1        193    3475 <NA>   2007
10 Adelie  Torgersen           42            20.2        190    4250 <NA>   2007
# … with 334 more rows, and abbreviated variable names ¹​flipper_length_mm,
#   ²​body_mass_g


Because our primary question involves comparing overweight individuals to normal weight individuals, we can stratify the scatter plot of PM2.5 and nocturnal symptoms by the BMI category (bmicat) variable, which indicates whether an individual is overweight or now.

To visualize this we can add a facet_grid(), which takes a formula argument.


We want one row and two columns, one column for each weight category. So we specify bmicat on the right hand side of the forumla passed to facet_grid().

g + 
  geom_point() + 
  geom_smooth(method = "lm") +
  facet_grid(. ~ bmicat) 

Scatterplot of PM2.5 and nocturnal symptoms by BMI category

Now it seems clear that the relationship between PM2.5 and nocturnal symptoms is relatively flat among normal weight individuals, while the relationship is increasing among overweight individuals.

This plot suggests that overweight individuals may be more susceptible to the effects of PM2.5.

Modifying geom properties

You can modify properties of geoms by specifying options to their respective geom_*() functions.

map aesthetics to constants


For example, here we modify the points in the scatterplot to make the color “steelblue”, the size larger, and the alpha transparency greater.

g + geom_point(color = "steelblue", size = 4, alpha = 1/2)

Modifying point color with a constant

map aesthetics to variables

In addition to setting specific geom attributes to constant values, we can map aesthetics to variables in our dataset.

For example, we can map the aesthetic color to the variable bmicat, so the points will be colored according to the levels of bmicat.

We use the aes() function to indicate this difference from the plot above.

g + geom_point(aes(color = bmicat), size = 4, alpha = 1/2)

Mapping color to a variable

Customizing the smooth

We can also customize aspects of the geoms.

For example, we can customize the smoother that we overlay on the points with geom_smooth().

Here we change the line type and increase the size from the default. We also remove the shaded standard error from the line.

g + 
  geom_point(aes(color = bmicat), 
             size = 2, 
             alpha = 1/2) + 
  geom_smooth(size = 4, 
              linetype = 3, 
              method = "lm", 
              se = FALSE)

Customizing a smoother

Other important stuff

Changing the theme

The default theme for ggplot2 uses the gray background with white grid lines.

If you don’t find this suitable, you can use the black and white theme by using the theme_bw() function.

The theme_bw() function also allows you to set the typeface for the plot, in case you don’t want the default Helvetica. Here we change the typeface to Times.


For things that only make sense globally, use theme(), i.e. theme(legend.position = "none"). Two standard appearance themes are included

  • theme_gray(): The default theme (gray background)
  • theme_bw(): More stark/plain
g + 
  geom_point(aes(color = bmicat)) + 
  theme_bw(base_family = "Times")

Modifying the theme for a plot


Let’s take our palmerpenguins scatterplot from above and change out the theme to use theme_dark().

# try it yourself

# A tibble: 344 × 8
   species island    bill_length_mm bill_depth_mm flipper_…¹ body_…² sex    year
   <fct>   <fct>              <dbl>         <dbl>      <int>   <int> <fct> <int>
 1 Adelie  Torgersen           39.1          18.7        181    3750 male   2007
 2 Adelie  Torgersen           39.5          17.4        186    3800 fema…  2007
 3 Adelie  Torgersen           40.3          18          195    3250 fema…  2007
 4 Adelie  Torgersen           NA            NA           NA      NA <NA>   2007
 5 Adelie  Torgersen           36.7          19.3        193    3450 fema…  2007
 6 Adelie  Torgersen           39.3          20.6        190    3650 male   2007
 7 Adelie  Torgersen           38.9          17.8        181    3625 fema…  2007
 8 Adelie  Torgersen           39.2          19.6        195    4675 male   2007
 9 Adelie  Torgersen           34.1          18.1        193    3475 <NA>   2007
10 Adelie  Torgersen           42            20.2        190    4250 <NA>   2007
# … with 334 more rows, and abbreviated variable names ¹​flipper_length_mm,
#   ²​body_mass_g

Modifying labels


There are a variety of annotations you can add to a plot, including different kinds of labels.

  • xlab() for x-axis labels
  • ylab() for y-axis labels
  • ggtitle() for specifying plot titles

labs() function is generic and can be used to modify multiple types of labels at once

Here is an example of modifying the title and the x and y labels to make the plot a bit more informative.

g + 
  geom_point(aes(color = bmicat)) + 
  labs(title = "MAACS Cohort") + 
  labs(x = expression("log " * PM[2.5]), 
       y = "Nocturnal Symptoms")

Modifying plot labels

A quick aside about axis limits

One quick quirk about ggplot2 that caught me up when I first started using the package can be displayed in the following example.

If you make a lot of time series plots, you often want to restrict the range of the y-axis while still plotting all the data.

In the base graphics system you can do that as follows.

testdat <- data.frame(x = 1:100, 
                      y = rnorm(100))
testdat[50,2] <- 100  ## Outlier!
     type = "l", 
     ylim = c(-3,3))

Time series plot with base graphics

Here, we have restricted the y-axis range to be between -3 and 3, even though there is a clear outlier in the data.


With ggplot2 the default settings will give you this.

g <- ggplot(testdat, aes(x = x, y = y))
g + geom_line()

Time series plot with default settings

One might think that modifying the ylim() attribute would give you the same thing as the base plot, but it doesn’t (?????)

g + 
  geom_line() + 
  ylim(-3, 3)

Time series plot with modified ylim

Effectively, what this does is subset the data so that only observations between -3 and 3 are included, then plot the data.

To plot the data without subsetting it first and still get the restricted range, you have to do the following.

g + 
  geom_line() + 
  coord_cartesian(ylim = c(-3, 3))

Time series plot with restricted y-axis range

And now you know!

Post-lecture materials


  • The ggplot2 book by Hadley Wickham
  • The R Graphics Cookbook by Winston Chang (examples in base plots and in ggplot2)
  • tidyverse web site

More complex example with ggplot2

Now you get the sense that plots in the ggplot2 system are constructed by successively adding components to the plot, starting with the base dataset and maybe a scatterplot. In this section bleow, you can see a slightly more complicated example with an additional variable.

Click here for a slightly more complicated example with ggplot().

Now, we will ask the question

How does the relationship between PM2.5 and nocturnal symptoms vary by BMI category and nitrogen dioxide (NO2)?

Unlike our previous BMI variable, NO2 is continuous, and so we need to make NO2 categorical so we can condition on it in the plotting. We can use the cut() function for this purpose. We will divide the NO2 variable into tertiles.

First we need to calculate the tertiles with the quantile() function.

cutpoints <- quantile(maacs$logno2_new, seq(0, 1, length = 4), na.rm = TRUE)

Then we need to divide the original logno2_new variable into the ranges defined by the cut points computed above.

maacs$no2tert <- cut(maacs$logno2_new, cutpoints)

The not2tert variable is now a categorical factor variable containing 3 levels, indicating the ranges of NO2 (on the log scale).

## See the levels of the newly created factor variable
[1] "(0.342,1.23]" "(1.23,1.47]"  "(1.47,2.17]" 

The final plot shows the relationship between PM2.5 and nocturnal symptoms by BMI category and NO2 tertile.

## Setup ggplot with data frame
g <- maacs %>%
        ggplot(aes(logpm25, NocturnalSympt))

## Add layers
g + geom_point(alpha = 1/3) + 
        facet_grid(bmicat ~ no2tert) + 
        geom_smooth(method="lm", se=FALSE, col="steelblue") + 
        theme_bw(base_family = "Avenir", base_size = 10) + 
        labs(x = expression("log " * PM[2.5])) + 
        labs(y = "Nocturnal Symptoms") + 
        labs(title = "MAACS Cohort")
`geom_smooth()` using formula 'y ~ x'

PM2.5 and nocturnal symptoms by BMI category and NO2 tertile

Final Questions

Here are some post-lecture questions to help you think about the material discussed.

  1. What happens if you facet on a continuous variable?

  2. Read ?facet_wrap. What does nrow do? What does ncol do? What other options control the layout of the individual panels? Why doesn’t facet_grid() have nrow and ncol arguments?

  3. What geom would you use to draw a line chart? A boxplot? A histogram? An area chart?

  4. What does geom_col() do? How is it different to geom_bar()?

Additional Resources