MexBrewer

CRAN status

Lifecycle: stable

GitHub Stars

GitHub search hit counter: R
GitHub search hit counter: data

GitHub issues GitHub release GitHub commit activity GitHub last commit

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

Currently, there is only a development version of {MexBrewer}, which can be installed like so:

if (!require("remotes")) install.packages("remotes")
remotes::install_github("paezha/MexBrewer") 

Artists

Electa Arenal

Revolución

This palette is called Revolucion.

Revolucion

Revolucion

Revolucion

Revolucion


Olga Costa

Naturaleza

This palette is called Naturaleza.

Naturaleza

Naturaleza

Naturaleza

Naturaleza

Ofrenda

This palette is called Ofrenda.

Ofrenda

Ofrenda

Ofrenda

Ofrenda

Vendedora

This palette is called Vendedora.

Vendedora

Vendedora

Vendedora

Vendedora


María Izquierdo

Alacena

This palette is called Alacena.

Alacena

Alacena

Alacena

Alacena

La Tierra

This palette is called Tierra.

Tierra

Tierra

Tierra

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.

Casa Azul

Casa Azul

Casa Azul

Casa Azul

Casa Azul

Casa Azul

Casa Azul

Casa Azul

Casa Azul

Casa Azul

Casa Azul

Casa Azul

Casita1

Casita1

Casita2

Casita2

Casita3

Casita3


Rina Lazo

Venerable Abuelo Maiz

This palette is called Maiz.

Maiz

Maiz

Maiz

Maiz


Fanny Rabel

La Ronda del Tiempo

This palette is called Ronda.

Ronda

Ronda

Ronda

Ronda


Aurora Reyes

El atentado a las maestras rurales

This palette is called Atentado.

Aurora, Concha, y Frida

Aurora, Concha, y Frida

Aurora

Aurora

Aurora, Concha, y Frida

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

Aurora, Concha, y Frida

Aurora, Concha, y Frida

Aurora

Aurora

Concha

Concha

Frida

Frida


Remedios Varo

La Huida

This palette is called Huida.

La Huida

La Huida

Huida

Huida

Taurus

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

Taurus

Taurus

Taurus1

Taurus1

Taurus2

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")

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")