Visualisation families
simplevis supports many visualisation family types.
library(dplyr)
library(simplevis)
library(palmerpenguins)
library(ggplot2)
library(patchwork)
set.seed(123456789)simplevis is a package of ggplot2 and leaflet wrapper functions that aims to make visualisation easier with less brainpower required.
simplevis supports many visualisation family types.
Each visualisation family generally has four functions.
This is based on whether or not a visualisation is to be:
*())*_col())*_facet())*_col_facet())The premise is that these are the most common types of visualisation for each of the families.
All arguments for variables are required unqutoted and follow an *_var format (i.e. x_var, y_var, col_var and facet_var)
For example, code below shows these combinations with code and output for the gg_point*() family.
gg_point_col_facet(penguins,
x_var = bill_length_mm,
y_var = body_mass_g,
col_var = sex,
facet_var = species)
simplevis plots are designed to work and look clean with the bare minimum of code.
However, there is plenty of flexibility to customise.
Change the colour palette by supplying a vector of hex code colours to the pal argument.
gg_point_col(penguins,
x_var = bill_length_mm,
y_var = body_mass_g,
col_var = species,
pal = c("#da3490", "#9089fa", "#47e26f"))
Numerous colour palettes are available from the pals package.
simplevis also supports easy colouring of numeric variables for gg_point*(), gg_bar*(), gg_hbar*(), gg_sf*() and gg_stars*() (and equivalent leaflet functions).
The col_method argument allows you to specify the method for colouring with a default of continuous.
gg_point_col(penguins,
x_var = bill_length_mm,
y_var = body_mass_g,
col_var = flipper_length_mm,
col_method = "continuous",
subtitle = "col_method = 'continuous'")
Other methods available are bin and quantile.
For bin and quantile, you can fine-tune using the col_breaks_n or col_cuts arguments.
You can also adjust the opacity of objects in the visualisation through the alpha_*() arguments.
Refer to the colour article for further information.
You can adjust the theme of any simplevis plot by providing a ggplot2 theme to the theme argument.
gg_point_col(penguins,
x_var = bill_length_mm,
y_var = body_mass_g,
col_var = species,
title = "A nice title",
subtitle = "And a subtitle",
theme = ggplot2::theme_grey())
You can also create your own quick themes with the gg_theme() function.
custom_theme <- gg_theme(
pal_body = "white",
pal_title = "white",
pal_subtitle = "white",
pal_background = c("#232323", "black"),
pal_gridlines = "black",
gridlines_h = TRUE,
gridlines_v = TRUE)
gg_point_col(penguins,
species,
x_var = bill_length_mm,
y_var = body_mass_g,
theme = custom_theme)
Refer to the themes article for further information.
There are lots of arguments available to modify the defaults.
In general, arguments have consistent prefixes based on x_*, y_*, col_* or facet_*, and as such the autocomplete can help identify what you need.
Some examples of transformations available are:
*_na_rm to quickly not include NA observations*_labels to adjust labels for any x, y, col or facet scale*_zero to start at zero for numeric x or y scales*_breaks_n for the number of numeric bins of breaks for the x, y or col scale to aim for*_rev to reverse the order of categorical x, y or col scales in bars*_expand to add padding to an x or y scale.*_balance to balance a numeric scale, so that zero is in the centrecol_legend_none to turn the legend off.plot_data <- storms %>%
group_by(year, status) %>%
summarise(wind = mean(wind))
gg_line_col(plot_data,
x_var = year,
y_var = wind,
col_var = status,
x_breaks_n = 10,
x_labels = function(x) stringr::str_sub(x, 3, 4),
y_labels = scales::label_dollar(accuracy = 1),
col_labels = c("H", "TD", "TS"),
y_zero = TRUE,
y_breaks_n = 10,
y_expand = ggplot2::expansion(add = c(0, 10)))