Package
ggplot2 (Wickham 2016)
Description
Draw a verticle line at the specified intercept.
Understandable aesthetics
required aesthetics
xintercept
optional aesthetics
alpha, colour, group, linetype, linewidth
See also
Example
a1 <- ggplot(worldbankdata, aes(y = Cooking, x = Electricity)) +
geom_vline(xintercept = 50) +
labs(title = "a1: geom_vline only") +
theme(aspect.ratio = 1)
a2 <- ggplot(worldbankdata, aes(y = Cooking, x = Electricity)) +
geom_vline(xintercept = 50) +
geom_point() +
labs(title = "a2: geom_vline \n and geom_point") +
theme(aspect.ratio = 1)
a1 | a2
Package
ggridges (Wilke 2023)
Description
Plots the sum of the x and width aesthetics versus y, filling the area between x and x + width with a color.
Understandable aesthetics
required aesthetics
x
y
optional aesthetics
width, group, scale, min_width, color, fill, alpha, linetype, linewidth
See also
Example
library(ggridges)
worldbankdata |>
filter(Income %in% c("L", "UM", "H")) |>
ggplot(aes(x = Income, y = Cooking,
width = after_stat(density),
fill = Income)) +
geom_vridgeline(stat = "ydensity", trim = FALSE, alpha = 0.85, scale = 2) +
labs(title = "Ridgeline of Cooking Fuel by Income Group")
Package
ggvoronoi (Garrett, Nar, and Fisher 2024)
Description
A Voronoi diagram is a way to divide space based on proximity to a set of points. Think of it as creating territories around each point, where every location in a territory is closer to that point than any other.
Understandable aesthetics
required aesthetics
x
y
optional aesthetics
width, group, scale, min_width, color, fill, alpha, linetype, linewidth
See also
Example
#remotes::install_github("garretrc/ggvoronoi", dependencies = TRUE, build_opts = c("--no-resave-data"))
library(ggplot2)
library(ggvoronoi)
# 1. Create a data frame with province names and centroid coordinates
province_points <- data.frame(
Province = c("Western", "Central", "Southern", "Northern",
"Eastern", "North Western", "North Central",
"Uva", "Sabaragamuwa"),
lon = c(79.95, 80.63, 80.22, 80.38, 81.05, 79.95, 80.65, 81.0, 80.35),
lat = c(6.90, 7.30, 6.05, 9.65, 7.85, 7.75, 8.35, 6.85, 6.75)
)
# 2. Define a bounding box outline (roughly Sri Lanka coordinates)
outline <- data.frame(
x = c(79.6, 82.0, 82.0, 79.6),
y = c(5.8, 5.8, 10.0, 10.0)
)
# 3. Plot Voronoi diagram
ggplot(province_points, aes(x = lon, y = lat)) +
geom_voronoi(aes(fill = Province), outline = outline) +
geom_point(color = "black", size = 2) +
geom_text(aes(label = Province), size = 3, vjust = -1) +
coord_equal() +
labs(title = "Voronoi Diagram of Sri Lanka Provinces") 
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
worldbankdata |>
dplyr::filter(Year == 2021) |>
ggplot(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())
Package
ggforce (Pedersen 2022)
Description
Fill violin plots with patterns.
Understandable aesthetics
required aesthetics
x
y
optional aesthetics
alpha, colour, group, linetype, linewidth, weight
ydensity
See also
Example
worldbankdata |>
dplyr::filter(Year == 2021) |>
ggplot(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())
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)
a1 <- worldbankdata |>
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')
a2 <- worldbankdata |>
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')
a1|a2
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
a1 <- worldbankdata |>
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)
a2 <- worldbankdata |>
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)
a1 | a2