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

More blogs to come on simplevis methods for adjusting colours, titles and scales, filtering out NA values, and working with ggplotly and leaflet. In the meantime, see the vignette and articles on the simplevis website.