2 min read

simplevis: visualisation made easier

Introduction

simplevis is a package of ggplot2 wrapper functions that aims to make beautiful ggplot2 visualisation with less brainpower and typing!

This blog will provide an overview of:

  • the visualisation family types that simplevis currently supports
  • how visualisation families support combinations of colouring (by a variable), facetting. both or neither.
library(simplevis)
library(dplyr)
library(palmerpenguins)

Visualisation family types

bar

plot_data <- storms %>%
  group_by(year) %>%
  summarise(wind = mean(wind))

gg_bar(plot_data, year, wind)

point

gg_point(iris, Sepal.Width, Sepal.Length)

line

plot_data <- storms %>%
  group_by(year) %>%
  summarise(wind = mean(wind))

gg_line(plot_data, year, wind)

boxplot

gg_boxplot(storms, year, wind)

hbar (i.e horizontal bar)

plot_data <- ggplot2::diamonds %>%
  group_by(cut) %>%
  summarise(price = mean(price))

gg_hbar(plot_data, price, cut)

sf (short for simple features map)

gg_sf(example_sf_point, borders = nz)

Colouring, facetting, neither or both

Each visualisation family generally has 4 functions.

The function name specifies whether or not a visualisation is to be coloured by a variable *_col(), facetted by a variable *_facet(), neither *() or both of these *_col_facet().

Colouring by a variable means that different values of a selected variable are to have different colours. Facetting means that different values of a selected variable are to have their facet.

A *() function such gg_point() requires only a dataset, an x variable and a y variable.

gg_point(penguins, bill_length_mm, body_mass_g)

A *_col() function such gg_point_col() requires only a dataset, an x variable, a y variable, and a colour variable.

gg_point_col(penguins, bill_length_mm, body_mass_g, sex)

A *_facet() function such gg_point_facet() requires only a dataset, an x variable, a y variable, and a facet variable.

gg_point_facet(penguins, bill_length_mm, body_mass_g, species)

A *_col_facet() function such gg_point_col_facet() requires only a dataset, an x variable, a y variable, a colour variable, and a facet variable.

gg_point_col_facet(penguins, bill_length_mm, body_mass_g, sex, species)

Data is generally plotted with a stat of identity, which means data is plotted as is. Only for boxplot, there is a different default stat of boxplot, which means data will be transformed to boxplot statistics.

Further information

For further information, see the vignette and articles on the simplevis website.