ggplot2 (ggplot) Introduction

August 26, 2016

(This article was first published on The Practical R, and kindly contributed to R-bloggers)

In this post I’ll briefly introduce how to use ggplot2 (ggplot), which by default makes nicer looking plots than the standard R plotting functions.

The first thing to know is that ggplot requires data frames work properly. It is an entirely different framework from the standard plotting functions in R. Let’s grab a default data frame in R called mtcars. Let’s confirm it’s a data frame using some code:

# Get the mtcars data types

R confirms that this is in fact a data frame.

# the output
[1] "data.frame"

Feel free to take a look at the data itself by just typing the name into R. For bevity, I won’t show the data in this post.

# Look at mtcars

Next let’s define some standard plot function names in ggplot.
geom_point = scatterplot (points or solid lines)
geom_boxplot = boxplot
geom_bar = column plot
There’s many more (really cool) plot types, but I’ll stop here for now.

Let’s make our scatterplot. Here’s the code to make a standard plot. Don’t forget to load the package ggplot2 before running this code using the library function (install ggplot2 first if you haven’t done so before).

# Plot the data
ggplot(mtcars, aes(hp, mpg)) + geom_point()


Success! The code above seems strange at first, but let’s dive into how it works. First we call ggplot and provide the data frame name ‘mtcars’. Then we give the x & y variables using the aes command. Finally we specify we’re making a scatterplot by attaching + geom_point().

Now let’s make this look better! This is where the power of ggplot shines. It’s really easy to make a nice looking plot.

# Plot the data
p <- ggplot(mtcars, aes(hp, mpg))
p + geom_point() + labs (x = "Horsepower (hp)", y = "Miles per Gallon (mpg)") +
  ggtitle("My mtcars Plot")


We can see that the syntax is a bit different this time. We save the first ggplot call to a variable p (p for plot), but any variable will work. Then we attached more plotting features using p + ——. For this plot we added custom x and y axis labels and a title.

Next let’s make a change to the overall look of the plot, using what ggplot calls a theme. We’ll add theme_bw.

# Plot the data
p <- ggplot(mtcars, aes(hp, mpg))
p + geom_point() + labs (x = "Horsepower (hp)", y = "Miles per Gallon (mpg)") +
  ggtitle("My mtcars Plot") + theme_bw()


Finally, let’s spruce it up my coloring the points blue and making them bigger, while also making our axes and titles bigger. The code below makes this final plot.

# Make the final plot
p <- ggplot(mtcars, aes(hp, mpg))
p + geom_point(size = 3, color = "blue") + 
  labs (x = "Horsepower (hp)", y = "Miles per Gallon (mpg)") +
  ggtitle("My mtcars Plot") + theme_bw()+
  theme(axis.text = element_text(size = 12), 
        axis.title = element_text(size = 14),
        plot.title = element_text(size = 18, face = "bold"))

ggplot2 final

Hope this helped explain the basics of ggplot. Here’s the link to the ggplot2 documentation (click me).

To leave a comment for the author, please follow the link and comment on their blog: The Practical R. offers daily e-mail updates about R news and tutorials on topics such as: Data science, Big Data, R jobs, visualization (ggplot2, Boxplots, maps, animation), programming (RStudio, Sweave, LaTeX, SQL, Eclipse, git, hadoop, Web Scraping) statistics (regression, PCA, time series, trading) and more...

If you got this far, why not subscribe for updates from the site? Choose your flavor: e-mail, twitter, RSS, or facebook...

Comments are closed.

Search R-bloggers


Never miss an update!
Subscribe to R-bloggers to receive
e-mails with the latest R posts.
(You will not see this message again.)

Click here to close (This popup will not appear again)