Developing Patterns

Developing Patterns - overview

To develop a pattern for use with {gridpattern} (and packages that use it such as {ggpattern}) you will need to:

1. Decide whether this is a geometry-based pattern or an array-based pattern.
2. Create a function with the required arguments and return value
3. Use register_pattern() to let {gridpattern} know that a certain pattern name should be delegated to your function.

This vignette shows how to:

• write a create_pattern() function of the correct signature for a geometry-based pattern or array-based pattern
• Instruct {gridpattern} on where to find this user-defined pattern

Pattern Classification - geometry-based and array-based

There are only 2 high-level classes of pattern supported by gridpattern - geometry-based and array-based.

Geometry-based patterns create a series of geometry objects and trim them (using sf::st_intersection(), gridGeometry::polyclipGrob(), gridpattern::clippingPathGrob(), etc) to be within the boundary of the grob. For example, the ‘stripes’ pattern in gridpattern is a series of equally spaced rectangular polygons.

Array-based patterns are RGBA image arrays. Any supplied image will be processed by gridpattern to ensure it is masked to only apply to the area within the grob’s boundary.

{ggpattern} Aesthetics - Descriptions and Defaults

Although custom {gridpattern} pattern parameters need not limit itself to the set of aesthetics provided by {ggpattern} doing so may make your pattern more useful for others:

Note {ggpattern} may also pass other geom aesthetics of possible interest such as fill. Also note that {ggpattern} will only pass pattern aesthetics values of length one but if the pattern is called directly by gridpattern::patternGrob() then the pattern may be passed pattern parameters of arbitrary length. {gridpattern} sets no defaults for the spare {ggpattern} aesthetics pattern_option_1 - pattern_option_5 and pattern_phase (nor for any other parameter not used by a builtin pattern) so a pattern that reads them should set its own defaults with the defaults argument of register_pattern() so they are also available when the pattern is called directly by gridpattern::patternGrob().

Geometry-based pattern functions - Formal Arguments and Return Values

All geometry-based pattern creation functions must:

1. Have the exact function signature: function(params, boundary_df, aspect_ratio, legend)

   • params - parameters for the pattern (the aesthetics) e.g pattern_fill
   • boundary_df - data.frame containing polygon information i.e. The polygon_df format.
   • aspect_ratio - the best guess that {gridpattern} / {ggpattern} is able to make as to the aspect ratio of the viewport in which this pattern is being rendered.
   • legend logical value to indicate whether or not this function is being called to render a key legend or the in-place geom fill.

2. Return a grid grob object. This can be any valid grob including a grid::grobTree(). The user should make sure it lies within the boundary represented by boundary_df either by clipping with functions like sf::st_intersection(), gridGeometry::polyclipGrob(), gridpattern::clippingPathGrob(), etc. or using bounded grob functions like gridpattern::patternGrob() or grid::polygonGrob().

Array-based pattern functions - Formal Arguments and Return Values

All array-based pattern creation functions must:

1. Have the exact function signature: function(width, height, params, legend)
   • width,height - dimensions of the bounding box of the geom area
   • params - parameters from the geom (the aesthetics) e.g pattern_fill
   • legend logical value to indicate whether or not this function is being called to render a key legend or the in-place geom fill.
2. Return a 3D array of RGBA values (all values in the range [0, 1]). gridpattern itself will mask this image so that it only applies to the area within the grob’s boundary.

The polygon_df data.frame format

The polygon_df is a very simple data.frame format to contain polygon values. This is used to pass the coordinates of the geom boundary from the geom to the pattern generating function.

It contains only ‘x’ and ‘y’ columns for the coordinates, and an ‘id’ column used to signify which polygon the coordinates belong to.

The following polygon_df data.frame contains 2 polygons:

Associating a function with {gridpattern} pattern name

Use register_pattern() to associate a pattern function with a pattern name. Use kind = "geometry" (the default) for geometry-based patterns and kind = "array" for array-based patterns.

register_pattern("your_pattern_name", your_geometry_pattern_function)
register_pattern("your_pattern_name", your_array_pattern_function, kind = "array")

• Packages should register their patterns in their .onLoad() function (see Registering patterns from a package below).
• Patterns can always be referred to by their qualified name "owner::name" e.g. "pkg::name", "gridpattern::stripe", or "R_GlobalEnv::name" (for patterns registered by the user).
• A bare name "name" may be used if only one owner (the user, {gridpattern}, or a package) has a pattern with that name. Otherwise there is a conflict and using the bare name throws an error unless the user has declared a preference with prefer_pattern() e.g. prefer_pattern("R_GlobalEnv::stripe") to use a user’s custom “stripe” pattern instead of the builtin one.
• conflicting_patterns() lists patterns whose bare names are shared by more than one pattern. unprefer_pattern() removes a preference.
• Package code that relies on a particular pattern should use its qualified name.
• register_pattern()’s defaults argument may set pattern-specific default parameter values e.g. defaults = list(pattern_type = "running", pattern_spacing = 0.1). Values may also be functions of the form function(params, gp) of the parameters filled in so far and the grob’s gpar() e.g. pattern_amplitude = function(params, gp) 0.5 * params$pattern_spacing or pattern_size = function(params, gp) gp$fontsize %||% 12. The generic defaults are filled in first and then the pattern-specific defaults in the order given so a function default may use any generic parameter and any earlier pattern-specific default. Names may also be the plain {ggplot2} aesthetics {ggpattern} passes along e.g. fill = "black" lets a pattern that reads params$fill under {ggpattern} also work with grid.pattern(); list such names in the unprefixed argument (e.g. unprefixed = "fill") since otherwise an unprefixed name is an error (it is probably a forgotten pattern_ prefix). Note {ggpattern} supplies its own defaults for most of its pattern aesthetics.
• registered_patterns() lists all the available patterns and unregister_pattern() removes a registration.
• has_pattern() checks whether pattern names currently resolve to a pattern.
• local_pattern() registers a pattern only until the current function (or test) exits.

.onLoad <- function(libname, pkgname) {
    rlang::on_package_load("gridpattern", {
        if (exists("register_pattern", envir = asNamespace("gridpattern"), inherits = FALSE)) {
            gridpattern::register_pattern("brick", create_pattern_brick)
        } else {
            funcs <- getOption("ggpattern_geometry_funcs", list())
            funcs[["brick"]] <- create_pattern_brick
            options(ggpattern_geometry_funcs = funcs)
        }
    })
}

Example geometry-based pattern #1 (recreate polygon)

All geometry-based pattern creation functions must:

1. Have the exact function signature: function(params, boundary_df, aspect_ratio, legend)
   • params - parameters from the geom (the aesthetics) e.g pattern_fill
   • boundary_df - data.frame containing polygon information i.e. The polygon_df format.
   • aspect_ratio - the best guess that {gridpattern} / {ggpattern} is able to make as to the aspect ratio of the viewport in which this pattern is being rendered.
   • legend logical value to indicate whether or not this function is being called to render a key legend or the in-place geom fill.
2. Return a grid grob bounded by the boundary represented by boundary_df (including grid structures like a grid::grobTree()).

For this example we’ll create a simple single color fill pattern based on grid::polygonGrob() called ‘polygon’.

create_pattern_polygon <- function(params, boundary_df, aspect_ratio, legend = FALSE) {
    x <- boundary_df$x
    y <- boundary_df$y
    id <- boundary_df$id

    alpha <- ifelse(is.na(params$pattern_alpha), 1, params$pattern_alpha)
    gp <- grid::gpar(alpha = alpha,
                     col = params$pattern_colour,
                     fill = params$pattern_fill,
                     lty = params$pattern_linetype,
                     lwd = params$pattern_linewidth)

    grid::polygonGrob(x = x, y = y, id = id, default.units = "npc", gp = gp)
}

We register the function with register_pattern(). The name corresponds to the pattern name used with grid.pattern() or the geom - in this case we will be using pattern = 'polygon'.

register_pattern("polygon", create_pattern_polygon)

grid.pattern("polygon", fill = "red", size = 4, linetype = "dashed",
             x = c(0.05, 0.05, 0.305, 0.305), y = c(0.05, 0.305, 0.305, 0.05))
grid.pattern("polygon", fill = "green", alpha = 0.2,
             x = c(0.35, 0.35, 0.65, 0.65), y = c(0.35, 0.65, 0.65, 0.35))
grid.pattern("polygon", fill = "blue", colour = "grey",
             x = c(0.7, 0.7, 1.0, 1.0), y = c(0.7, 1.0, 1.0, 0.7))

Example geometry-based pattern function #2 (using other patterns)

All geometry-based pattern creation functions must:

1. Have the exact function signature: function(params, boundary_df, aspect_ratio, legend)
   • params - parameters from the geom (the aesthetics) e.g pattern_fill
   • boundary_df - data.frame containing polygon information i.e. The polygon_df format.
   • aspect_ratio - the best guess that {gridpattern} / {ggpattern} is able to make as to the aspect ratio of the viewport in which this pattern is being rendered.
   • legend logical value to indicate whether or not this function is being called to render a key legend or the in-place geom fill.
2. Return a grid grob bounded by the boundary represented by boundary_df (including grid structures like a grid::grobTree()).

For this example we’ll create an example that re-uses the pre-existing ‘stripe’, ‘circle’, and ‘gradient’ patterns and combines them into a new ‘complex’ pattern.

create_pattern_complex <- function(params, boundary_df, aspect_ratio, legend = FALSE) {
    args <- as.list(params)
    args <- args[grep("^pattern_", names(args))]
    args$x <- boundary_df$x
    args$y <- boundary_df$y
    args$id <- boundary_df$id
    args$prefix <- ""

    args_stripe <- args
    args_stripe$pattern <- "gridpattern::stripe"
    args_stripe$pattern_density <- 0.5 * args$pattern_density
    args_stripe$pattern_spacing <- 2 * args$pattern_spacing
    grob_stripe <- do.call(gridpattern::patternGrob, args_stripe)

    args_circle <- args
    args_circle$pattern <- "gridpattern::regular_polygon"
    args_circle$pattern_shape <- c("circle", "null")
    args_circle$pattern_yoffset <- args$pattern_spacing + args$pattern_yoffset
    args_circle$pattern_type = "horizontal"
    grob_circle <- do.call(gridpattern::patternGrob, args_circle)

    args_gradient <- args
    args_gradient$pattern <- "gridpattern::gradient"
    args_gradient$pattern_fill <- "#00000070"
    args_gradient$pattern_fill2 <- "#FFFFFF70"
    args_gradient$pattern_orientation <- "vertical"
    grob_gradient <- do.call(gridpattern::patternGrob, args_gradient)

    grid::grobTree(grob_stripe, grob_circle, grob_gradient)
}

We register the function with register_pattern(). The name corresponds to the pattern name used with grid.pattern() or the geom - in this case we will be using pattern = 'complex'.

register_pattern("complex", create_pattern_complex)

grid.pattern("complex", fill = "red", angle = 45, spacing = 0.05, density = 0.3,
             x = c(0.0, 0.0, 0.3, 0.3), y = c(0.0, 0.3, 0.3, 0.0))
grid.pattern("complex", fill = "green", angle = 45, spacing = 0.2, density = 0.2,
             x = c(0.35, 0.35, 0.65, 0.65), y = c(0.35, 0.65, 0.65, 0.35))
grid.pattern("complex", fill = "blue", angle = 45, spacing = 0.1, density = 0.3,
             x = c(0.7, 0.7, 1.0, 1.0), y = c(0.7, 1.0, 1.0, 0.7))
grid::grid.polygon(x = c(0.0, 0.0, 0.3, 0.3, 0.35, 0.35, 0.65, 0.65, 0.7, 0.7, 1.0, 1.0),
                   y = c(0.0, 0.3, 0.3, 0.0, 0.35, 0.65, 0.65, 0.35, 0.7, 1.0, 1.0, 0.7),
                   id = rep(1:3, each = 4),
                   gp = grid::gpar(col = "black", fill = NA, lwd=4))

Example array-based pattern function

All array-based pattern creation functions must:

1. Have the exact function signature: function(width, height, params, legend)
   • width,height - dimensions of the bounding box of the geom area
   • params - parameters from the geom (the aesthetics) e.g pattern_fill
   • legend logical value to indicate whether or not this function is being called to render a key legend or the in-place geom fill.
2. Return a 3D array of RGBA values (all values in the range [0, 1]).

For this example we’ll create a simple example that cycles through RGBA values.

Parameters for this pattern:

• pattern_type is used to distinguish between source data for the pattern

Note: This pattern exploits vector recyling in the creation of the RGBA array, and as dimensions change the alignment of the R, G, B and A planes will not remain in a fixed relationship. Thus if you change the shape of the rendered image, you will change the nature of the pattern.

create_pattern_simple <- function(width, height, params, legend) {

  # Ensure the selected pattern is sane.
  choice <- params$pattern_type
  if (is.null(choice) || is.na(choice) || !is.character(choice)) {
    choice <- 'a'
  }

  # Choose the values with which to fill the array
  values <- switch(
    choice,
    a = rep(c(0, 1, 0, 1, 1, 0, 0, 1, 1, 1), each = 3),
    b = rep(c(1, 0, 0, 1, 0.5, 0.5, 1, 1, 0, 0, 0, 0, 0, 0.5), each = 7),
    c = rep(seq(0, 1, 0.05), each = 7),
    rep(c(0, 1, 0, 1, 1, 0, 0, 1, 1, 1), each = 3)
  )
  # Create an RGBA array of the requested dimensions
  simple_array <- array(values, dim = c(height, width, 4))

  simple_array
}

We register the function with register_pattern() using kind = "array". The name corresponds to the pattern name used with grid.pattern() or the geom - in this case we will be using pattern = 'simple'.

register_pattern("simple", create_pattern_simple, kind = "array")

grid::grid.polygon(x = c(0, 0, 1, 1), y = c(0, 1, 1, 0),
                   gp = grid::gpar(col=NA, fill="grey"))
grid.pattern("simple", type = "a",
             x = c(0.0, 0.0, 0.3, 0.3), y = c(0.0, 0.3, 0.3, 0.0))
grid.pattern("simple", type = "b",
             x = c(0.35, 0.35, 0.65, 0.65), y = c(0.35, 0.65, 0.65, 0.35))
grid.pattern("simple", type = "c",
             x = c(0.7, 0.7, 1.0, 1.0), y = c(0.7, 1.0, 1.0, 0.7))

Other examples

The {ggpattern} package contains a vignette on developing {gridpattern} / {ggpattern} patterns with a few more examples: https://coolbutuseless.github.io/package/ggpattern/articles/

Also the {gridpattern} source contains the full source code for 21 patterns: https://github.com/trevorld/gridpattern/tree/main/R