# What is a pirateplot()?

A pirateplot, is the RDI (Raw data, Descriptive statistics, and Inferential statistics) plotting choice of R pirates who are displaying the relationship between 1 to 3 categorical independent variables, and one continuous dependent variable.

A pirateplot has 4 main elements

1. points, symbols representing the raw data (jittered horizontally)
2. bar, a vertical bar showing central tendencies
3. bean, a smoothed density (inspired by Kampstra and others (2008)) representing a smoothed density
4. inf, a rectangle representing an inference interval (e.g.; Bayesian Highest Density Interval or frequentist confidence interval)

# Main arguments

Here are the main arguments to pirateplot()

Main Pirateplot Arguments
Argument Description Examples
formula A formula height ~ sex + eyepatch, weight ~ Time
data A dataframe pirates, ChickWeight
main Plot title ‘Pirate heights’, ’Chicken Weights
pal A color palette ‘xmen’, ‘black’
theme A plotting theme 0, 1, 2
inf Type of inference ‘ci’, ‘hdi’, ‘iqr’

# Themes

pirateplot() currently supports three themes which change the default look of the plot. To specify a theme, use the theme argument:

## Theme 1

theme = 1, the default theme, shows all four pirateplot elements:

# Default theme
pirateplot(formula = weight ~ Time,
data = ChickWeight,
theme = 1,
main = "theme = 1")

## Theme 2

theme = 2, emphasizes the beans and changes the look of the points:

# Default theme
pirateplot(formula = weight ~ Time,
data = ChickWeight,
theme = 2,
main = "theme = 2")

## Theme 0

theme = 0 allows you to start a pirateplot from scratch – that is, it turns of all elements. You can then selectively turn elements on with individual arguments (e.g.; bean.f.o, point.o)

# Default theme
pirateplot(formula = weight ~ Time,
data = ChickWeight,
theme = 0,
main = "theme = 0\nStart from scratch")

# Color palettes

You can specify a general color palette using the pal argument. You can do this in two ways.

The first way is to specify the name of a color palette in the piratepal() function. Here they are:

piratepal("all")

For example, here is a pirateplot using the "southpark" palette

pirateplot(formula = weight ~ Time,
data = ChickWeight,
pal = "southpark",
theme = 2,
main = "southpark color palette")

The second method is to simply enter a vector of one or more colors. Here, I’ll create a black and white pirateplot of the same data by specifying pal = gray(.1)

pirateplot(formula = weight ~ Time,
data = ChickWeight,
pal = gray(.1),
main = "pal = gray(.1)")

You can even specify palette colors as a vector to selectively color elements:

pirateplot(formula = weight ~ Time,
data = ChickWeight,
pal = gray(c(rep(.9, 5), .1, rep(.9, 3), .1)))

# Customising elements

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

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",
bean.f.o = .2, # Bean fill
point.o = .2, # Points
inf.f.o = .4, # Inference fill
inf.b.o = .8, # Inference border
avg.line.o = 1, # Average line
inf.f.col = "white", # Inf fill col
inf.b.col = "black", # Inf border col
point.col = "black", # point col
avg.line.col = "black", # avg line col
point.cex = .5 # Small points
)

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

pirateplot(formula = weight ~ Time,
data = ChickWeight,
main = "Adjusting an existing theme",
inf.f.o = 0, # Turn off inf fill
inf.b.o = 0, # Turn off inf border
point.o = .2,   # Turn up points
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:

pirateplot(formula = weight ~ Time,
data = ChickWeight,
main = "Reducing a pirateplot to a barplot",
theme = 0,   # Start from scratch
bar.f.o = .7, # Just turn on the bars
gl.col = gray(.7)
)

There are several more arguments that you can use to customise your plot:

element arguments 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

Here’s an example using a background color, gridlines, and quantile lines.

pirateplot(formula = weight ~ Time,
data = ChickWeight,
main = "Chicken weights by Time",
theme = 2,
back.col = gray(.95), # Add light gray background
gl.col = "gray", # Gray gridlines
gl.lwd = c(.75, .5),
quant = c(.1, .9), # 10th and 90th quantiles
quant.col = "black" # Black quantile lines
)

# Multiple IVs

You can use up to 3 categorical IVs in your plot. Here are some examples:

pirateplot(formula = height ~ sex + eyepatch,
data = pirates,
main = "Pirate Heights",
theme = 2,
gl.col = gray(.7))

pirateplot(formula = time ~ genre + sequel,
data = subset(movies,
genre %in% c("Action", "Adventure", "Comedy") &
time > 0),
main = "Movie running times",
theme = 2,
gl.col = gray(.7),
inf.f.col = piratepal("basel")[1:3],
bean.f.o = .1,
point.o = .05,
avg.line.o = 0
)

# Contribute!

I am very happy to receive new contributions and suggestions to improve the pirateplot. If you come up a new theme (i.e.; customisation) that you like, or have a favorite color palette that you’d like to have implemented, please contact me (yarrr.book@gmail.com) or post an issue at www.github.com/ndphillips/yarrr/issues and I might include it in a future update.

# References

Kampstra, Peter, and others. 2008. “Beanplot: A Boxplot Alternative for Visual Comparison of Distributions.” Journal of Statistical Software 28 (1): 1–9.