|>
worldbankdata ::filter(Year == 2021) |>
dplyrggplot(aes(y = Cooking, x=Income, fill=Income)) +
geom_violin() +
scale_fill_brewer(palette = "Dark2") +
theme(axis.title.x=element_blank(),
axis.text.x=element_blank(),
axis.ticks.x=element_blank())
24 geom_v
24.1 geom_violin
Package
ggplot2 (Wickham 2016)
Description
Creates violin plot.
Understandable aesthetics
required aesthetics
x
y
optional aesthetics
alpha
, colour
, group
, linetype
, linewidth
, weight
See also
Example
24.2 geom_violin_pattern
Package
ggforce (Pedersen 2022)
Description
Fill violin plots with patterns.
Understandable aesthetics
required aesthetics
x
y
optional aesthetics
alpha
, colour
, group
, linetype
, linewidth
, weight
24.2.1 The statistical transformation to use on the data for this layer
ydensity
See also
Example
|>
worldbankdata ::filter(Year == 2021) |>
dplyrggplot(aes(y = Cooking, x=Income, fill=Income)) +
geom_violin_pattern() +
scale_fill_brewer(palette = "Dark2") +
theme(axis.title.x=element_blank(),
axis.text.x=element_blank(),
axis.ticks.x=element_blank())
24.3 geom_voronoi_tile
Package
ggforce (Pedersen 2022)
Description
Voronoi tiles are the polygons that result from the spatial division of a plane based on the input points.
Understandable aesthetics
required aesthetics
x
y
optional aesthetics
alpha
, colour
, linetype
, size
See also
geom_voronoi_segment, geom_delaunay_tile, geom_delaunay_segment
Example
library(ggforce)
<- worldbankdata |>
a1 filter(Income == "L") |>
filter(Region == "Middle East & North Africa" | Region == "Sub-Saharan Africa") |>
ggplot(aes(x=Cooking, y=Electricity)) +
geom_voronoi_tile(alpha=0.5, aes(fill=Region)) +
labs(title = "a1: geom_voronoi_tile only") +
scale_fill_brewer(palette = "Dark2") +
scale_color_brewer(palette = "Dark2") +
theme(aspect.ratio = 1) +
theme(legend.position='bottom')
<- worldbankdata |>
a2 filter(Income == "L") |>
filter(Region == "Middle East & North Africa" | Region == "Sub-Saharan Africa") |>
ggplot(aes(x=Cooking, y=Electricity)) +
geom_voronoi_tile(alpha=0.5, aes(fill=Region)) +
geom_point(aes(col=Region)) +
labs(title = "a1: geom_voronoi_tile and geom_point") +
scale_fill_brewer(palette = "Dark2") +
scale_color_brewer(palette = "Dark2") +
theme(aspect.ratio = 1) +
theme(legend.position='none')
|a2 a1
24.4 geom_voronoi_segment
Package
ggforce (Pedersen 2022)
Description
Represents the borders between the regions assigned to different input points.
Understandable aesthetics
required aesthetics
x
y
optional aesthetics
alpha
, colour
, linetype
, size
See also
geom_voronoi_tile, geom_delaunay_tile, geom_delaunay_segment
Example
library(ggforce)
library(deldir) #to calculate delaunay triangulation
<- worldbankdata |>
a1 filter(Year == 2021) |>
filter(Income == "LM" | Income == "UM") |>
ggplot(aes(x=Cooking, y=Electricity)) +
geom_voronoi_segment() +
labs(title = "a1: geom_voronoi_segment only") +
theme(aspect.ratio = 1)
<- worldbankdata |>
a2 filter(Year == 2021) |>
filter(Income == "LM" | Income == "UM") |>
ggplot(aes(x=Cooking, y=Electricity)) +
geom_point() +
geom_voronoi_segment() +
labs(title = "a2: geom_point and \n geom_voronoi_segment") +
theme(aspect.ratio = 1)
| a2 a1