Package
ggplot2 (Wickham 2016)
Description
Description Draw a straight line (\(Y=mX+c\)) for a given slope (\(m\)) and intercept (\(c\)).
Understandable aesthetics
Unlike most other geoms, geom_abline does not depend on the x and y variables that we map for the main plot. geom_abline has its own independent characteristics: intercept and slope.
See also
geom_point, geom_vline, geom_hline
Example
a1 <- ggplot() +
geom_abline(intercept = 0, slope = 1) +
labs(title = "a1: geom_abline only") +
theme(aspect.ratio = 1)
a2 <- ggplot(happiness_gdp_income, aes(y =HappinessScore2016, x = HappinessScore2015)) +
geom_abline(intercept = 0, slope = 1) +
geom_point() +
labs(title = "a2: geom_abline \n and geom_point") +
theme(aspect.ratio = 1)
a1 | a2
Package
ggalluvial(Brunson and Read 2019; Brunson 2020)
Description
Create alluvial plot. An alluvial plot is a type of diagram that is particularly useful for visualizing categorical data and the flow or transition between different categorical variables over multiple stages or categories.
Understandable aesthetics
required aesthetics
Option 1 (See Example 1)
axis1 - First axis
axis2 - Second axis
y - Counts to determines the height/weight of the alluvium
Option 2 (alluvium time-series plot, see Example 2)
x - Time variable determining the horizontal axis
y - Determines the height/size of the flow
alluvium - According to this variable it defines the flow across the years. In our Example 2, Bangladesh and Sri Lanka.
optional aesthetics
alpha, colour, fill, linetype, size, group (group is used internally; arguments are ignored)
See also
geom_stratum, geom_flow, geom_line geom_lode
Example 1
This example shows the distribution of income level categories in 2021 and 2022 in the regions Sub-Saharan Africa and Latin America & Caribbean.
library(ggalluvial)
freq.table <- worldbankdata |>
select(Country, Region, Year, Income) |>
filter(Year > 2020) |>
filter(Region %in% c("Sub-Saharan Africa", "Latin America & Caribbean")) |>
group_by(Region, Year, Income) |>
summarise(n = n()) |>
drop_na()
freq.table# A tibble: 14 × 4
# Groups: Region, Year [4]
Region Year Income n
<fct> <dbl> <fct> <int>
1 Latin America & Caribbean 2021 LM 5
2 Latin America & Caribbean 2021 UM 19
3 Latin America & Caribbean 2021 H 17
4 Latin America & Caribbean 2022 LM 4
5 Latin America & Caribbean 2022 UM 19
6 Latin America & Caribbean 2022 H 18
7 Sub-Saharan Africa 2021 L 24
8 Sub-Saharan Africa 2021 LM 16
9 Sub-Saharan Africa 2021 UM 6
10 Sub-Saharan Africa 2021 H 1
11 Sub-Saharan Africa 2022 L 22
12 Sub-Saharan Africa 2022 LM 18
13 Sub-Saharan Africa 2022 UM 6
14 Sub-Saharan Africa 2022 H 1a1 <- freq.table |>
ggplot(aes(y = n, axis1 = Income, axis2 = Region)) +
geom_alluvium(aes(fill = as.factor(Year)), width = 1 / 12) +
scale_fill_brewer(palette = "Dark2") +
labs(title = "a1: geom_alluvium only")
a2 <- freq.table |>
ggplot(aes(y = n, axis1 = Income, axis2 = Region)) +
geom_alluvium(aes(fill = as.factor(Year)), width = 1 / 12) +
geom_stratum(width = 1 / 12, fill = "black", color = "grey") +
geom_label(stat = "stratum", aes(label = after_stat(stratum))) + scale_fill_brewer(palette = "Dark2") +
labs(title = "a2: geom_alluvium, \n geom_stratum and geom_label")
a1 / a2
Example 2
p1 <- worldbankdata |>
filter(Country %in% c("Bangladesh", "Sri Lanka"),
Year >= 2013, Year <= 2021) |>
ggplot(aes(x = Year,
y = Electricity,
alluvium = Country,
stratum = Country,
fill = Country)) +
geom_alluvium(alpha = 0.6) +
#geom_stratum(width = 0.4, color = "black") +
scale_x_continuous(breaks = 2013:2021) +
theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust = 1)) +
labs(title = "a1: geom_alluvium only",
x = "Year",
y = "Electricity",
fill = "Country")
p2 <- worldbankdata |>
filter(Country %in% c("Bangladesh", "Sri Lanka"),
Year >= 2013, Year <= 2021) |>
ggplot(aes(x = Year,
y = Electricity,
alluvium = Country,
stratum = Country,
fill = Country)) +
geom_alluvium(alpha = 0.6) +
geom_stratum(width = 0.4, color = "black") +
scale_x_continuous(breaks = 2013:2021) +
theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust = 1)) +
labs(title = "a2: geom_alluvium and geom_stratum",
x = "Year",
y = "Electricity",
fill = "Country")
p1/p2
Package
ggforce(Pedersen 2022)
Description
Draw a circle or a segment of a circle.
Understandable aesthetic
required aesthetics
x0 - x coordinate of the center of the circle
y0 - y coordinate of the center of the circle
r - radius,
start - starting point of the circumference
end - end point of the circumference
optional aesthetics
color, linewidth, linetype, alpha, lineend
The statistical transformation to use on the data for this layer
stat_arc
See also
Example
In the example below, geom_arc() is applied twice to show how the black arc forms the full circle, while the red arc highlights a specific portion of that circle.
library(ggforce)
ggplot() +
geom_arc(aes(x0 = 0, y0 = 0, r = 8, start = 1, end = 8)) +
geom_arc(aes(x0 = 0, y0 = 0, r = 8, start = 1, end = 5), col = "red", size = 2) +
theme(aspect.ratio = 1)
Package
ggforce(Pedersen 2022)
Description
To draw pie chart and donut chart defining centre point, a radius and a start and end angle.
Understandable aesthetic
required aesthetics
x0 - x-coordinate of the center of the pie chart
y0 - y coordinate of the center of the circle
r0 - Inner radius of the arc bar. If it is 0, the chart becomes a pie chart (no hole)
r - Outer radius of the pie chart slices
amount - Determines the size (angle) of each slice. Larger counts produce larger slices
fill - Fill colour for each slice
exploid - Moves selected slices slightly outward from the center to highlight them
optional aesthetics
color, linewidth, linetype, alpha, lineend
The statistical transformation to use on the data for this layer
stat_arc
See also
Example
# Count observations in each Income category
df <- worldbankdata |>
filter(!is.na(Region)) |>
count(Region, name = "count") |>
mutate(
Region = Region,
focus1 = 0,
focus2 = c(0.2, 0, 0, 0, 0, 0, 0)
)
df# A tibble: 7 × 4
Region count focus1 focus2
<fct> <int> <dbl> <dbl>
1 East Asia & Pacific 1343 0 0.2
2 Europe & Central Asia 2038 0 0
3 Latin America & Caribbean 1512 0 0
4 Middle East & North Africa 756 0 0
5 North America 108 0 0
6 South Asia 288 0 0
7 Sub-Saharan Africa 1693 0 0 # Pie chart
a1 <- ggplot(df) +
geom_arc_bar(aes(
x0 = 0, y0 = 0,
r0 = 0, r = 2,
amount = count,
fill = Region,
explode = focus1
), stat = "pie") +
scale_fill_brewer(palette = "Dark2") +
theme(aspect.ratio = 1, legend.position = "bottom") +
labs(fill = "Regions", title = "a1: with exploid = 0")
a2 <- ggplot(df) +
geom_arc_bar(aes(
x0 = 0, y0 = 0,
r0 = 0, r = 2,
amount = count,
fill = Region,
explode = focus2
), stat = "pie") +
scale_fill_brewer(palette = "Dark2") +
theme(aspect.ratio = 1, legend.position = "bottom") +
labs(fill = "Regions", title = "a2: with exploid = 0.2 for East Asia and Pacific")
a1/a2
Package
ggplot2 (Wickham 2016)
Description
Create an area plot. This cover the space between x-axis and line that connects the data points.
Understandable aesthetics
required aesthetics
x, y
optional aesthetics
alpha, colour, fill, group, linetype, linewidth
See also
Example
a1 <- worldbankdata |>
filter(Country == "Bangladesh") |>
filter(Year >= 2013 & Year <= 2021) |>
ggplot(aes(x = Year, y = Electricity)) +
geom_area(alpha = 0.5) +
theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust = 1)) +
scale_x_continuous(breaks = 2013:2021) +
labs(title = "a1: geom_area only")
a2 <- worldbankdata |>
filter(Country == "Bangladesh") |>
filter(Year >= 2013 & Year <= 2021) |>
ggplot(aes(x = Year, y = Electricity)) +
geom_area(alpha = 0.5) +
theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust = 1)) +
scale_x_continuous(breaks = 2013:2021) +
geom_point(col = "red") +
labs(title = "a2: geom_area \n and geom_point")
a1 | a2
Package
ggdemetra
Example
library(ggdemetra)
worldbankdata |>
filter(Country == "Bangladesh") |>
filter(Year >= 2013 & Year <= 2021) |>
ggplot(aes(x = Year, y = Electricity)) +
geom_line() +
#geom_sa(color = "#155692", message = FALSE) +
geom_arima(geom = "label",
x_arima = - Inf, y_arima = -Inf,
vjust = -1, hjust = -0.1,
message = FALSE)
Package
metR (Campitelli 2021)
Description
Draws directional arrows (like vectors) on a plot, which can represent directions or flows — such as wind directions, movement, or gradients. The arrow from (x, y) and pointing in the direction (dx, dy) (vector components).
Understandable aesthetics
required aesthetics
x
y
optional aesthetics
arrow,type, arrow.angle, arrow.length, arrow.ends, arrow.type
See also
library(metR)
data <- tibble::tibble(
x = c(10, 20, 30, 40, 50), # Longitude or X-coordinates
y = c(1, 2, 3, 4, 5), # Latitude or Y-coordinates
dx = c(1, 0, -1, 0, 1), # Wind direction components (change in X)
dy = c(1, -1, 0, 1, 0) # Wind direction components (change in Y)
)
ggplot(data, aes(x = x, y = y)) +
geom_point(color = "#1b9e77", size = 3) +
geom_arrow(aes(dx = dx, dy = dy), color = "#d95f02", size = 1, arrow.type = "closed") +
labs(title = "Wind Directions at Different Locations", x = "Longitude", y = "Latitude")
Package
ggformula (Kaplan and Pruim 2023)
Description
Plot Average Shifted Histogram (ASH). The ASH is a nonparametric probability density estimator derived from a collection of histograms.
Understandable aesthetics
required aesthetics
x
y
optional aesthetics
color, fill, bins, alpha, size
See also
Example
library(ggformula)
p1 <- worldbankdata |>
filter(Income == "LM") |>
ggplot(aes(x = Electricity)) +
geom_ash(bins = 20, color = "#d95f02") + labs(title="Average Shifted Histogram")
p2 <- worldbankdata |>
filter(Income == "LM") |>
ggplot(aes(x = Electricity)) +
geom_histogram(aes(y = stat(density)), color = "black", fill = "gray") +
geom_ash(bins = 20, color = "#d95f02") + labs(title = "Histogram and Average Shifted Histogram")
p1|p2
Package
ggforce (Pedersen 2022)
Description
A matrix-style (commonly from GGally package) visualizations to automatically display variable distributions on diagonal panels, handling both continuous and discrete data while simplifying the mapping of panel variables.
Understandable aesthetics
required aesthetics
data
optional aesthetics
alpha, colour, group, linetype, linewidth
See also
geom_density, geom_histogram, geom_autohistogram, geom_autopoint,
Example
library(ggplot2)
library(GGally)
# Select numeric variables
df <- happiness_gdp_income |>
select("HappinessScore2015", "HappinessScore2016", "HappinessScore2017")
ggplot(df) +
facet_matrix(
vars(
HappinessScore2015,
HappinessScore2016,
HappinessScore2017
),
layer.diag = 1
) +
geom_autodensity()Warning: Removed 16 rows containing non-finite outside the scale range
(`stat_autodensity()`).
Package
ggforce (Pedersen 2022)
Description
Understandable aesthetics
required aesthetics
data
optional aesthetics
alpha, colour, group, linetype, linewidth
See also
geom_histogram, geom_auotodensity, geom_auotpoint
Example
library(dplyr)
library(ggplot2)
library(GGally)
# Prepare dataset
df <- happiness_gdp_income %>%
select(HappinessScore2015, HappinessScore2016, HappinessScore2017,
GDPpercapita2015) %>%
rename(GDPpercapita = GDPpercapita2015) %>%
# Create income category
mutate(IncomeCategory = case_when(
GDPpercapita < 1.2 ~ "Low",
GDPpercapita >= 1.2 & GDPpercapita < 1.4 ~ "Medium",
GDPpercapita >= 1.4 ~ "High"
)) %>%
mutate(
IncomeCategory = factor(IncomeCategory, levels = c("Low", "Medium", "High")),
# Create discrete happiness scores by rounding
HappinessScore2015_discrete = factor(round(HappinessScore2015)),
HappinessScore2016_discrete = factor(round(HappinessScore2016)),
HappinessScore2017_discrete = factor(round(HappinessScore2017))
)
# Plot facet matrix using discrete happiness scores
ggplot(df, aes(x = .panel_x, fill = IncomeCategory)) +
facet_matrix(
vars(HappinessScore2015_discrete, HappinessScore2016_discrete, HappinessScore2017_discrete),
layer.diag = 1,
grid.y.diag = FALSE
) +
geom_autohistogram(alpha = 0.7, position = "dodge") + # diagonal histograms
geom_autopoint(alpha = 0.6, aes(color = IncomeCategory)) + # off-diagonal scatterplots
scale_fill_brewer(palette = "Set2") +
scale_color_brewer(palette = "Set2") +
theme_minimal() +
labs(
fill = "Income Category",
color = "Income Category",
title = "Discrete Happiness Scores by Income Category",
x = "",
y = ""
)
Package
ggplot2 (Wickham 2016)
Description
Understandable aesthetics
required aesthetics
x
y
optional aesthetics
alpha, colour, group, linetype, linewidth
See also
Example
Package
ggplot2 (Wickham 2016)
Description
Understandable aesthetics
required aesthetics
data
optional aesthetics
alpha, colour, group, linetype, linewidth
See also
Example