worldbankdata |>
filter(Year == 2021) |>
group_by(Income) |>
summarise(n = n()) |>
ggplot(aes(x = Income, y = n)) + geom_col()
5 geom_c
5.1 geom_col
Package
ggplot2 (Wickham 2016)
Description
Create bar charts
Understandable aesthetics
Required aesthetics
x, y
Optional aesthetics
alpha, colour, fill, group, linetype, linewidth
See also
Example
5.2 geom_col_pattern
Package
ggpattern (FC, Davis, and ggplot2 authors 2023)
Description
Fill columns with a pattern. User can map a variable for pattern or set a pattern.
Understandable aesthetics
Required aesthetics
x, y
Optional aesthetics
pattern, fill, colour
See also
Example
worldbankdata |>
filter(Year == 2021) |>
group_by(Income) |>
summarise(n = n()) |>
ggplot(aes(x = Income, y = n)) +
ggpattern::geom_col_pattern(aes(pattern = n, pattern_angle=n),
colour = 'black', fill="white") 
5.3 geom_count
Package
ggplo2t (Wickham 2016)
Description
Counts the observations at every point on the plot, and then maps the count with the size of the point.
Understandable aesthetics
Required aesthetics
x, y
Optional aesthetics
alpha, colour, fill, group, shape, size, stroke
See also
Example
Here, both geom_point and geom_count are plotted to see the difference.
a1 <- ggplot(worldbankdata, aes(y = Cooking, x=Electricity)) +
geom_point(alpha = 0.5) +
labs(title = "a1: geom_point") +
theme(aspect.ratio = 1)
a2 <- ggplot(worldbankdata, aes(y = Cooking, x=Electricity)) +
geom_count() +
labs(title = "a2: geom_count") +
theme(aspect.ratio = 1)
a1 | a2
5.4 geom_circle
Package
ggforce (Pedersen 2022)
Description
Draw circles based on a center point and a radius.
Understandable aesthetics
required aesthetics
x0 - starting coordinate of x-axis , y0 - starting coordinate of x-axis, r - radius
optional aesthetics
color, fill, linewidth, linetype, alpha, lineend
See also
Example
worldbankdata |>
filter(Year == 2021) |>
ggplot(aes(y = Cooking, x=Electricity, col=Income)) +
geom_point() +
scale_color_brewer(palette = "Dark2") +
ggforce::geom_circle(aes(x0 = 26, y0 = 5, r = 20),
inherit.aes = FALSE) +
theme(aspect.ratio = 1)
5.5 geom_contour
Package
ggplot2 (Wickham 2016)
Description
Create contour plots.
Understandable aesthetics
Required aesthetics
x, y
Optional aesthetics
alpha, colour, fill , group, linetype, linewidth, subgroup
See also
geom_contour_filled, geom_tile, geom_density_2d
Example
mean <- c(0.5, -0.5)
sigma <- matrix(c(1, 0.5, 0.5, 1), nrow=2)
data.grid <- expand.grid(x=seq(-3, 3, length.out=200),
y=seq(-3, 3, length.out=200))
df <- cbind(data.grid, prob = mvtnorm::dmvnorm(data.grid, mean=mean, sigma=sigma))
ggplot(df, aes(x=x, y=y, z=prob)) +
geom_contour() +
theme(aspect.ratio = 1)
5.6 geom_contour_filled
Package
ggplot2 (Wickham 2016)
Description
Create contour plots
Understandable aesthetics
x, y, alpha, colour, linetype, linewidth, group, weight
See also
geom_contour, geom_tile, geom_density_2d
Example
mean <- c(0.5, -0.5)
sigma <- matrix(c(1, 0.5, 0.5, 1), nrow=2)
data.grid <- expand.grid(x=seq(-3, 3, length.out=200),
y=seq(-3, 3, length.out=200))
df <- cbind(data.grid, prob = mvtnorm::dmvnorm(data.grid, mean=mean, sigma=sigma))
ggplot(df, aes(x=x, y=y, z=prob)) +
geom_contour_filled() +
theme(aspect.ratio = 1)
5.7 geom_curve
Package
ggplot2 (Wickham 2016)
Description
geom_segment() draws a straight line between between two points. geom_curve draws a curved line.
Understandable aesthetics
required aesthetics
x, y
optional aesthetics
alpha, colour, linetype, linewidth, group
The statistical transformation to use on the data for this layer
identity
See also
Examples
df <- data.frame(x1 = 0, x2 = 100, y1 = 0, y2 = 100)
ggplot(df) +
geom_curve(aes(x = x1, y = y1, xend = x2, yend = y2))
df <- data.frame(x2 = c( 3, 4, 4, 3, -3, -4, -4, -3),
y2 = c( 4, 3, -3, -4, -4, -3, 3, 3),
x1 = rep(0, 8),
y1 = rep(0, 8))
ggplot(df) +
geom_curve(aes(x = x1, y = y1, xend = x2, yend = y2),
curvature = 0.75, angle = -45,
arrow = arrow(length = unit(0.25,"cm"))) +
coord_equal() +
xlim(-5, 5) + ylim(-5, 5)
5.8 geom_crossbar
Package
ggplot2 (Wickham 2016)
Description
Plot a vertical interval defined by y, ymin and ymax or x, xmin and xmax.
Understandable aesthetics
required aesthetics
x or y
xmin or ymin
xmax or ymax
optional aesthetics
alpha, colour, linetype, linewidth, group
See also
Examples
Example 1
summarydf <- worldbankdata |>
drop_na() |>
select(Electricity, Income) |>
group_by(Income) |>
reframe(qs = quantile(Electricity, c(0.25, 0.5 ,0.75))) |>
mutate(q=rep(c("Q1", "Q2", "Q3"), 4)) |>
pivot_wider(names_from = q,
values_from = qs)
summarydf# A tibble: 4 × 4
Income Q1 Q2 Q3
<fct> <dbl> <dbl> <dbl>
1 L 16.6 32.2 50.7
2 LM 62.0 86.7 97.7
3 UM 95.9 99.5 100.
4 H 100 100 100 ggplot(summarydf, aes(x=Income, ymin = Q1, y=Q2, ymax = Q3)) +
geom_crossbar(size=1,col="red", width = .5)
Example 2
summary_stats <- worldbankdata |>
drop_na() |>
select(Electricity, Income) |>
group_by(Income) |>
reframe(mean = mean(Electricity),
sd = sd(Electricity))
ggplot(summary_stats, aes(x = Income, y = mean, ymin = mean - sd, ymax = mean + sd)) +
geom_crossbar(width = 0.5, fatten = 2) 