Easy themes for your plots

Configuration

Before using this package you’ll want to configure color codes and palette combinations. This is accomplished on the R/config.yml file using standard yaml formatting (including a trailing newline). This needs to be performed the package is installed. Required components are:

1. colors: define hex codes that correspond to specific hex color codes. If you’re looking for inspiration there are plenty of tools to help. These will provide both the basis for palettes you define but also be available to use manually.
2. palettes: named collections of colors defined in the colors section. We minimally require a main palette and support as many more as desired.

colors:
  orange: "#F58220"
  lime: "#7AC143"
  brown: "#591F00"
  gold: "#ffd051"
  sand: "#f7a964"  
  green: "#508d2a"
  red: "#b84000"
  cream: "#b84000"
palettes:
  all:
    - orange
    - lime
    - brown
    - green
    - gold
    - red
  main:
    - orange
    - lime  
  light:
    - orange
    - gold

Quick start

These examples demonstrate usage of two functions (scales) that can be used to modify color schemes of a ggplot. Common params for these include:

• palette Name of a configured palette of colors
• reverse Boolean if you want to reverse the order of configured colors
• discrete Boolean indicating whether color aesthetic is discrete. Alternatively it will create a scale appropriate for continuous values.

library(EasyBranding)
library(ggplot2)
library(ggprism)

theme_set(theme_prism() + theme(aspect.ratio = 0.4))
p <- ggplot(iris, aes(x=Sepal.Length, y=Sepal.Width, color = Species)) +
  geom_point(size = 2) +
  geom_smooth(formula = 'y ~ x', method = 'glm', se = F)

p + scale_color_brand() + 
  labs(title='main palette')

p + scale_color_brand(palette = 'dark') + 
  labs(title='dark palette')

p + scale_color_brand(reverse = T) +
  labs(title='reverse main palette')

In this series we demonstrate how palettes are created when datasets require more colors than those defined in a palette. This is typically accomplished by interpolating intermediate values, therefore when a palette is defined by 2 values the colors are all intermediate those two, then more are specified then colors are interpolated along those specifications.

df <- data.frame(category = LETTERS[1:20],
                 value = runif(20)+1)
p <- ggplot(df, aes(category, value, fill = category)) +
  geom_col()
p + scale_fill_brand() + 
  labs(title='main palette') +
  theme(legend.position = 'none')

p + scale_fill_brand(palette = "all") + 
  labs(title='*all* palette') +
  theme(legend.position = 'none')