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Data visualization with ggplot2

Acknowledgements

This material is based on DataCarpentry ggplot2 material with many modifications and shortenings.

Review

Remember an earlier lesson where we introduced basic plotting commands using built-in data and base plotting tools. For example, we made a few plots using the famous iris dataset, which measured petal and sepal length and width for several different species of flower. To learn more about this dataset you can also run ?iris.

 # Load some data and look at the first few lines
data(iris)
head(iris)
##   Sepal.Length Sepal.Width Petal.Length Petal.Width Species
## 1          5.1         3.5          1.4         0.2  setosa
## 2          4.9         3.0          1.4         0.2  setosa
## 3          4.7         3.2          1.3         0.2  setosa
## 4          4.6         3.1          1.5         0.2  setosa
## 5          5.0         3.6          1.4         0.2  setosa
## 6          5.4         3.9          1.7         0.4  setosa

# Make a basic scatter plot
with(iris, plot(Sepal.Length, Sepal.Width))

plot of chunk irisbase

When we call plot in this way, we are using built-in, or base graphics. R's base graphics are powerful and nearly infinitely customizable.

ggplot2

We're going to be using a data visualization package called ggplot2 for drawing the plots.

Recall how to install and load packages. Install the package if you haven't already:

# Only need to do this once
install.packages("ggplot2")

Then load it:

library(ggplot2)

Plotting with ggplot2

ggplot2 is a widely used R package that extends R's visualization capabilities. It takes the hassle out of things like creating legends, mapping other variables to scales like color, or faceting plots into small multiples. We'll learn about what all these things mean shortly. To start with, let's produce a scatterplot of the columns Sepal.Length and Sepal.Width.

ggplot(iris, aes(Sepal.Length, Sepal.Width)) + geom_point()
 # or long format: ggplot(data = iris, aes(x = Sepal.Length, y = Sepal.Width)) + geom_point()

plot of chunk ggdiamonds

The ggplot function has two required arguments: the data used for creating the plot, and an aesthetic mapping to describe how variables in said data are mapped to things we can see on the plot. Let's use ggplot to recreate some of the same plots we produced above. First, the simple scatterplot:

We see the strong relationship between petal length and sepal length of a flower.

Faceting and scaling

One option we could use is to color-code the points by their species of origin. Here, we pass another col= argument with the variable we'd like to use for color-coding:

ggplot(iris, aes(Sepal.Length, Sepal.Width, col = Species)) + geom_point()

plot of chunk clarcolor

We can see that ggplot2 color-codes the points using a (most of the time) sensible default color scheme, and automatically draws a legend on the side for us. This requires a good deal of extra error-prone coding using base graphics.

Differentiating points by shape is also easy:

ggplot(iris, aes(Sepal.Length, Sepal.Width, col = Species, shape = Species)) + geom_point()

plot of chunk clarcolorshape

Graphs are often easier to interpret after facetting. This can be accomplished easily using ggplot2:

ggplot(iris, aes(Sepal.Length, Sepal.Width, col = Species)) + geom_point() + facet_grid(Species ~ .)

plot of chunk facetclar

Here, we add another layer with facet_grid with a formula object that's constructed with the ~ operator. We've plotted the sepal length vs. petal length separately for each species.

Let's assume the flowers have been collected at 5 different sites A-E coded in the variable field.

iris$field <- rep(LETTERS[1:5], times = 30)

Now we can examine the sepal-petal length relationship for various fields. Here facet the plot across two different categorical variables using the same syntax:

ggplot(iris, aes(Sepal.Length, Sepal.Width, col = Species)) + geom_point() + facet_grid(field ~ Species)

plot of chunk facetcol

What we've done here in addition to faceting is map a feature of the data (here, the cut quality) onto a scale (here, color). This behavior will work differently depending on whether you're looking at categorical or continuous variables. We can also map features to other scales such as size=, shape=, linetype=, or even transparency using alpha=. All of these different scales can be combined with each other or with facets, and give you an extremely powerful and easy-to-use graphical toolbox for exploratory data analysis.

ggplot2 and the Grammar of Graphics

The ggplot2 package provides an R implementation of Leland Wilkinson's Grammar of Graphics (1999). The Grammar of Graphics challenges data analysts to think beyond the garden variety plot types (e.g. scatter-plot, barplot) and the consider the components that make up a plot or graphic, such as how data are represented on the plot (as lines, points, etc.), how variables are mapped to coordinates or plotting shape or colour, what transformation or statistical summary is required, and so on. Specifically, ggplot2 allows users to build a plot layer-by-layer by specifying:

  • a geom*, which specifies how the data are represented on the plot (points, lines, bars, etc.),
  • aesthetics that map variables in the data to axes on the plot or to plotting size, shape, color, etc.,
  • a stat, a statistical transformation or summary of the data applied prior to plotting,
  • facets, which we've already seen above, that allow the data to be divided into chunks on the basis of other categorical or continuous variables and the same plot drawn for each chunk.

Because ggplot2 implements a layered grammar of graphics, data points and additional information (scatterplot smoothers, confidence bands, etc.) can be added to the plot via additional layers, each of which utilize further geoms, aesthetics, and stats.

To make the best use of ggplot2 it helps to understand the grammar and how it affects how plots are produced. In addition, it is important to note that ggplot2 is not a general-purpose plotting tool-kit; you may not be able to achieve certain plots or additions to a figure of they do not map onto concepts included in the layered grammar.

ggplot(iris, aes(Sepal.Length, Sepal.Width)) + geom_point()

plot of chunk ggdiamonds

Here, we've built our plot in layers. First, we create a canvas for plotting layers to come using the ggplot function, specifying which data to use (here, the iris data frame), and an aesthetic mapping of Sepal.Length to the x-axis and Sepal.Width to the y-axis. We next add a layer to the plot, specifying a geom, or a way of visually representing the aesthetic mapping. Here we're using a point. Instead of using a point, we could use a different geom. Here, let's use hexagonal binning instead of a point.

ggplot(iris, aes(Sepal.Length, Sepal.Width)) + geom_hex()

plot of chunk gghexbin

Here, each bin represents a segment of the plotting surface with lighter blue colors representing more density in that segment. The number of bins can be adjusted as an argument to the geom_hex() function. This is one method of solving the overplotting problem we have in this plot without doing any faceting. Another method, here using points again, is to lower the opacity of each point. Here, alpha=1/5 sets the opacity of each point to 20%. In other words, 5 points would have to overlap to result in a completely solid point. Note that in this case we're not mapping the alpha level aesthetic to some other variable as we did above with color -- we're setting it to a static value of 0.2 for all points in the layer.

ggplot(iris, aes(Sepal.Length, Sepal.Width)) + geom_point(alpha=1/5)

plot of chunk gghexbin2

We can easily add more layers to the plot. For instance, we could add another layer displaying a smoothed conditional mean using the geom_smooth() function.

ggplot(iris, aes(Sepal.Length, Sepal.Width)) + geom_point() + geom_smooth()

plot of chunk smoothgam

We'll get a message telling us that because we have >1,000 observations we will default to using a generalized additive model. We could easily plot a straight line by specifying that we want a linear model (method="lm") instead of a generalized additive model, the default for large datasets.

ggplot(iris, aes(Sepal.Length, Sepal.Width)) + geom_point() + geom_smooth(method="lm")

plot of chunk smoothlinear

Faceting and scaling

One option we could use is to color-code the points by their species of origin. Here, we can map the color of the points (an aesthetic) onto one of the variables in our dataset.

ggplot(iris, aes(Sepal.Length, Sepal.Width, col=Species)) + geom_point()

plot of chunk ggclarcol

Plots can be customized, e.g. plotting bigger points or manually set colors

ggplot(iris, aes(Sepal.Length, Sepal.Width, col=Species)) + geom_point(size = 3)

plot of chunk ggclarcolbig

ggplot(iris, aes(Sepal.Length, Sepal.Width, col=Species)) + geom_point(size = 3) +
scale_color_manual(values = c("red", "green", "blue"))

plot of chunk ggclarcolbig

ggplot(iris, aes(Sepal.Length, Sepal.Width, col=Species)) + geom_point() + facet_grid(field ~ Species)

plot of chunk ggfacet

By combining multiple layers with aesthetic mappings to different scales, ggplot2 provides a foundation for producing a wide range of statistical graphics beyond simple "named" plots like scatter plots, histograms, bar plots, etc.

What about box plots of the sepal length grouped separately by species, color-coded by the field? Simple:

ggplot(iris, aes(Species, Sepal.Length)) + geom_boxplot(aes(fill=field))

plot of chunk ggboxplots

Or what about a kernel density plot (think about a smooth histogram) of the iris's sepal length in different semitransparent curves with the color fill mapped to each Species, with a title and a proper axis labels? This also shows the syntax of building up a plot one step at a time. We first initialize the plot with ggplot, giving it the data we're working with, and aesthetic mappings. We then add a geom_density layer, limit the x-axis displayed, and finally give it a title and axis labels. The plot is in the g object here; we can simply enter g and the plot will be displayed.

g <- ggplot(iris, aes(Sepal.Length, fill=Species)) 
g <- g + geom_density(alpha=1/4) 
g <- g + ggtitle("Sepal Length by Species")
g <- g + xlab("Sepal Length") + ylab("Density")
g

plot of chunk ggdensity

Finally, we can save the plot created using the ggsave function:

ggsave(filename="~/Desktop/sepal-length-density.pdf", plot=g)

There are endless ways to combine aesthetic mappings with different geoms and multiple layers. Read about other geoms, mappings, scales, and other layer options at the links below.

Further ggplot2 resources