Code Handout - Data Visualisation with ggplot2

Last updated on 2023-07-10 | Edit this page

This document contains all of the functions that we have covered thus far in the course. It will be updated every week, after we’ve added new skills. Each function is presented alongside an example of how it is used.

All of the examples below are in the context of the Palmer Penguins, found here (link).

Foundations of ggplot()

  • ggplot() – a function to create the shell of a visualization, where specific variables are mapped to different aspects of the plot

R 

penguins %>% 
  ggplot(aes(x = bill_length_mm, y = bill_depth_mm, color = species))

  • aes() – aesthetics that can be used when creating a ggplot(), where the aesthetics can either be hard coded (e.g. color = "blue") or associated with a variable (e.g. color = sex).

    • The following are the aesthetic options for most plots:
      • x
      • y
      • alpha – changes transparency
      • color – produces colored outline
      • fill – fills with color
      • group – used with categorical variables, similar to color

  • + – an important aspect creating a ggplot() is to note that the geom_XXX() function is separated from the ggplot() function with a plus sign, +.

    • ggplot() plots are constructed in series of layers, where the plus sign separates these layers.
    • Generally, the + sign can be thought of as the end of a line, so you should always hit enter/return after it. While it is not mandatory to move to the next line for each layer, doing so makes the code a lot easier to organize and read.

R 

penguins %>% 
  ggplot(aes(x = bill_length_mm, y = bill_depth_mm, color = species)) + 
  geom_point()

Geometric Objects to Visualize the Data

  • geom_histogram( ) – adds a histogram to the plot, where the observations are binned into ranges of values and then frequencies of observations are plotted on the y-axis
    • You can specify the number of bins you want with the bins argument

R 

penguins %>% 
  ggplot(aes(x = bill_length_mm)) + 
  geom_histogram(bins = 20)
  • geom_boxplot( ) – adds a boxplot to the plot, where observations are aggregated (summarized), the min, Q1, median, Q3, and maximum are plotted as the box and whiskers, and “outliers” are plotted as points.
    • You can plot a vertical boxplot by specifying the x variable, or a horizontal boxplot by specifying the y variable.
    • Note: the min and max may not be included in the whiskers, if they are deemed to be “outliers” based on the \(1.5 \\times \\text{IQR}\) rule.

R 

## Horizontal boxplot
penguins %>% 
  ggplot(aes(x = bill_length_mm)) + 
  geom_boxplot()

## Vertical boxplot
penguins %>% 
  ggplot(aes(y = bill_length_mm)) + 
  geom_boxplot()
  • geom_density() – adds a density curve to the plot, where the probability density is plotted on the y-axis (so the density curve has a total area of one).
    • By default this creates a density curve without shading. By specifying a color in the fill argument, the density curve is shaded.
    • Can be thought of as the “one group” violin plot!

R 

penguins %>% 
  ggplot(aes(x = bill_length_mm)) + 
  geom_density(fill = "tomato")
  • geom_violin() – plots violins for each level of a categorical variable
    • Can be thought of as a hybrid mix of geom_boxplot() and geom_density(), as the density is displayed, but it is reflected to provide a plot similar in nature to a boxplot.
    • To obtain violins stacked vertically, declare the categorical variable as y. To obtain side-by-side violins, declare the categorical variable as x.

R 

## Stacked vertically
penguins %>% 
  ggplot(aes(x = bill_length_mm, y = species)) + 
  geom_violin()

## Side-by-side
penguins %>% 
  ggplot(aes(y = bill_length_mm, x = species)) + 
  geom_violin()
  • geom_bar() – creates a barchart of a categorical variable
    • Can produce stacked barcharts by specifying a variable as the fill aesthetic.
    • Can change from stacked barchart to a side-by-side barchart by specifying position = "dodge".
    • If your data are already in counts (e.g. output from count()), then you can specify the stat = "identity" argument inside geom_bar().

R 

## Stacked barchart
penguins %>%
    ggplot(aes(x = species)) +
    geom_bar(aes(fill = sex))

## Side-by-side barchart
penguins %>%
    ggplot(aes(x = species)) +
    geom_bar(aes(fill = sex),
             position = "dodge")

## If data are raw counts
penguins %>% 
  count(species, sex) %>% 
  ggplot(aes(x = species, y = n)) + 
  geom_bar(aes(fill = sex),
           stat = "identity",
           position = "dodge")
  • geom_point() – plots each observation as an (x, y) point, used to create scatterplots
    • Can use alpha to increase the transparency of the points, to reduce overplotting.
    • Can specify aesthetics inside of geom_point() for local aesthetics (point level) or inside of ggplot() for global aesthetics (plot level)

R 

penguins %>% 
  ggplot(aes(x = bill_length_mm, y = bill_depth_mm)) + 
  geom_point(aes(color = species))
  • geom_jitter() – plots each observation as an (x, y) point and adds a small amount of jitter around the point
    • Useful so that we can see each point in the locations where there are overlapping points.
    • Can specify the width and height of the jittering using the optional arguments.

R 

penguins %>% 
  ggplot(aes(y = body_mass_g, x = species)) + 
  geom_violin() + 
  geom_jitter(aes(color = sex), width = 0.25, height = 0.25)
  • geom_smooth() – plots a line over a set of points, draws the readers eye to a specific trend
    • The methods we will use are “lm” for a linear model (straight line), and “loess” for a wiggly line
    • By default, the smoother gives you gray SE bars, to remove these add se = FALSE

R 

penguins %>% 
  ggplot(aes(x = bill_length_mm, y = bill_depth_mm, color = species)) + 
  geom_point() + 
  geom_smooth(method = "lm")
  • facet_wrap() – creates subplots of your original plot, based on the levels of the variable you input
    • To facet by one variable, use ~variable.
    • To facet by two variables, use variable1 ~ variable2.
    • If you prefer for your facets to be organized in rows or columns, use the nrow and/or ncol arguments.

R 

penguins %>% 
  ggplot(aes(x = bill_length_mm, y = bill_depth_mm, color = species)) + 
  geom_point() + 
  geom_smooth(method = "lm") + 
  facet_wrap(~island, nrow = 1)

Plot Characteristics

  • labs() – specifies the plot labels, possible labels are: x, y, color, fill, title, and subtitle

R 

penguins %>% 
  ggplot(aes(x = bill_length_mm, y = bill_depth_mm, color = species)) + 
  geom_point() + 
  geom_smooth(method = "lm") +
  labs(x = "Bill Length (mm)", 
       y = "Bill Depth (mm)", 
       color = "Penguin Species")
  • theme_bw() – changes the plotting background to the classic dark-on-light ggplot2 theme.
    • This theme may work better for presentations displayed with a projector.
    • Other theme options are theme_minimal(), theme_light(), and theme_void().

R 

penguins %>% 
  ggplot(aes(x = bill_length_mm, y = bill_depth_mm, color = species)) + 
  geom_point() + 
  geom_smooth(method = "lm") +
  labs(x = "Bill Length (mm)", 
       y = "Bill Depth (mm)", 
       color = "Penguin Species") + 
  theme_bw()
  • theme() –
    • Possible options are:
      • panel.grid – controls the grid lines (panel.grid = element_blank() removes grid lines)
      • text – specifies font size for the entire plot (e.g. text = element_text(size = 16)
      • axis.text.x – specifies the font size for the x-axis text
      • axis.text.y – specifies the font size for the y-axis text
      • plot.title – specifies aspects of the plot title, can use plot.title = element_text(hjust = 0.5) to centre the title

R 

penguins %>% 
  ggplot(aes(x = bill_length_mm, y = bill_depth_mm, color = species)) + 
  geom_point() + 
  geom_smooth(method = "lm") +
  labs(x = "Bill Length (mm)", 
       y = "Bill Depth (mm)", 
       color = "Penguin Species") + 
  theme_bw() + 
  theme(axis.text.x = element_text(size = 12), 
        axis.text.y = element_text(size = 12))

Exporting Plots

  • ggsave() – convenient function for saving a plot
    • Unless specified, defaults to the last plot that was made.
    • Uses the size of the current graphics device to determine the size of the plot.

R 

plot1 <- penguins %>% 
  ggplot(aes(x = bill_length_mm, y = bill_depth_mm, color = species)) + 
  geom_point() + 
  geom_smooth(method = "lm") + 
  facet_wrap(~island, nrow = 1)

ggsave(path = "images/faceted_plot.png", plot = plot1)