Data visualization with ggplot2

Overview

Teaching: min
Exercises: min

Questions

• What is ggplot2? How can I use it to make publication quality graphics?

Objectives

• 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 tidyverse package.

library(tidyverse)

If not still in the workspace, load the data we saved in the previous lesson.

iris <- read_csv(file = "data/iris.csv")
phys_dates<-read_csv("data_output/phys_dates.csv")

Plotting with ggplot2

ggplot2 is a plotting package that makes it simple 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 scatter plot. This helps in creating publication quality plots with minimal amounts of adjustments and tweaking.

ggplot2 functions like data in the ‘long’ format, i.e., a column for every dimension, and a row for every observation. Well-structured data will save you lots of time when making figures with ggplot2

ggplot graphics are built step by step 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>()

• use the ggplot() function and bind the plot to a specific data frame using the data argument

ggplot(data = iris)

• define a mapping (using the aesthetic (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.

ggplot(data = iris, 
       mapping = aes(x = Sepal.Length, 
                     y = Sepal.Width))

• 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 the + operator. Because we have two continuous variables, let’s use geom_point() first:

ggplot(data = iris, 
       mapping = aes(x = Sepal.Length, 
                     y = Sepal.Width)) +
  geom_point()

The + 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:

# Assign plot to a variable
iris_plot <- ggplot(data = iris, 
       mapping = aes(x = Sepal.Length, 
                     y = Sepal.Width))

# Draw the plot
iris_plot + 
    geom_point()

Notes

• Anything you put in the 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 mapping you set up in aes().
• You can also specify mappings for a given geom independently of the mappings defined globally in the ggplot() function.
• The + sign used to add new layers must be placed at the end of the line containing the previous layer. If, instead, the + sign is added at the beginning of the line containing the new layer, ggplot2 will not add the new layer and will return an error message.

# This is the correct syntax for adding layers
iris_plot +
  geom_point()

# This will not add the new layer and will return an error message
iris_plot
  + geom_point()

Building your plots iteratively

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:

ggplot(data = iris, 
       mapping = aes(x = Sepal.Width, 
                     y = Sepal.Length)) +
    geom_point()

We can also add colors for all the points:

ggplot(data = iris, 
       mapping = aes(x = Sepal.Width, 
                     y = Sepal.Length)) +
    geom_point(color = "blue")

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. To reference the values in a vector, we need to use the aes() function. Here is an example where we color with species:

ggplot(data = iris, 
       mapping = aes(x = Sepal.Width, 
                     y = Sepal.Length)) +
    geom_point(aes(color = Species))

Color = . We can also specify the colors directly inside the mapping provided in the ggplot() function. This will be seen by any geom layers and the mapping will be determined by the x- and y-axis set up in aes().

ggplot(data = iris, 
       mapping = aes(x = Sepal.Width, 
                     y = Sepal.Length, 
                     color = Species)) +
    geom_point()

Notice that we can change the geom layer and colors will be still determined by species

ggplot(data = iris, 
       mapping = aes(x = Sepal.Width, 
                     y = Sepal.Length, 
                     color = Species)) +
    geom_jitter()

Challenge 1

Use what you just learned to create a scatter plot. Use these skills to plot the of the phys_dates dataset. Plot Cond_day over Cond_night with the species showing in different colors. Is this a good way to show this type of data?

Solution to Challenge 1

ggplot(data = phys_dates, 
       mapping = aes(x = Cond_day, 
                     y = Cond_night)) +
   geom_point(aes(color = Species))

Boxplot

We can use boxplots to visualize the distribution of sepal length within each species:

ggplot(data = iris, 
       mapping = aes(x = Species, 
                     y = Sepal.Length)) +
    geom_boxplot()

By adding points to boxplot, we can have a better idea of the number of measurements and of their distribution:

ggplot(data = iris, 
       mapping = aes(x = Species, 
                     y = Sepal.Length)) +
    geom_boxplot() +
    geom_jitter(color = "tomato")

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?

The violin plot: an alternative to the boxplot

Boxplots are useful summaries, but hide the shape of the distribution. For example, if the distribution is bimodal, we would not see it in a boxplot. An alternative to the boxplot is the violin plot, where the shape (of the density of points) is drawn.

Challenge 2

Try replacing the box plot with a violin plot with geom_violin().

Solution to Challenge 2

      ggplot(data = iris, 
             mapping = aes(x = Species, 
                           y = Sepal.Length)) +
      geom_violin()

Now, let’s try a box plot with some new data.

Challenge 3

• Create a boxplot for Petal.Length.
• Overlay the boxplot layer on a jitter layer to show actual measurements.
• Add color to the data points on your boxplot according to species.

Solution to Challenge 3

     ggplot(data = iris, 
            mapping = aes(x = Species, 
                          y = Petal.Length)) +
     geom_boxplot() +
     geom_jitter(color = "tomato")

Plotting time series data

Because the iris dataset does not contain a time element, let’s use the phys_dates dataset to look at timeseries analysis. Let’s say we wanted to look at the average photosynthesis rate by month and plant Functional group (Fgroup). First we need to group the data and calculate the mean photosynthesis measurement within each group:

monthly_photo_fg <- phys_dates %>%
  group_by(Month, Fgroup)%>%
  summarize(mean_phot = mean(Photo))

Time series data can be visualized as a line plot with years on the x axis and counts on the y axis:

ggplot(data = monthly_photo_fg, 
       mapping = aes(x = Month, 
                     y = mean_phot)) +
     geom_line()

Unfortunately, this plot does not represent the data in a usable manner, because we plotted data for all the functional groups together. We need to tell ggplot to draw a line for each species by modifying the aesthetic function to include group = Species:

ggplot(data = monthly_photo_fg, 
       mapping = aes(x = Month, 
                     y = mean_phot, 
                     group = Fgroup)) +
     geom_line()

We will be able to distinguish species in the plot if we add colors (using color also automatically groups the data):

ggplot(data = monthly_photo_fg, 
       mapping = aes(x = Month, 
                     y = mean_phot, 
                     color = Fgroup)) +
     geom_line()

Faceting

ggplot2 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 species:

ggplot(data = monthly_photo_fg, 
       mapping = aes(x = Month, 
                     y = mean_phot)) +
     geom_line() +
    facet_wrap(~ Fgroup)

Now we would like to split the line in each plot by the species within each functional group. To do that we need to calculate means in the data frame grouped by month, Species, and Fgroup:

mo_sp_fg <- phys_dates %>%
  group_by(Month, Species, Fgroup)%>%
  summarize(mean_phot = mean(Photo))

We can now make the faceted plot by splitting further by species using color (within a single plot):

ggplot(data = mo_sp_fg, 
       mapping = aes(x = Month, 
                     y = mean_phot, 
                     color = Species)) +
     geom_line() +
    facet_wrap(~ Fgroup) 

Usually plots with white background look more readable when printed. We can set the background to white using the function theme_bw(). Additionally, you can remove the grid:

ggplot(data = mo_sp_fg, 
       mapping = aes(x = Month, 
                     y = mean_phot, 
                     color = Species)) +
     geom_line() +
    facet_wrap(~ Fgroup) +
     theme_bw() +
     theme(panel.grid = element_blank())

ggplot2 themes

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_minimal() and 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 (including an Excel 2003 theme). The ggplot2 extensions website provides a list of packages that extend the capabilities of ggplot2, including additional themes.

Challenge 4

Use what you just learned to create a plot that depicts how the average MD of each species changes over a season. Color the lines by functional group.

Solution to Challenge 4

         seasonCond <- phys_dates %>%
               group_by(Month, Species, Fgroup) %>%
               summarize(avg_cond = mean(Percent_cond))

        ggplot(data = seasonCond, 
               mapping = aes(x=Month, 
                             y=avg_cond, 
                             color = Fgroup)) +
        geom_line() +
        facet_wrap(~ Species) +
        theme_bw()

The facet_wrap geometry extracts plots into an arbitrary number of dimensions to allow them to cleanly fit on one page. On the other hand, the facet_grid geometry allows you to explicitly specify how you want your plots to be arranged via formula notation (rows ~ columns; a . can be used as a placeholder that indicates only one row or column).

Let’s modify the previous plot to compare how the photosynthesis rate of functional groups have changed through time:

ggplot(data = mo_sp_fg, 
       mapping = aes(x = Month, 
                     y = mean_phot, 
                     color = Species)) +
    geom_line() +
    facet_grid(Fgroup ~ .)

# One row, facet by column
ggplot(data = mo_sp_fg, 
       mapping = aes(x = Month, 
                     y = mean_phot, 
                     color = Species)) +
    geom_line() +
    facet_grid(.~ Fgroup)

Customization

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:

ggplot(data = mo_sp_fg, 
       mapping = aes(x = Month, 
                     y = mean_phot, 
                     color = Species)) +
     geom_line() +
    facet_wrap(~ Fgroup) +
    labs(title = "Average photosynthesis over a season",
         x = "Month of observation",
         y = "Mean photosynthesis") +
    theme_bw()

The axes have more informative names, but their readability can be improved by increasing the font size:

ggplot(data = mo_sp_fg, 
       mapping = aes(x = Month, 
                     y = mean_phot, 
                     color = Species)) +
     geom_line() +
    facet_wrap(~ Fgroup) +
    labs(title = "Average photosynthesis over a season",
         x = "Month of observation",
         y = "Mean photosynthesis") +
    theme_bw() +
    theme(text=element_text(size = 16))

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:

ggplot(data = mo_sp_fg, 
       mapping = aes(x = Month, 
                     y = mean_phot, 
                     color = Species)) +
     geom_line() +
    facet_wrap(~ Fgroup) +
    labs(title = "Average photosynthesis over a season",
         x = "Month of observation",
         y = "Mean photosynthesis") +
    theme_bw() +
    theme(axis.text.x = element_text(color = "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))

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:

grey_theme <- 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))
ggplot(phys_dates, 
       aes(x = Species, 
           y = Photo)) +
    geom_boxplot() +
    grey_theme

Arranging and exporting plots

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 gridExtra package allows us to combine separate ggplots into a single figure using grid.arrange():

install.packages("gridExtra")

library(gridExtra)

sp_photo_boxplot <- ggplot(phys_dates, 
       aes(x = Species, 
           y = Photo)) +
    geom_boxplot() +
    grey_theme

sp_photo_line<-ggplot(data = mo_sp_fg, 
       mapping = aes(x = Month, 
                     y = mean_phot, 
                     color = Species)) +
     geom_line() +
    facet_wrap(~ Fgroup) +
    labs(title = "Average photosynthesis over a season",
         x = "Month of observation",
         y = "Mean photosynthesis") +
    grey_theme

grid.arrange(sp_photo_boxplot, sp_photo_line, ncol = 2, widths = c(4, 6))

In addition to the ncol and nrow arguments, used to make simple arrangements, there are tools for constructing more complex layouts.

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.

Instead, use the ggsave() function, which allows you easily change the dimension and resolution of your plot by adjusting the appropriate arguments (width, height and dpi).

Make sure you have the fig_output/ folder in your working directory.

my_plot <- ggplot(phys_dates, 
       aes(x = Species, 
           y = Photo)) +
    geom_boxplot() +
    grey_theme
  
ggsave("fig_output/boxplot.png", my_plot, width = 15, height = 10)

# This also works for grid.arrange() plots
combo_plot <- 
grid.arrange(sp_photo_boxplot, sp_photo_line, ncol = 2, widths = c(4, 6))

ggsave("fig_output/combo_plot.png", combo_plot, width = 10, height = 5,  dpi = 300)

Note: The parameters width and height also determine the font size in the saved plot.

Final Plotting Challenge

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 ggplot2 cheat sheet for inspiration: https://www.rstudio.com/wp-content/uploads/2015/08/ggplot2-cheatsheet.pdf 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 http://www.cookbook-r.com/Graphs/Colors_(ggplot2)/).

Key Points

• Creating a ggplot require 3 things: data, aesthetics, and geoms

• Ggplots are highly customizable.

• Faceting lets you make smaller graphs with cleaner plot areas.

• Custom and premade themes can applied to any plot.