In j-effendy/dataviz:

knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  fig.path = "man/figures/README-",
  out.width = "100%"
)

ebviz

The goal of ebviz is to assist authors of a Multiple Regression Poststratification edited book in creating visualizations that are readable, cohesive, and accessible.

Installation

You can install the ebviz package from GitHub with:

# install.packages("devtools")
devtools::install_github("j-effendy/ebviz")

Example

These are basic plot examples of theme_seagull() and theme_wombat() using mtcars and economics_long dataset:

library(ebviz)
library(ggplot2)
library(patchwork)

w1 <- ggplot(mtcars, aes(mpg,disp,color=factor(carb))) + 
  geom_point(size=3) + 
  labs(title="Scatter Plot",
       subtitle = "theme_wombat()",
       caption = "Data: mtcars") +
  theme_wombat()

w2 <- ggplot(mtcars, aes(factor(carb),fill=factor(carb))) + 
  geom_bar() + 
  scale_y_continuous(expand = c(0,0)) + 
  scale_x_discrete(expand = c(0,0)) +
  labs(title="Bar Plot",
       subtitle = "theme_wombat()",
       caption = "Data: mtcars") +
  theme_wombat()

w3 <- ggplot(economics_long, aes(date, value01, colour = variable)) +
  geom_line() +
   scale_y_continuous(expand = c(0,0)) + 
   scale_x_date(expand = c(0,0)) +
  labs(title = "Line Plot",
       subtitle = "theme_wombat()",
       caption = "Data: economics_long") +
  theme_wombat()

s1 <- ggplot(mtcars, aes(mpg,disp,color=factor(carb))) + 
  geom_point(size=3) + 
  labs(title="Scatter Plot",
       subtitle = "theme_seagull()",
       caption = "Data: mtcars") +
  theme_seagull()

s2 <- ggplot(mtcars, aes(factor(carb),fill=factor(carb))) + 
  geom_bar() + 
  scale_y_continuous(expand = c(0,0)) + 
  scale_x_discrete(expand = c(0,0)) +
  labs(title="Bar Plot",
       subtitle = "theme_seagull()",
       caption = "Data: mtcars") +
  theme_seagull()

s3 <- ggplot(economics_long, aes(date, value01, colour = variable)) +
  geom_line() +
  scale_y_continuous(expand = c(0,0)) + 
  scale_x_date(expand = c(0,0)) +
  labs(title = "Line Plot",
       subtitle = "theme_seagull()",
       caption = "Data: economics_long") +
  theme_seagull()

(s1 + w1) / (s2 + w2) / (s3 + w3)

These are basic plot examples of ebviz colour palettes using mtcars dataset:

library(dataviz)
library(ggplot2)
library(patchwork)

p1 <- ggplot(mtcars, aes(mpg,disp,color=factor(vs))) +
        geom_point(size=3) +
        scale_colour_dataviz("quokka") +
        labs(title="Scatter Plot",
             subtitle = "Quokka Palette",
             caption = "Data: mtcars") +
        theme_wombat()

p2 <- ggplot(mtcars, aes(mpg,disp,color=factor(vs))) +
        geom_point(size=3) +
        scale_colour_dataviz("quoll") +
        labs(title="Scatter Plot",
               subtitle = "Quoll Palette",
               caption = "Data: mtcars") +
        theme_wombat()

p3 <- ggplot(mtcars, aes(mpg,disp,color=mpg)) +
        geom_point(size=3) +
        geom_point(size=3) +
        scale_colour_dataviz("seal") +
        labs(title="Scatter Plot",
                 subtitle = "Seal Palette",
                 caption = "Data: mtcars") +
        theme_wombat()

p4 <- ggplot(mtcars, aes(mpg,disp,color=mpg)) +
        geom_point(size=3) +
        geom_point(size=3) +
        scale_colour_dataviz("snake") +
        labs(title="Scatter Plot",
                 subtitle = "Snake Palette",
                 caption = "Data: mtcars") +
        theme_wombat()

p5 <- ggplot(mtcars, aes(mpg,disp,color=mpg)) +
        geom_point(size=3) +
        scale_colour_dataviz("dingo") +
        labs(title="Scatter Plot",
                 subtitle = "Dingo Palette",
                 caption = "Data: mtcars") +
        theme_wombat()

p6 <- ggplot(mtcars, aes(mpg,disp,color=mpg)) +
        geom_point(size=3) +
        scale_colour_dataviz("dolphin") +
        labs(title="Scatter Plot",
                 subtitle = "Dolphin Palette",
                 caption = "Data: mtcars") +
        theme_wombat()

(p1 + p2) / (p3 + p4) / (p5 + p6)