Quick introduction to ggplot2 (no knowledge of R assumed)
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This is a bare-bones introduction to ggplot2, a visualization package in R. It assumes no knowledge of R.

There is also a literate programming version of this tutorial in ggplot2-tutorial.R.


Let's start with a preview of what ggplot2 can do.

Given Fisher's iris data set and one simple command...

qplot(Sepal.Length, Petal.Length, data = iris, color = Species)

...we can produce this plot of sepal length vs. petal length, colored by species.

Sepal vs. Petal, Colored by Species


You can download R here. After installation, you can launch R in interactive mode by either typing R on the command line or opening the standard GUI (which should have been included in the download).

R Basics


Vectors are a core data structure in R, and are created with c(). Elements in a vector must be of the same type.

numbers = c(23, 13, 5, 7, 31)
names = c("edwin", "alice", "bob")

Elements are indexed starting at 1, and are accessed with [] notation.

numbers[1] # 23
names[1] # edwin

Data frames

Data frames are like matrices, but with named columns of different types (similar to database tables).

books = data.frame(
    title = c("harry potter", "war and peace", "lord of the rings"), # column named "title"
    author = c("rowling", "tolstoy", "tolkien"),
    num_pages = c("350", "875", "500")

You can access columns of a data frame with $.

books$title # c("harry potter", "war and peace", "lord of the rings")
books$author[1] # "rowling"

You can also create new columns with $.

books$num_bought_today = c(10, 5, 8)
books$num_bought_yesterday = c(18, 13, 20)

books$total_num_bought = books$num_bought_today + books$num_bought_yesterday


Suppose you want to import a TSV file into R as a data frame.

tsv file without header

For example, consider the data/students.tsv file (with columns describing each student's age, test score, and name).

13   100 alice
14   95  bob
13   82  eve

We can import this file into R using read.table().

students = read.table("data/students.tsv", 
    header = F, # file does not contain a header (`F` is short for `FALSE`),
                # so we must manually specify column names                    
    sep = "\t", # file is tab-delimited        
    col.names = c("age", "score", "name") # column names

We can now access the different columns in the data frame with students$age, students$score, and students$name.

csv file with header

For an example of a file in a different format, look at the data/studentsWithHeader.tsv file.


Here we have the same data, but now the file is comma-delimited and contains a header. We can import this file with

students = read.table("data/students.tsv", 
    sep = ",",
    header = T  # first line contains column names, so we can
)               # immediately call `students$age`                    

(Note: there is also a read.csv function that uses sep = "," by default.)


There are many more options that read.table can take. For a list of these, just type help(read.table) (or ?read.table) at the prompt to access documentation.

# These work for other functions as well.


With these R basics in place, let's dive into the ggplot2 package.


One of R's greatest strengths is its excellent set of packages. To install a package, you can use the install.packages() function.


To load a package into your current R session, use library().


Scatterplots with qplot()

Let's look at how to create a scatterplot in ggplot2. We'll use the iris data frame that's automatically loaded into R.

What does the data frame contain? We can use the head function to look at the first few rows.

head(iris) # by default, head displays the first 6 rows. see `?head`
head(iris, n = 10) # we can also explicitly set the number of rows to display

  Sepal.Length Sepal.Width Petal.Length Petal.Width Species
           5.1         3.5          1.4         0.2  setosa
           4.9         3.0          1.4         0.2  setosa
           4.7         3.2          1.3         0.2  setosa
           4.6         3.1          1.5         0.2  setosa
           5.0         3.6          1.4         0.2  setosa
           5.4         3.9          1.7         0.4  setosa

(The data frame actually contains three types of species: setosa, versicolor, and virginica.)

Let's plot Sepal.Length against Petal.Length using ggplot2's qplot() function.

qplot(Sepal.Length, Petal.Length, data = iris)
# Plot Sepal.Length vs. Petal.Length, using data from the `iris` data frame.
# * First argument `Sepal.Length` goes on the x-axis.
# * Second argument `Petal.Length` goes on the y-axis.
# * `data = iris` means to look for this data in the `iris` data frame.    

Sepal Length vs. Petal Length

To see where each species is located in this graph, we can color each point by adding a color = Species argument.

qplot(Sepal.Length, Petal.Length, data = iris, color = Species) # dude!

Sepal vs. Petal, Colored by Species

Similarly, we can let the size of each point denote petal width, by adding a size = Petal.Width argument.

qplot(Sepal.Length, Petal.Length, data = iris, color = Species, size = Petal.Width)
# We see that Iris setosa flowers have the narrowest petals.

Sepal vs. Petal, Sized by Petal Width

qplot(Sepal.Length, Petal.Length, data = iris, color = Species, size = Petal.Width, alpha = I(0.7))
# By setting the alpha of each point to 0.7, we reduce the effects of overplotting.

Sepal vs. Petal, with Transparency

Finally, let's fix the axis labels and add a title to the plot.

qplot(Sepal.Length, Petal.Length, data = iris, color = Species,
    xlab = "Sepal Length", ylab = "Petal Length", 
    main = "Sepal vs. Petal Length in Fisher's Iris data")

Sepal vs. Petal, Titled

Other common geoms

In the scatterplot examples above, we implicitly used a point geom, the default when you supply two arguments to qplot().

# These two invocations are equivalent.
qplot(Sepal.Length, Petal.Length, data = iris, geom = "point")
qplot(Sepal.Length, Petal.Length, data = iris)

But we can also easily use other types of geoms to create more kinds of plots.

Barcharts: geom = "bar"

movies = data.frame(
    director = c("spielberg", "spielberg", "spielberg", "jackson", "jackson"),
    movie = c("jaws", "avatar", "schindler's list", "lotr", "king kong"),
    minutes = c(124, 163, 195, 600, 187)

# Plot the number of movies each director has.
qplot(director, data = movies, geom = "bar", ylab = "# movies")
# By default, the height of each bar is simply a count.

# Movies

# But we can also supply a different weight.
# Here the height of each bar is the total running time of the director's movies.
qplot(director, weight = minutes, data = movies, geom = "bar", ylab = "total length (min.)")

Total Running Time

Line charts: geom = "line"

qplot(Sepal.Length, Petal.Length, data = iris, geom = "line", color = Species) 
# Using a line geom doesn't really make sense here, but hey.

Sepal vs. Petal, Lined

# `Orange` is another built-in data frame that describes the growth of orange trees.
qplot(age, circumference, data = Orange, geom = "line",
    colour = Tree,
    main = "How does orange tree circumference vary with age?")

Orange Tree Growth

# We can also plot both points and lines.
qplot(age, circumference, data = Orange, geom = c("point", "line"), colour = Tree)

Orange Tree with Points

And that's it with what I'll cover.

Next Steps

I skipped over a lot of aspects of R and ggplot2 in this intro.

For example,

  • There are many geoms (and other functionalities) in ggplot2 that I didn't cover, e.g., boxplots and histograms.
  • I didn't talk about ggplot2's layering system, or the grammar of graphics it's based on.

So I'll end with some additional resources on R and ggplot2.

Edwin Chen :: @edchedch :: http://blog.echen.me