Package 'PortinariColors'

Portinari color and fill scales for ggplot2

Description

Discrete or continuous color and fill scales built from a Portinari palette. They are ordinary ggplot2 scales and accept the usual arguments (name, labels, breaks, guide, ...). Match the palette category to the data: qualitative palettes for unordered groups, sequential palettes for ordered values, and diverging palettes for values around a midpoint (see portinari.palettes()).

Usage

scale_color_portinari(
  palette = "cafe",
  type = c("discrete", "continuous"),
  direction = 1,
  colorblind = FALSE,
  ...
)

scale_colour_portinari(
  palette = "cafe",
  type = c("discrete", "continuous"),
  direction = 1,
  colorblind = FALSE,
  ...
)

scale_fill_portinari(
  palette = "cafe",
  type = c("discrete", "continuous"),
  direction = 1,
  colorblind = FALSE,
  ...
)

Arguments

palette	Name of the palette. See portinari.palettes().
type	"discrete" (default) for categorical variables, or "continuous" for numeric variables.
direction	1 (default) or -1 to reverse the palette.
colorblind	If TRUE, only colorblind-safe colors are used.
...	Further arguments passed to ggplot2::discrete_scale() (discrete) or ggplot2::scale_colour_gradientn() / ggplot2::scale_fill_gradientn() (continuous).

Value

A ggplot2 scale object.

See Also

portinari.pal()

Examples

library(ggplot2)
# Qualitative: unordered groups
ggplot(mtcars, aes(wt, mpg, colour = factor(cyl))) +
  geom_point(size = 3) +
  scale_color_portinari("cafe", name = "Cylinders")

# Sequential: ordered values
ggplot(faithfuld, aes(waiting, eruptions, fill = density)) +
  geom_raster() +
  scale_fill_portinari("guerra_azul", type = "continuous", direction = -1)

# Diverging: values around a midpoint
ggplot(mtcars, aes(wt, mpg, colour = mpg - mean(mpg))) +
  geom_point(size = 3) +
  scale_color_portinari("guerra", type = "continuous", name = "mpg vs mean")

# Diverging, discrete: an ordered scale with a neutral middle
ggplot(mtcars, aes(factor(gear), fill = factor(carb))) +
  geom_bar() +
  scale_fill_portinari("retirantes")

---

Check colorblind safety of a Portinari palette

Description

Simulates protanopia, deuteranopia and tritanopia (Machado et al., 2009) and measures the smallest CIELAB color difference (CIE76 dE) within the discrete palette of n colors returned by portinari.pal(), for every n. A discrete palette of n colors is colorblind-safe when all three simulations keep that minimum at or above threshold.

Usage

portinari.cvd.check(palette = "cafe", threshold = 10)

Arguments

palette	Name of the palette. See portinari.palettes().
threshold	Minimum acceptable dE. Default 10.

Value

A data frame with one row per palette size n (from 2 up to the palette length) and columns normal, protanopia, deuteranopia and tritanopia (minimum pairwise dE in each condition) and colorblind_safe.

Examples

portinari.cvd.check("inah")

---

Portinari color palette

Description

Returns a vector of hex colors from a Portinari palette.

Usage

portinari.pal(
  palette = "cafe",
  n,
  type = c("discrete", "continuous"),
  direction = 1,
  colorblind = FALSE
)

Arguments

palette	Name of the palette. See portinari.palettes() for choices.
n	Number of colors. Defaults to the length of the palette (or of its colorblind-safe subset when colorblind = TRUE).
type	"discrete" returns distinct colors and errors when n is larger than the palette. "continuous" interpolates. When omitted, the type is chosen from n.
direction	1 (default) for the standard order, -1 to reverse it.
colorblind	If TRUE, only colors that remain distinguishable under protanopia, deuteranopia and tritanopia simulation are used. See portinari.cvd.check().

Details

Palettes come in three categories (see portinari.palettes()): qualitative palettes for unordered groups, sequential palettes for ordered values, and diverging palettes for values around a meaningful midpoint. Discrete palettes pick n colors that suit the category: the most distinct colors for qualitative palettes, evenly spaced steps for sequential palettes, and balanced steps either side of the light center for diverging palettes. Continuous palettes interpolate the full palette in CIELAB space.

Value

A character vector of hex colors with class "portinari_palette".

See Also

scale_color_portinari(), scale_fill_portinari(), portinari.palettes()

Examples

portinari.pal("cafe")
portinari.pal("retirantes", n = 3)
portinari.pal("guerra_azul", n = 20, type = "continuous")
portinari.pal("inah", colorblind = TRUE)

---

List the available Portinari palettes

Description

List the available Portinari palettes

Usage

portinari.palettes(category = NULL)

Arguments

category

Optional filter: one or more of "qualitative", "diverging" and "sequential".

Value

A data frame with one row per palette: its category, the number of colors, how many colors are colorblind-safe (cvd_n), whether the whole palette is colorblind-friendly, and a short description. See portinari.works() for the paintings behind each palette.

Examples

portinari.palettes()
portinari.palettes("diverging")

---

Paintings behind the Portinari palettes

Description

The works each palette was drawn from, with links to the collection pages. The sequential palettes guerra_azul and paz_ouro are derived from guerra and paz and share their works.

Usage

portinari.works(palette = NULL)

Arguments

palette

Optional character vector of palette names to filter by.

Value

A data frame with columns palette, title, year, collection, inventory, url (collection or story page) and image_url (the museum's image, where one is published; NA otherwise).

Examples

portinari.works()
portinari.works("retirantes")

---

Show Portinari palettes

Description

Draws each palette as a row of color swatches using base graphics, labeled with its category.

Usage

show_portinari(palette = NULL, category = NULL)

Arguments

palette	Character vector of palette names. Defaults to all palettes (or all palettes in category).
category	Optional filter: one or more of "qualitative", "diverging" and "sequential".

Value

The palette names, invisibly.

Examples

show_portinari()
show_portinari(category = "diverging")
show_portinari(c("cafe", "inah"))

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