Visualizations the ggplot Way

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Objectives for this lesson

• Meet the “grammar of graphics” for ggplot2
• Trust that this is better than base R’s plot
• Learn to layer visual elements on top of tidy data
• Glimpse the vast collection of ggplot2 options

Specific achievements

• Create several “aesthetics” or mappings for different plots
• Build boxplots, scatterplots, smoothed lines and histograms
• Style plots with colors
• Repeat plots for different subsets of data

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Getting started

Let’s start by loading a few packages along with a sample dataset, which is the animals table from the Portal Project Teaching Database.

library(dplyr)
animals <- read.csv('data/animals.csv', na.strings = '') %>%
select(year, species_id, sex, weight) %>%
na.omit()


Omitting rows that have missing values for the species_id, sex, and weight columns is not strictly necessary, but it will prevent ggplot from returning missing values warnings.

Layered graphics

As a first example, this code plots each invidual’s weight by species:

library(ggplot2)
ggplot(animals,
aes(x = species_id, y = weight)) +
geom_point()


The ggplot command expects a data frame and an aesthetic mapping. The aes function creates the aesthetic, a mapping between variables in the data frame and visual elements in the plot. Here, the aesthetic maps species_id to the x-axis and weight to the y-axis.

The ggplot function by itself does not display anything until we add a geom_* layer, in this example a geom_point. Layers are literally added, with +, to the object created by the ggplot function.

Individual points are hard to distinguish in this plot. Might a boxplot be a better visualization? The only change needed is in the geom_* layer.

ggplot(animals,
aes(x = species_id, y = weight)) +
geom_boxplot()


Add geom_* layers together to create a multi-layered plot:

ggplot(animals,
aes(x = species_id, y = weight)) +
geom_boxplot() +
geom_point()


Each geom_* object accepts some general and some specialized arguments. For styling with shapes and colors, or for performing some dplyr-like data transformations.

ggplot(animals,
aes(x = species_id, y = weight)) +
geom_boxplot() +
geom_point(
color = 'red',
stat = 'summary',
fun.y = 'mean')


The geom_point layer definition illustrates these features:

• With stat = 'summary', the plot replaces the raw data with the result of a summary function, defined by fun.y.
• Setting color = red applies one color to the whole layer.

Associating color (or any attribute, like the shape of points) to a variable is another kind of aesthetic mapping. Passing the color argument to the aes function works quite differently than assiging color to a geom_*.

ggplot(animals,
aes(x = species_id, y = weight,
color = species_id)) +
geom_boxplot() +
geom_point(stat = 'summary',
fun.y = 'mean')


Exercise 1

Use dplyr to filter down to the animals with species_id equal to DM. Use ggplot to show how the mean weight of this species changes each year, showing males and females in different colors. (Hint: Baby steps! Start with a scatterplot of weight by year. Then expand your code to plot only the means. Then try to distinguish sexes.)

View solution

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Smooth lines

The geom_smooth layer adds a regression line with confidence intervals (95% CI by default). The method = 'lm' parameter specifies that a linear model is used for smoothing.

Prepare some data in dplyr as for a linear model with a categorical predictor.

levels(animals\$sex) <- c('Female', 'Male')
animals_dm <- filter(animals, species_id == 'DM')

ggplot(animals_dm,
aes(x = year, y = weight, shape = sex)) +
geom_point(size = 3, stat = 'summary', fun.y = 'mean') +
geom_smooth(method = 'lm')


Even better would be to distinguish everything (points and lines) by color:

ggplot(animals_dm,
aes(x = year, y = weight,
shape = sex, color = sex)) +
geom_point(size = 3, stat = 'summary', fun.y = 'mean') +
geom_smooth(method = 'lm')


Notice that by adding aesthetic mappings in the base aesthetic (in the ggplot command), it is applied to any layer that recognizes the parameter.

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Storing and re-plotting

The output of ggplot can be assigned to a variable (here, it’s year_wgt). It is then possible to add new elements to it with the + operator. We will use this method to try different color scales for the previous plot.

year_wgt <- ggplot(animals_dm,
aes(x = year, y = weight,
color = sex, shape = sex)) +
geom_point(size = 3,
stat = "summary",
fun.y = "mean") +
geom_smooth(method = "lm")


The plot information stored in year_wgt can be used on its own, or with additional layers.

year_wgt


By overwriting the year_wgt variable, the stored plot gets updated with the black and red color scale.

year_wgt <- year_wgt +
scale_color_manual(values = c("black", "red"))

year_wgt


Exercise 2

Create a histogram, using a geom_histogram layer, of the weights of individuals of species DM and divide the data by sex. Note that instead of using color in the aesthetic, you’ll use fill to distinguish the sexes. To silence that warning, open the help with ?geom_histogram and determine how to explicitly set the bin width.

View solution

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Axes, labels and themes

Let’s start from the histogram like the one generated in the exercise.

histo <- ggplot(animals_dm,
aes(x = weight, fill = sex)) +
geom_histogram(binwidth = 3, color = 'white')

histo


We change the title and axis labels with the labs function. We have various functions related to the scale of each axis, i.e. the range, breaks and any transformations of the values on the axis.

histo <- histo + labs(
title = 'Dipodomys merriami weight distribution',
x = 'Weight (g)',
y = 'Count')

histo


For information on how to add special symbols and formatting to plot labels, see ?plotmath.

Here, we use scale_x_continuous to modify the continuous (as opposed to discrete) x-axis.

histo <- histo + scale_x_continuous(
limits = c(20, 60),
breaks = c(20, 30, 40, 50, 60))

histo


Many plot-level options in ggplot, from background color to font sizes, are defined as part of themes. The next modification to histo changes the base theme of the plot to theme_bw (replacing the default theme_grey) and set a few options manually with the theme function. Try ?theme for a list of available theme options.

histo <- histo + theme_bw() + theme(
legend.position = c(0.2, 0.5),
plot.title = element_text(face = 'bold', vjust = 2),
axis.title.y = element_text(size = 13, vjust = 1),
axis.title.x = element_text(size = 13, vjust = 0))

histo


Note that position is relative to plot size (i.e. between 0 and 1).

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Facets

To conclude this overview of ggplot2, we’ll apply the same plotting instructions to different subsets of the data in panels called “facets”. The facet_wrap function takes a formula argument that specifies the grouping on either side of a ‘~’ using a factor in the data.

animals_common <- filter(animals,
species_id %in% c('DM', 'PP', 'DO'))
faceted <- ggplot(animals_common, aes(x = weight)) +
geom_histogram() +
facet_wrap( ~ species_id) +
labs(title = "Weight of most common species",
x = "Count",
y = "Weight (g)")

faceted


The un-grouped data may be added as a layer on each panel, but you have to drop the grouping variable (i.e. month).

faceted_all <- faceted + geom_histogram(
data = select(animals_common, -species_id),
alpha = 0.2)

faceted_all


Finally, let’s show off some additional styling with fill and the very unusual ..density.. aesthetic. The .. notation is shared by several ggplot functions that perform a calculation. Using ..density.. as the y-axis variable allows a geometry to display the probability density of variable assigned to the x-axis.

faceted_density <- ggplot(animals_common,
aes(x = weight, fill = species_id)) +
geom_histogram(aes(y = ..density..)) +
facet_wrap( ~ species_id) +
labs(title = "Weight of most common species",
x = "Count",
y = "Weight (g)")

faceted_density


Exercise 3

The formula notation for facet_grid (different from facet_wrap) interprets left-side variables as one axis and right-side variables as another. For these three common animals, create facets in the weight histogram along two categorical variables, with a row for each sex and a column for each species.

View solution

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Solution 1

animals_dm <- filter(animals, species_id == 'DM')
ggplot(animals_dm,
aes(x = year, y = weight, color = sex)) +
geom_line(stat = 'summary',
fun.y = 'mean')


Return

Solution 2

filter(animals, species_id == 'DM') %>%
ggplot(aes(x = weight, fill = sex)) +
geom_histogram(binwidth = 1)


Return

Solution 3

ggplot(animals_common,
aes(x = weight)) +
geom_histogram() +
facet_grid(sex ~ species_id) +
labs(title = 'Weight of common species by sex',
x = 'Count',
y = 'Weight (g)')


Return

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