wetherspoons

It’s a hot summer’s day and you’re sipping on your 2-for-£12 pitchers from your local spoons (https://www.jdwetherspoon.com/ for you uncultured swines). ‘Wow, what a pretty cocktail I’m drinking’, you think to yourself. ‘If only I could use these colours on my next coursework assignment’. Well fear not…

This package provides palettes inspired by the cocktails of JD Wetherspoon (and by the amazing MetBrewer).

Feel free to contact me with comments or suggestions:
LinkedIn: @dougal-toms
Twitter: @DougalToms

Installation

wetherspoons can be installed through GitHub

R

install.packages("devtools")
devtools::install_github("dougaltoms/wetherspoons")

Python

pip install git+https://github.com/dougaltoms/wetherspoons.git

Palettes

An overview of available cocktail palettes can be had with the WhatsOnTheMenu() function:

library(wetherspoons)

wetherspoons::WhatsOnTheMenu()

Once you’ve chosen your cocktail, you can generate colour values by ordering a pitcher of, using the pitcher.of() function:

wetherspoons::pitcher.of("BlueLagoon")

## [1] "#164D83" "#2A81CA" "#3CA2E1" "#55CCE3" "#80BCAE" "#F2E554"

Examples

Rayshader

Plotting 2D hillshaded maps using rayshader and wetherspoons

library(rayshader)

palette <- wetherspoons::pitcher.of("TuttiFrutti", n=250, direction=1)

montereybay %>%
    height_shade(texture=palette) %>%
    add_overlay(sphere_shade(montereybay, zscale=4), alphalayer=.5) %>%
    plot_map()

ggplot2

wetherspoons provides relevant scales for use with ggplot2. The following examples use the commonly used iris data set.

library(ggplot2)

ggplot(iris, aes(x = Petal.Length, fill = Species)) +
  geom_density(alpha = 0.8)+
  scale_fill_manual(values=pitcher.of("BlueLagoon",3))+
  labs(x="Petal length", y="Density",
       title="How petal length varies across Iris species",
       subtitle="Coloured using wetherspoons")+
  theme_minimal()

library(ggplot2)

ggplot(data=iris, aes(x=Sepal.Length, y=Sepal.Width, color=Species)) +
  geom_point(size=2) +
  scale_color_manual(values=pitcher.of("SexOnTheBeach",3))+
  labs(x="Length", y="Width",
       title="Length of Iris sepals plotted against their width",
       subtitle = "Coloured using wetherspoons")+
  theme_minimal()

library(ggplot2)

ggplot(data=iris, aes(x=Species, y=Petal.Length, fill=Species)) +
  geom_violin() +
  scale_fill_manual(values=pitcher.of("PurpleRain",n=3, direction=1))+
  labs(title="Petal length by Iris Species", y="Petal length",
       subtitle="Coloured using wetherspoons")+
  theme_minimal()

matplotlib

wetherspoons provides matplotlib functionality with the ability to create custom cmaps

import matplotlib.pyplot as plt
import numpy as np

# Create wetherspoons cmap
cmap = wetherspoons.pitcher_of("BlueLagoon", direction=1, type="continuous")

# Create data
X, Y = np.meshgrid(np.linspace(-3, 3, 16), np.linspace(-3, 3, 16))
Z = (1 - X/2 + X**5 + Y**3) * np.exp(-X**2 - Y**2)

# Plot
fig, ax = plt.subplots()
ax.imshow(Z, cmap=cmap)

plt.show()