worldbankdata |>
ggplot(aes(x = Electricity)) + geom_density()
6 geom_d
6.1 geom_density
Package
ggplot2 (Wickham 2016)
Description
Computes and draws kernel density estimation.
Understandable aesthetics
required aesthetics
x, y
optional aesthetics
alpha, colour, fill, group, linetype, linewidth, weight
See also
Example
6.2 geom_density_line
Package
ggridges (Wilke 2023)
Description
Draws a density plot same as geom_density. The difference is that the geom draws a ridgeline (line with filled area underneath).
Understandable aesthetics
required aesthetics
x
y
optional aesthetics
alpha, colour, fill, group, linetype, linewidth, weight
See also
Example
library(ggridges)
worldbankdata |>
ggplot(aes(x = Electricity)) +
geom_density_line()
6.3 geom_density_2d
Package
ggplot2 (Wickham 2016)
Description
Computes a 2D kernel density estimation using MASS::kde2d() and display the results with contours.
Understandable aesthetics
stat_density
required aesthetics
x
y
optional aesthetics
alpha, colour, group, linetype, linewidth
See also
Example
a1 <- worldbankdata |>
ggplot(aes(y = Cooking, x=Electricity)) +
geom_density_2d() +
xlim(0, 100) +
ylim(0, 100) +
theme(aspect.ratio = 1) +
labs(title = "a1: geom_density_2d only")
a2 <- worldbankdata |>
ggplot(aes(y = Cooking, x=Electricity)) +
geom_point() +
geom_density_2d() +
theme(aspect.ratio = 1) +
labs(title = "a2: geom_point and \n geom_density_2d")
a1|a2
6.4 geom_density_2d_filled
Package
ggplot2 (Wickham 2016)
Description
Computes a 2D kernel density estimation using MASS::kde2d() and display the results with filled contour bands.
Understandable aesthetics
required aesthetics
x
y
optional aesthetics
alpha, colour, group, linetype, linewidth, subgroup
See also
Example
a1 <- worldbankdata |>
filter(Year == "2021") |>
ggplot(aes(y = Cooking, x=Electricity)) +
geom_density_2d_filled() +
labs(title = "a1: geom_density_2d_filled only") +
theme( aspect.ratio = 1)
a2 <- worldbankdata |>
filter(Year == "2020") |>
ggplot(aes(y = Cooking, x=Electricity)) +
geom_density_2d_filled(alpha = 0.5) +
geom_point() +
labs(title = "a2: geom_point and \n geom_density_2d_filled") +
theme( aspect.ratio = 1)
a3 <- worldbankdata |>
filter(Year == "2020") |>
ggplot(aes(y = Cooking, x=Electricity)) +
geom_point(alpha=0.5) +
labs(title = "a3: geom_point") +
theme(aspect.ratio = 1)
a1 / a2 / a3
6.5 geom_density_ridges
Package
ggridges (Wilke 2023)
Description
Arranges multiple density plots in a staggered fashion.
Understandable aesthetics
required aesthetics
x, y
optional aesthetics
colour, fill, group, height, alpha, linetype, linewidth, scale, rel_min_height
See also
Example
library(ggridges)
worldbankdata |>
ggplot(aes(y = Income, x=Electricity)) +
geom_density_ridges() 
6.6 geom_density_ridges_gradient
Package
ggridges (Wilke 2023)
Description
Arranges multiple density plots in a staggered fashion.
Understandable aesthetics
required aesthetics
x, y
optional aesthetics
colour, fill, group, height, alpha, linetype, linewidth, scale, rel_min_height
See also
Example
library(ggridges)
worldbankdata |>
ggplot(aes(y = Income, x=Electricity, fill=stat(x))) +
geom_density_ridges_gradient() +
scale_fill_viridis_c()
6.7 geom_dl
Package
directlabels (Hocking 2023)
Description
Display direct labels on the plot.
Understandable aesthetics
layer
See also
Example
library(directlabels)
a1 <- worldbankdata |>
ggplot(aes(y = Cooking, x=Electricity)) +
geom_point(aes(col=Income)) +
theme(aspect.ratio = 1) +
scale_color_brewer(palette = "Set1")
a1 +
geom_dl(aes(label=Income), method="smart.grid")+
scale_shape_manual(values=c(H = 1,
UM = 6,
L = 3,
LM = 2),
guide="none")
6.8 geom_dotplot
Package
ggplot2 (Wickham 2016)
Description
Create dotplot.
6.8.1 Understandable aesthetics
required aesthetics
x or y
optional aesthetics
alpha, colour, fill , group, linetype, stroke
See also
Example
worldbankdata |>
ggplot(aes(x=Cooking)) +
geom_dotplot(binwidth = 1) +
theme(legend.position="none", aspect.ratio = 1)
6.9 geom_delaunay_tile
Package
ggforce (Pedersen 2022)
Description
Display voronoi tesselation and delaunay triangulation.
Understandable aesthetics
required aesthetics
x or y
optional aesthetics
alpha, colour, fill , linetype, size
See also
Example
library(ggforce)
library(deldir) #to calculate delaunay triangulation
a1 <- worldbankdata |>
filter(Income == "L") |>
ggplot(aes(x=Cooking, y=Electricity)) +
geom_delaunay_tile(alpha=0.5) +
labs(title = "a1: geom_delaunay_tile only") +
theme(aspect.ratio = 1)
a2 <- worldbankdata |>
filter(Income == "L") |>
ggplot(aes(x=Cooking, y=Electricity)) +
geom_point() +
geom_delaunay_tile(alpha=0.5) +
labs(title = "a2: geom_point and \n geom_delaunay_tile") +
theme(aspect.ratio = 1)
a1 | a2
6.10 geom_delaunay_segment
Package
ggforce (Pedersen 2022)
Description
Display voronoi tesselation and delaunay triangulation.
Understandable aesthetics
required aesthetics
x or y
optional aesthetics
alpha, colour, fill , linetype, size
See also
Example
library(ggforce)
library(deldir) #to calculate delaunay triangulation
a1 <- worldbankdata |>
filter(Income == "L") |>
ggplot(aes(x=Cooking, y=Electricity)) +
geom_delaunay_segment() +
theme(aspect.ratio = 1) +
labs(title = "a1: geom_delaunay_segment only")
a2 <- worldbankdata |>
filter(Income == "L") |>
ggplot(aes(x=Cooking, y=Electricity)) +
geom_point() +
geom_delaunay_segment() +
theme(aspect.ratio = 1) +
labs(title = "a2: geom_point and \n geom_delaunay_segment")
a1 | a2
6.11 geom_delaunay_segment2
Package
ggforce (Pedersen 2022)
Description
Display voronoi tesselation and delaunay triangulation.
Understandable aesthetics
required aesthetics
x or y
optional aesthetics
alpha, colour, fill , linetype, size
See also
Example
library(ggforce)
library(deldir) #to calculate delaunay triangulation
a1 <- worldbankdata |>
filter(Income == "L") |>
ggplot(aes(x=Cooking, y=Electricity)) +
geom_delaunay_segment2() +
theme(aspect.ratio = 1) +
labs(title = "a1: geom_delaunay_segment2 only")
a2 <- worldbankdata |>
filter(Income == "L") |>
ggplot(aes(x=Cooking, y=Electricity)) +
geom_point() +
geom_delaunay_segment2() +
theme(aspect.ratio = 1) +
labs(title = "a2: geom_point and \n geom_delaunay_segment2")
a1 | a2
6.12 geom_dumbbell
Package
ggalt(Rudis, Bolker, and Schulz 2017)
Description
Create dumbbell charts.
Understandable aesthetics
required aesthetics
x, y, xend, yend
optional aesthetics
alpha, colour, group, linetype, size
See also
Example
library(ggalt)
df <- worldbankdata |>
group_by(Income) |>
summarise(min = min(Electricity, na.rm=TRUE), max = max(Electricity, na.rm=TRUE))
df# A tibble: 5 × 3
Income min max
<fct> <dbl> <dbl>
1 L 2.11 99.8
2 LM 3.81 100
3 UM 26.5 100
4 H 65.9 100
5 <NA> 17.8 100 ggplot(df, aes(y=Income, x=min, xend=max)) +
xlab("Electricity Range") +
geom_dumbbell(color = "darkgray", # Color of the line between min and max
size = 3, # Line width
dot_guide = FALSE, # Whether to add a guide from origin to X or not
size_x = 3, # Size of the X point
size_xend = 3, # Size of the X end point
colour_x = "#762a83", # Color of the X point
colour_xend = "#1b7837") # Color of the X end point 
Hocking, Toby Dylan. 2023. Directlabels: Direct Labels for Multicolor Plots. https://CRAN.R-project.org/package=directlabels.
Pedersen, Thomas Lin. 2022. Ggforce: Accelerating ’Ggplot2’. https://CRAN.R-project.org/package=ggforce.
Rudis, Bob, Ben Bolker, and Jan Schulz. 2017. Ggalt: Extra Coordinate Systems, ’Geoms’, Statistical Transformations, Scales and Fonts for ’Ggplot2’. https://CRAN.R-project.org/package=ggalt.
Wickham, Hadley. 2016. Ggplot2: Elegant Graphics for Data Analysis. Springer-Verlag New York. https://ggplot2.tidyverse.org.
Wilke, Claus O. 2023. Ggridges: Ridgeline Plots in ’Ggplot2’. https://CRAN.R-project.org/package=ggridges.