- Produce scatter plots, boxplots, and time series plots using ggplot.
- Set universal plot settings.
- Describe what faceting is and apply faceting in ggplot.
- Modify the aesthetics of an existing ggplot plot (including axis labels and color).
- Build complex and customized plots from data in a data frame.
We start by loading the required packages.
ggplot2 is included in the
If not still in the workspace, load the data we saved in the previous lesson.
ggplot2 is a plotting package that provides helpful commands to create complex plots from data in a data frame. It provides a more programmatic interface for specifying what variables to plot, how they are displayed, and general visual properties. Therefore, we only need minimal changes if the underlying data change or if we decide to change from a bar plot to a scatterplot. This helps in creating publication quality plots with minimal amounts of adjustments and tweaking.
ggplot2 refers to the name of the package itself. When using the package we use the function
ggplot() to generate the plots, and so references to using the function will be referred to as
ggplot() and the package as a whole as
ggplot2 plots work best with data in the ‘long’ format, i.e., a column for every variable, and a row for every observation. Well-structured data will save you lots of time when making figures with
ggplot graphics are built layer by layer by adding new elements. Adding layers in this fashion allows for extensive flexibility and customization of plots.
To build a ggplot, we will use the following basic template that can be used for different types of plots:
ggplot(data = <DATA>, mapping = aes(<MAPPINGS>)) + <GEOM_FUNCTION>()
ggplot()function and bind the plot to a specific data frame using the
aes) function), by selecting the variables to be plotted and specifying how to present them in the graph, e.g., as x/y positions or characteristics such as size, shape, color, etc.
add ‘geoms’ – graphical representations of the data in the plot (points, lines, bars).
ggplot2 offers many different geoms; we will use some common ones today, including:
geom_point()for scatter plots, dot plots, etc.
geom_boxplot()for, well, boxplots!
geom_line()for trend lines, time series, etc.
To add a geom to the plot use
+ operator. Because we have two continuous variables, let’s use
+ in the
ggplot2 package is particularly useful because it allows you to modify existing
ggplot objects. This means you can easily set up plot “templates” and conveniently explore different types of plots, so the above plot can also be generated with code like this:
ggplot()function can be seen by any geom layers that you add (i.e., these are universal plot settings). This includes the x- and y-axis you set up in
+sign used to add layers must be placed at the end of each line containing a layer. If, instead, the
+sign is added in the line before the other layer,
ggplot2will not add the new layer and will return an error message.
ggplot, which also contained the
ggplot()function is now unsupported and has been removed from CRAN in order to reduce accidental installations and further confusion.
Scatter plots can be useful exploratory tools for small datasets. For data sets with large numbers of observations, such as the
surveys_completedata set, overplotting of points can be a limitation of scatter plots. One strategy for handling such settings is to use hexagonal binning of observations. The plot space is tessellated into hexagons. Each hexagon is assigned a color based on the number of observations that fall within its boundaries. To use hexagonal binning with
ggplot2, first install the R package
Then use the
- What are the relative strengths and weaknesses of a hexagonal bin plot compared to a scatter plot? Examine the above scatter plot and compare it with the hexagonal bin plot that you created.
Building plots with
ggplot2 is typically an iterative process. We start by defining the dataset we’ll use, lay out the axes, and choose a geom:
Then, we start modifying this plot to extract more information from it. For instance, we can add transparency (
alpha) to avoid overplotting:
We can also add colors for all the points:
Or to color each species in the plot differently, you could use a vector as an input to the argument color.
ggplot2 will provide a different color corresponding to different values in the vector. Here is an example where we color with
Use what you just learned to create a scatter plot of
species_idwith the plot types showing in different colors. Is this a good way to show this type of data?
We can use boxplots to visualize the distribution of weight within each species:
By adding points to the boxplot, we can have a better idea of the number of measurements and of their distribution:
Notice how the boxplot layer is behind the jitter layer? What do you need to change in the code to put the boxplot in front of the points such that it’s not hidden?
Boxplots are useful summaries, but hide the shape of the distribution. For example, if there is a bimodal distribution, it would not be observed with a boxplot. An alternative to the boxplot is the violin plot (sometimes known as a beanplot), where the shape (of the density of points) is drawn.
- Replace the box plot with a violin plot; see
In many types of data, it is important to consider the scale of the observations. For example, it may be worth changing the scale of the axis to better distribute the observations in the space of the plot. Changing the scale of the axes is done similarly to adding/modifying other components (i.e., by incrementally adding commands). Try making these modifications:
- Represent weight on the log10 scale; see
So far, we’ve looked at the distribution of weight within species. Try making a new plot to explore the distribution of another variable within each species.
Create boxplot for
hindfoot_length. Overlay the boxplot layer on a jitter layer to show actual measurements.
Add color to the data points on your boxplot according to the plot from which the sample was taken (
Hint: Check the class for
plot_id. Consider changing the class of
plot_idfrom integer to factor. Why does this change how R makes the graph?
Let’s calculate number of counts per year for each genus. First we need to group the data and count records within each group:
Timelapse data can be visualized as a line plot with years on the x-axis and counts on the y-axis:
Unfortunately, this does not work because we plotted data for all the genera together. We need to tell ggplot to draw a line for each genus by modifying the aesthetic function to include
group = genus:
We will be able to distinguish genera in the plot if we add colors (using
color also automatically groups the data):
In the previous lesson, we saw how to use the pipe operator
%>% to use different functions in a sequence and create a coherent workflow. We can also use the pipe operator to pass the
data argument to the
ggplot() function. The hard part is to remember that to build your ggplot, you need to use
+ and not
The pipe operator can also be used to link data manipulation with consequent data visualization.
ggplot has a special technique called faceting that allows the user to split one plot into multiple plots based on a factor included in the dataset. We will use it to make a time series plot for each genus:
Now we would like to split the line in each plot by the sex of each individual measured. To do that we need to make counts in the data frame grouped by
We can now make the faceted plot by splitting further by sex using
color (within a single plot):
We can also facet both by sex and genus:
You can also organise the panels only by rows (or only by columns):
ggplot2 before version 3.0.0 used formulas to specify how plots are faceted. If you encounter
wrap(...) code containing
~, please read https://ggplot2.tidyverse.org/news/#tidy-evaluation.
Usually plots with white background look more readable when printed. Every single component of a
ggplot graph can be customized using the generic
theme() function, as we will see below. However, there are pre-loaded themes available that change the overall appearance of the graph without much effort.
For example, we can change our previous graph to have a simpler white background using the
In addition to
theme_bw(), which changes the plot background to white,
ggplot2 comes with several other themes which can be useful to quickly change the look of your visualization. The complete list of themes is available at https://ggplot2.tidyverse.org/reference/ggtheme.html.
theme_light() are popular, and
theme_void() can be useful as a starting point to create a new hand-crafted theme.
The ggthemes package provides a wide variety of options.
Use what you just learned to create a plot that depicts how the average weight of each species changes through the years.
#> `summarise()` has grouped output by 'year'. You can override using the `.groups` argument.
Take a look at the
ggplot2 cheat sheet, and think of ways you could improve the plot.
Now, let’s change names of axes to something more informative than ‘year’ and ‘n’ and add a title to the figure:
The axes have more informative names, but their readability can be improved by increasing the font size. This can be done with the generic
Note that it is also possible to change the fonts of your plots. If you are on Windows, you may have to install the
extrafont package, and follow the instructions included in the README for this package.
After our manipulations, you may notice that the values on the x-axis are still not properly readable. Let’s change the orientation of the labels and adjust them vertically and horizontally so they don’t overlap. You can use a 90 degree angle, or experiment to find the appropriate angle for diagonally oriented labels. We can also modify the facet label text (
strip.text) to italicize the genus names:
ggplot(data = yearly_sex_counts, mapping = aes(x = year, y = n, color = sex)) + geom_line() + facet_wrap(vars(genus)) + labs(title = "Observed genera through time", x = "Year of observation", y = "Number of individuals") + theme_bw() + theme(axis.text.x = element_text(colour = "grey20", size = 12, angle = 90, hjust = 0.5, vjust = 0.5), axis.text.y = element_text(colour = "grey20", size = 12), strip.text = element_text(face = "italic"), text = element_text(size = 16))
If you like the changes you created better than the default theme, you can save them as an object to be able to easily apply them to other plots you may create:
With all of this information in hand, please take another five minutes to either improve one of the plots generated in this exercise or create a beautiful graph of your own. Use the RStudio
ggplot2cheat sheet for inspiration.
Here are some ideas:
- See if you can change the thickness of the lines.
- Can you find a way to change the name of the legend? What about its labels?
- Try using a different color palette (see https://www.cookbook-r.com/Graphs/Colors_(ggplot2)/).
Faceting is a great tool for splitting one plot into multiple plots, but sometimes you may want to produce a single figure that contains multiple plots using different variables or even different data frames. The
patchwork package allows us to combine separate ggplots into a single figure while keeping everything aligned properly. Like most R packages, we can install
patchwork from CRAN, the R package repository:
After you have loaded the
patchwork package you can use
+ to place plots next to each other,
/ to arrange them vertically, and
plot_layout() to determine how much space each plot uses:
library(patchwork) plot_weight <- ggplot(data = surveys_complete, aes(x = species_id, y = weight)) + geom_boxplot() + labs(x = "Species", y = expression(log(Weight))) + scale_y_log10() plot_count <- ggplot(data = yearly_counts, aes(x = year, y = n, color = genus)) + geom_line() + labs(x = "Year", y = "Abundance") plot_weight / plot_count + plot_layout(heights = c(3, 2))
You can also use parentheses
() to create more complex layouts. There are many useful examples on the patchwork website
After creating your plot, you can save it to a file in your favorite format. The Export tab in the Plot pane in RStudio will save your plots at low resolution, which will not be accepted by many journals and will not scale well for posters. The
ggplot2 extensions website provides a list of packages that extend the capabilities of
ggplot2, including additional themes.
Instead, use the
ggsave() function, which allows you easily change the dimension and resolution of your plot by adjusting the appropriate arguments (
my_plot <- ggplot(data = yearly_sex_counts, aes(x = year, y = n, color = sex)) + geom_line() + facet_wrap(vars(genus)) + labs(title = "Observed genera through time", x = "Year of observation", y = "Number of individuals") + theme_bw() + theme(axis.text.x = element_text(colour = "grey20", size = 12, angle = 90, hjust = 0.5, vjust = 0.5), axis.text.y = element_text(colour = "grey20", size = 12), text = element_text(size = 16)) ggsave("name_of_file.png", my_plot, width = 15, height = 10) ## This also works for plots combined with patchwork plot_combined <- plot_weight / plot_count + plot_layout(heights = c(3, 2)) ggsave("plot_combined.png", plot_combined, width = 10, dpi = 300)
Note: The parameters
height also determine the font size in the saved plot.
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