MexBrewer

MexBrewer is a package with color palettes inspired by the works of Mexican painters and muralists. This package was motivated and draws heavily from the code of Blake R. Mills’s {MetBrewer}, the package with color palettes form the Metropolitan Museum of Art of New York. The structure of the package and coding, like {MetBrewer}, are based on {PNWColors} and {wesanderson}.

Installation

The package is available from CRAN:

install.packages("MexBrewer")

The development version of the package can be installed like so:

if (!require("remotes")) install.packages("remotes")
# remotes::install_github("paezha/MexBrewer")
install.packages("MexBrewer", repos = c('https://paezha.r-universe.dev', 'https://cloud.r-project.org')) 

Artists

Electa Arenal

Revolución

This palette is called Revolucion.

---

Olga Costa

Naturaleza

This palette is called Naturaleza.

Ofrenda

This palette is called Ofrenda.

Vendedora

This palette is called Vendedora.

---

María Izquierdo

Alacena

This palette is called Alacena.

La Tierra

This palette is called Tierra.

---

Frida Khalo

La Casa Azul

These palettes are called Casita1, Casita2, and Casita3. They are inspired by the colors of Frida’s home in Coyoacán, Mexico City.

---

Rina Lazo

Venerable Abuelo Maiz

This palette is called Maiz.

---

Fanny Rabel

La Ronda del Tiempo

This palette is called Ronda.

---

Aurora Reyes

El atentado a las maestras rurales

This palette is called Atentado.

Aurora, Concha, y Frida

This work of Aurora Rivera inspired three palettes, called Aurora, Concha, and Frida.

---

Remedios Varo

La Huida

This palette is called Huida.

Taurus

This work of Remedios Varo inspired two palettes, called Taurus1 and Taurus2.

---

Examples

library(aRtsy) # Koen Derks' package for generative art 
library(flametree) # Danielle Navarro's package for generative art
library(MexBrewer)
library(sf)
library(tidyverse)

Invoke data sets used in the examples:

data("mx_estados") # Simple features object with the boundaries of states in Mexico
data("df_mxstate_2020") # Data from {mxmaps }with population statistics at the state level

Join population statistics to state boundaries:

mx_estados <- mx_estados |>
  left_join(df_mxstate_2020 |>
              #Percentage of population that speak an indigenous language
              mutate(pct_ind_lang = indigenous_language/pop * 100) |> 
              dplyr::transmute(pop2020 = pop,
                               am2020 = afromexican,
                               state_name, 
                               pct_ind_lang),
            by = c("nombre" = "state_name"))

Distribution of population by geographic region in Mexico:

ggplot(data = mx_estados,
       aes(x = region, y = pop2020, fill = region)) +
  geom_boxplot() +
  scale_fill_manual(values = mex.brewer("Concha", n = 5)) +
  theme_minimal()

Percentage of population who speak an indigenous language in 2020 by state:

ggplot() +
  geom_sf(data = mx_estados,
          aes(fill = pct_ind_lang),
          color = "white",
          size = 0.08) +
  scale_fill_gradientn(colors = mex.brewer("Tierra")) +
  theme_minimal()

Some Rtistry

Danielle Navarro’s {flametree}

The following three images were created using the {flametree} package.

# pick some colours
shades <- MexBrewer::mex.brewer("Vendedora") |>
  as.vector()

# data structure defining the trees
dat <- flametree_grow(seed = 3563,
                      time = 11, 
                      trees = 10)

# draw the plot
dat |> 
  flametree_plot(
    background = shades[1],
    palette = shades[2:length(shades)], 
    style = "nativeflora"
  )

# pick some colours
shades <- MexBrewer::mex.brewer("Concha") |>
  as.vector()

# data structure defining the trees
dat <- flametree_grow(seed = 3536,
                      time = 8, 
                      trees = 6)

# draw the plot
dat |> 
  flametree_plot(
    background = shades[1],
    palette = rev(shades[2:length(shades)]), 
    style = "wisp"
  )

# pick some colours
shades <- MexBrewer::mex.brewer("Maiz") |>
  as.vector()

# data structure defining the trees
dat <- flametree_grow(seed = 3653,
                      time = 8, 
                      trees = 6)

# draw the plot
dat |> 
  flametree_plot(
    background = shades[1],
    palette = shades[2:length(shades)], 
    style = "minimal"
  )

Koen Derks’s aRtsy

The following three images were created using the {aRtsy} package.

Functions:

my_formula <- list(
  x = quote(runif(1, -1, 1) * x_i^2 - sin(y_i^2)),
  y = quote(runif(1, -1, 1) * y_i^3 - cos(x_i^2))
)

canvas_function(colors = mex.brewer("Atentado"), 
                polar = FALSE,
                by = 0.005,
                formula = my_formula)

Mosaic:

canvas_squares(colors = mex.brewer("Alacena"), 
               cuts = 20,
               ratio = 1.5,
               resolution = 200,
               noise = TRUE)

Mandelbrot’s set:

canvas_mandelbrot(colors = mex.brewer("Naturaleza"), 
                  zoom = 8,
                  iterations = 200,
                  resolution = 500)

Meghan S. Harris’s waves

These plots are adaptations of Meghan Harris’s artsy waves. Create data frames with wave functions:

##Set up the "range" on the x axis for horizontal waves=====
wave_theta <- seq(from = -pi,
                  to = -0, 
                  by = 0.01) 

# Create waves using functions
wave_1 <- data.frame(x = wave_theta) |> 
  mutate(y = (sin(x) * cos(2 * wave_theta) + exp(x * 2)))

wave_2 <- data.frame(x = wave_theta) |>
  mutate(y = (0.5 * sin(x) * cos(2.0 * wave_theta) + exp(x)) - 0.5)

Define a function to convert a single wave into a set of n waves. The function takes a data frame with a wave function and returns a data frame with n waves:

# Creating a function for iterations====

wave_maker <- function(wave_df, n, shift){
  
  #Create an empty list to store our multiple dataframes(waves)#
  wave_list<- list()
  
  #Create a for loop to iteratively make "n" waves shifted a distance `shift` from each other #
  for(i in seq_along(1:n)){
    
    wave_list[[i]] <- wave_df |>
      mutate(y = y - (shift * i),
             group = i)  
  }
  
  #return the completed data frame to the environment#
  return(bind_rows(wave_list))
}

Create layered waves using the data frames with the wave functions above:

wave_layers <- rbind(wave_1 |>
                       wave_maker(n = 5, 
                                  shift = 0.075),
                     wave_2 |>
                       wave_maker(n = 5, 
                                  shift = 0.075) |>
                       mutate(group = group + 5)) # adjust the group counter to identify waves uniquely

Plot layered waves using cartesian coordinates and palette Ofrenda:

ggplot(wave_layers) +
  geom_rect(aes(xmin = -pi, 
                xmax = -0.0, 
                ymin = min(y) - 0.50,
                ymax = max(y) + 0.30 ),
            size = 2.5,
            color = mex.brewer("Ofrenda")[6],
            fill = mex.brewer("Ofrenda")[4]) +
  geom_rect(aes(xmin = -pi, 
                xmax = -0.0, 
                ymin = min(y) - 0.50,
                ymax = max(y) + 0.30 ),
            size = 1,
            color = "black",
            fill = NA) +
  geom_ribbon(aes(x, 
                  ymin = y - 0.025 * 4 * x,
                  ymax = y + 0.015 * 10 * x, 
                  group = group, 
                  fill = group),
              color = "black",
              size = 0.5) + 
  scale_fill_gradientn(colors = mex.brewer("Ofrenda"))+
  theme_void() +
  theme(legend.position = "none")
#> Warning: Using `size` aesthetic for lines was deprecated in ggplot2 3.4.0.
#> ℹ Please use `linewidth` instead.

Plot layered waves using polar coordinates and palette Atentado:

ggplot(wave_layers) +
  geom_rect(aes(xmin = -pi, 
                xmax = -0.0, 
                ymin = min(y) - 0.45,
                ymax = max(y) + 0.30 ),
            size = 2.5,
            color = mex.brewer("Atentado")[6],
            fill = mex.brewer("Atentado")[3]) +
  geom_rect(aes(xmin = -pi, 
                xmax = -0.0, 
                ymin = min(y) - 0.45,
                ymax = max(y) + 0.30 ),
            size = 1,
            color = "black",
            fill = NA) +
  geom_ribbon(aes(x, 
                  ymin = y - 0.025 * 4 * x,
                  ymax = y + 0.015 * 10 * x, 
                  group = group, 
                  fill = group),
              color = "black",
              size = 0.5) + 
  scale_fill_gradientn(colors = mex.brewer("Atentado")) +
  coord_polar(theta = "x",  
              start = 0, 
              direction = 1, 
              clip = "on") +
  theme_void() +
  theme(legend.position = "none")