11.6 pirateplot()

Table 11.4: pirateplot() function arguments

Argument	Description
formula	A formula specifying a y-axis variable as a function of 1, 2 or 3 x-axis variables. For example, formula = weight ~ Diet + Time will plot weight as a function of Diet and Time
data	A dataframe containing the variables specified in formula
theme	A plotting theme, can be an integer from 1 to 4. Setting theme = 0 will turn off all plotting elements so you can then turn them on individually.
pal	The color palette. Can either be a named color palette from the piratepal() function (e.g. "basel", "xmen", "google") or a standard R color. For example, make a black and white plot, set pal = "black"
cap.beans	If cap.beans = TRUE, beans will be cut off at the maximum and minimum data values

A pirateplot a plot contained in the yarrr package written specifically by, and for R pirates The pirateplot is an easy-to-use function that, unlike barplots and boxplots, can easily show raw data, descriptive statistics, and inferential statistics in one plot. Figure 11.5 shows the four key elements in a pirateplot:

Figure 11.5: The pirateplot(), an R pirate’s favorite plot!

Table 11.5: 4 elements of a pirateplot()

Element	Description
Points	Raw data.
Bar / Line	Descriptive statistic, usually the mean or median
Bean	Smoothed density curve showing the full data distribution.
Band	Inference around the mean, either a Bayesian Highest Density Interval (HDI), or a Confidence Interval (CI)

The two main arguments to pirateplot() are formula and data. In formula, you specify plotting variables in the form y ~ x, where y is the name of the dependent variable, and x is the name of the independent variable. In data, you specify the name of the dataframe object where the variables are stored.

Let’s create a pirateplot of the ChickWeight data. I’ll set the dependent variable to weight, and the independent variable to Time using the argument formula = weight ~ Time:

yarrr::pirateplot(formula = weight ~ Time, # dv is weight, iv is Diet
                   data = ChickWeight,
                   main = "Pirateplot of chicken weights",
                   xlab = "Diet",
                   ylab = "Weight")

11.6.1 Pirateplot themes

There are many different pirateplot themes, these themes dictate the overall look of the plot. To specify a theme, just use the theme = x argument, where x is the theme number:

For example, here is a pirateplot height data from the pirates dataframe using theme = 3. Here, I’ll plot pirates’ heights as a function of their sex and whether or not they wear a headband. I’ll also make the plot all grayscale by using the pal = "gray" argument:

yarrr::pirateplot(formula = height ~ sex + headband,    # DV = height, IV1 = sex, IV2 = headband
                  data = pirates,           
                  theme = 3,
                  main = "Pirate Heights",
                  pal = "gray")

11.6.2 Customizing pirateplots

Regardless of the theme you use, you can always customize the color and opacity of graphical elements. To do this, specify one of the following arguments. Note: Arguments with .f. correspond to the filling of an element, while .b. correspond to the border of an element:

Table 11.6: Customising plotting elements

element	color	opacity
points	point.col, point.bg	point.o
beans	bean.f.col, bean.b.col	bean.f.o, bean.b.o
bar	bar.f.col, bar.b.col	bar.f.o, bar.b.o
inf	inf.f.col, inf.b.col	inf.f.o, inf.b.o
avg.line	avg.line.col	avg.line.o

For example, I could create the following pirateplots using theme = 0 and specifying elements explicitly:

pirateplot(formula = weight ~ Time,
           data = ChickWeight,
           theme = 0,
           main = "Fully customized pirateplot",
           pal = "southpark", # southpark color palette
           bean.f.o = .6, # Bean fill
           point.o = .3, # Points
           inf.f.o = .7, # Inference fill
           inf.b.o = .8, # Inference border
           avg.line.o = 1, # Average line
           bar.f.o = .5, # Bar
           inf.f.col = "white", # Inf fill col
           inf.b.col = "black", # Inf border col
           avg.line.col = "black", # avg line col
           bar.f.col = gray(.8), # bar filling color
           point.pch = 21,
           point.bg = "white",
           point.col = "black",
           point.cex = .7)

If you don’t want to start from scratch, you can also start with a theme, and then make selective adjustments:

pirateplot(formula = weight ~ Time,
           data = ChickWeight,
           main = "Adjusting an existing theme",
           theme = 2,  # Start with theme 2
           inf.f.o = 0, # Turn off inf fill
           inf.b.o = 0, # Turn off inf border
           point.o = .2,   # Turn up points
           bar.f.o = .5, # Turn up bars
           bean.f.o = .4, # Light bean filling
           bean.b.o = .2, # Light bean border
           avg.line.o = 0, # Turn off average line
           point.col = "black") # Black points

Just to drive the point home, as a barplot is a special case of a pirateplot, you can even reduce a pirateplot into a horrible barplot:

# Reducing a pirateplot to a (at least colorful) barplot
pirateplot(formula = weight ~ Diet,
           data = ChickWeight,
           main = "Reducing a pirateplot to a (horrible) barplot",
           theme = 0,                                    # Start from scratch
           pal = "black",
           inf.disp = "line",                            # Use a line for inference
           inf.f.o = 1,                                  # Turn up inference opacity
           inf.f.col = "black",                          # Set inference line color
           bar.f.o = .3)                                

There are many additional arguments to pirateplot() that you can use to complete customize the look of your plot. To see them all, look at the help menu with ?pirateplot or look at the vignette at

Table 11.7: Additional pirateplot() customizations.

Element	Argument	Examples
Background color	back.col	back.col = 'gray(.9, .9)'
Gridlines	gl.col, gl.lwd, gl.lty	gl.col = 'gray', gl.lwd = c(.75, 0), gl.lty = 1
Quantiles	quant, quant.lwd, quant.col	quant = c(.1, .9), quant.lwd = 1, quant.col = 'black'
Average line	avg.line.fun	avg.line.fun = median
Inference Calculation	inf.method	inf.method = 'hdi', inf.method = 'ci'
Inference Display	inf.disp	inf.disp = 'line', inf.disp = 'bean', inf.disp = 'rect'

# Additional pirateplot customizations
pirateplot(formula = weight ~ Diet, 
           data = ChickWeight,
           main = "Adding quantile lines and background colors",
           theme = 2,
           cap.beans = TRUE,
           back.col = transparent("blue", .95), # Add light blue background
           gl.col = "gray", # Gray gridlines
           gl.lwd = c(.75, 0),
           inf.f.o = .6, # Turn up inf filling
           inf.disp = "bean", # Wrap inference around bean
           bean.b.o = .4, # Turn down bean borders
           quant = c(.1, .9), # 10th and 90th quantiles
           quant.col = "black") # Black quantile lines

11.6.3 Saving output

If you include the plot = FALSE argument to a pirateplot, the function will return some values associated with each bean in the plot. In the next chunk, I’ll

# Create a pirateplot
pirateplot(formula = tattoos ~ sex + headband,
           data = pirates)

# Save data from the pirateplot to an object
tattoos.pp <- pirateplot(formula = tattoos ~ sex + headband,
                         data = pirates,
                         plot = FALSE)

Now I can access the summary and inferential statistics from the plot in the tattoos.pp object. The most interesting element is $summary which shows summary statistics for each bean (aka, group):

# Show me statistics from groups in the pirateplot
tattoos.pp
## $summary
##      sex headband bean.num   n  avg inf.lb inf.ub
## 1 female       no        1  55  5.0    4.3    5.5
## 2   male       no        2  47  4.3    3.2    5.0
## 3  other       no        3  11  5.3    2.5    7.2
## 4 female      yes        4 409 10.0    9.8   10.3
## 5   male      yes        5 443 10.0    9.7   10.3
## 6  other      yes        6  35 10.6    9.9   11.4
## 
## $avg.line.fun
## [1] "mean"
## 
## $inf.method
## [1] "hdi"
## 
## $inf.p
## [1] 0.95

Once you’ve created a plot with a high-level plotting function, you can add additional elements with low-level functions. For example, you can add data points with points(), reference lines with abline(), text with text(), and legends with legend().