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
- how visualisation families support combinations of colouring (by a variable), facetting. both or neither.
library(simplevis) library(dplyr) library(palmerpenguins)
Visualisation family types
plot_data <- storms %>% group_by(year) %>% summarise(wind = mean(wind)) gg_bar(plot_data, year, wind)
gg_point(iris, Sepal.Width, Sepal.Length)
plot_data <- storms %>% group_by(year) %>% summarise(wind = mean(wind)) gg_line(plot_data, year, wind)
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
*() or both of these
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.
*() function such
gg_point() requires only a dataset, an x variable and a y variable.
gg_point(penguins, bill_length_mm, body_mass_g)
*_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)
*_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)
*_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.
More blogs to come on
simplevis methods for adjusting colours, titles and scales, filtering out NA values, and working with
leaflet. In the meantime, see the vignette and articles on the simplevis website.