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}.
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") This palette is called Revolucion.
Revolucion
Revolucion
This palette is called Naturaleza.
Naturaleza
Naturaleza
This palette is called Ofrenda.
Ofrenda
Ofrenda
This palette is called Vendedora.
Vendedora
Vendedora
This palette is called Alacena.
Alacena
Alacena
This palette is called Tierra.
Tierra
Tierra
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
Casita1
Casita2
Casita3
This palette is called Maiz.
Maiz
Maiz
This palette is called Ronda.
Ronda
Ronda
This palette is called Atentado.
Aurora, Concha, y Frida
Aurora
This work of Aurora Rivera inspired three palettes, called Aurora,
Concha, and Frida.
Aurora, Concha, y Frida
Aurora
Concha
Frida
This palette is called Huida.
La Huida
Huida
This work of Remedios Varo inspired two palettes, called Taurus1 and
Taurus2.
Taurus
Taurus1
Taurus2
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 levelJoin 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()
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 uniquelyPlot 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")