library(forcats)
library(tidyr)
# Transform and filter the `movies_na` dataset
movies_na_long <- movies_na %>%
# Pivot genre columns (from Action to Short) into long format:
# Creates two columns: "genre" and "value"
pivot_longer(Action:Short, names_to = "genre") %>%
# Filter to keep only:
# - rows where the movie is flagged in that genre (value != 0)
# - only the genres Comedy and Drama
# - movies released between 1900 and 2000
filter(
value != 0,
genre %in% c("Comedy", "Drama"),
between(year, 1900, 2000)
) %>%
# Convert genre to a factor
mutate(genre = as_factor(genre)) %>%
# Count the number of movies per genre per year
count(genre, year)3 Geoms
This book/site is under construction
Everything is liable to change, even the title
A geom (short for geometric object) is a component that defines how data is visually represented in a plot. Geoms determine the type of visualization or the graphical shape that will be drawn.
“These geoms are the fundamental building blocks of ggplot2 (Wickham et al. 2025). They are useful in their own right, but are also used to construct more complex geoms. Most of these geoms are associated with a named [chart]: when that geom is used by itself in a [chart], that [chart] has a special name.” (Wickham 2016)
ggplot2 already has a long list of geoms. We won’t be discussing those unless there is an extension package that is an improvement to the original. Primarily, this chapter focuses on the geoms that ggplot2 does not include.
3.1 Area charts
Area charts are based on line charts. The area between the x-axis and each line (or the area between lines) is shaded to help highlight the volume of the data.
In this chapter, we’ll take a look at the horizon chart, an improved version of the ribbon chart, and the streamgraph. They are all different takes on the area chart.
3.1.1 Horizon chart
A horizon chart is a method for condensing time series data into a format that is both informative and relatively easy to interpret.
Often, when you have both positive and negative values, they lie on both sides of the x-axis. In a horizon chart, the negative values are on the same side as the positive ones.
We use color to show whether the values are positive or negative. But also for the magnitude of those values.
As Jonathan Schwabish points out in their book, Better Data Visualizations (Schwabish 2021), “the purpose of the horizon chart is not necessarily to enable readers to pick out specific values, but instead to easily spot general trends and identify extreme values”.
3.1.1.1 Viz #1: Helsinki temperatures, part I
For the horizon chart, we’ll be using ggHoriPlot (Rivas-González 2022). The package includes various example data sets. But we’ll be using weather data from the Finnish Meteorological Institute (FMI). Its open data, weather observations are licensed under CC BY 4.0.
Using the FMI API (Application Programming Interface), I retrieved the average temperatures in Helsinki (Kaisaniemi weather station) for the years 2000-2024. You can take a look at the data below.
We have avg_temperature_celsius (daily average temperature (in Celsius)), day, month, and year. We also have the date_dummy column. It is there because we want to use the month as the x-axis. But the column needs to be in date format for our use case. So we need all the rows to have the same dummy year with real months and days. I chose 2024 because it was a leap year. Without it, all the rows with February 29th would have NA in that column instead of the correct values.
Before we can proceed with the visualization, we need to perform some data wrangling. First, we’ll remove outliers using the interquartile range (IQR) method.
library(dplyr)
# Filter temperature data to exclude outliers based on 1.5 * IQR method
cutpoints <- temperature_hki %>%
filter(
between(
avg_temperature_celsius,
quantile(
avg_temperature_celsius, 0.25, na.rm = TRUE
) - 1.5 * IQR(avg_temperature_celsius, na.rm = TRUE),
quantile(
avg_temperature_celsius, 0.75, na.rm = TRUE
) + 1.5 * IQR(avg_temperature_celsius, na.rm = TRUE)
)
)Fifteen outliers were filtered out, and we can continue. Next, we’ll calculate the midpoint of the temperature range and also divide the scale into evenly spaced value ranges. We’ll use the first as the origin for the horizon chart and the second to determine how to color the areas.
# Calculate the midpoint of the temperature range for use in horizon chart
origin <- cutpoints %>%
summarize(origin = mean(range(avg_temperature_celsius))) %>%
pull(origin)
# Create the scale vector:
# 7 evenly spaced values across the filtered temperature range.
# Drop the 4th value (the midpoint), as required by gghoriplot scale input
scale <- cutpoints %>%
summarize(
min_val = min(avg_temperature_celsius),
max_val = max(avg_temperature_celsius)
) %>%
# Generate 7 evenly spaced values
with(seq(min_val, max_val, length.out = 7)) %>%
# Convert to tibble to use dplyr::slice()
tibble() %>%
# Remove the middle value (4th out of 7)
slice(-4) %>%
# Return as plain numeric vector
pull(.)The origin is 3.55, and the scale cutpoints are as follows: -19.3, -11.68, -4.07, 11.17, 18.78, 26.4.
Now we’re ready for the visualization itself. Besides ggHoriPlot and ggplot2, we’ll be using ggthemes (Arnold 2024) to provide us the theme. We’ll dive deeper into themes (including ggthemes in Section 9.1.6) later on in Chapter 9.
We’re using geom_horizon() to create the horizon chart. The arguments to pay attention to are fill (inside aes()), origin, and horizonscale. They are all using the origin and scale we calculated earlier. scale_fill_hcl() is also available in the ggHoriPlot package. Otherwise, we’re using basic ggplot2 functionalities.
library(ggHoriPlot) # for geom_horizon() to create horizon plots
library(ggplot2) # for general plotting functions
library(ggthemes) # for additional themes like theme_few()
# Create the horizon chart
ggplot(temperature_hki) +
# Horizon chart layer, mapping x, y, and fill aesthetics
geom_horizon(
aes(
date_dummy, # x-axis: typically date
avg_temperature_celsius, # y-axis: temperature variable
fill = ..Cutpoints.. # fill determined by horizon chart cutpoints
),
origin = origin, # baseline (e.g., 0°C); defines neutral midpoint
horizonscale = scale # vertical scale; controls how bands are split
) +
# Use a diverging color scale (red-blue), reversed so red = high temp
scale_fill_hcl(palette = 'RdBu', reverse = TRUE) +
# Create one small horizon chart per year, stacked vertically
facet_grid(vars(year)) +
# Use a clean, simple theme based on the rules and examples from Stephen
# Few's Show Me the Numbers and Practical Rules for Using Color in Charts
theme_few() +
# Customize appearance
theme(
# Customize x-axis labels
axis.text.x = element_text(size = 10),
# Remove unnecessary labels
axis.text.y = element_blank(),
axis.title.y = element_blank(),
axis.ticks.y = element_blank(),
panel.border = element_blank(),
# Remove vertical space between facets
panel.spacing.y = unit(0, "lines"),
# Subtle caption style
plot.caption = element_text(size = 8, hjust = 0, color = "#777777"),
# Adjust margins because otherwise Jan is too close to the left edge
plot.margin = margin(10, 10, 10, 15),
# Customize facet labels
strip.text.y = element_text(size = 8, angle = 0, hjust = 0)
) +
# Format x-axis to show months (with short names) without expansion
scale_x_date(
expand = c(0, 0),
date_breaks = "1 month",
date_labels = "%b"
) +
# Add informative plot title, subtitle, and data source caption
labs(
title = "Average daily temperature (Celsius) in Helsinki",
subtitle = "From 2000 to 2024",
caption = "Data: Finnish Meteorological Institute open data, weather observations (CC BY 4.0)",
x = NULL # remove x-axis title
)
I’m not a climatologist, but it does seem like there is a trend, over time, of Helsinki having milder winters. The summer temperatures are less clear-cut and will need a closer look.
3.1.2 Ribbon chart (improved)
As the ggplot2 documentation tells us, an area chart is, in fact, a special case of a ribbon chart. That makes sense when you realize that every type of area chart has a ymin and ymax. In the basic area chart, ymin is zero, and ymax is y. (Wickham et al. 2025)
The ribbon chart, then, displays the area between two lines. geom_ribbon() gets the job done when the lines don’t meet. But as you’ll soon see, for those cases where they do, you need something called braiding. You can read more about it in the ggbraid (Grantham 2025) documentation.
3.1.2.1 Viz #2: IMDB movies, Part I
We’ll take a look at what the problem (and solution) looks like with real data. The question we’d like to answer here is which genre, comedy or drama, has produced more movies during the 20th century.
But let’s first examine the data we’ll be using for this. Time series data works best for this type of chart. Let’s stick to the ggplot2movies (Wickham 2015) data set that we first encountered in Section 2.1.2 and the movies_na tibble.
We’ll perform some transformations to prepare the data for visualization. In this case, we’ll need the data in two formats, long and short.
Let’s first create the long tibble consisting of genre, year, and n (for the count).
One minor detail to note here is that we’ll turn the genre column from character to factor format. We’ll use forcats (Wickham 2023a) from tidyverse (Wickham 2023c) to do that. It’s not necessary in this case, but it’s good practice.
Next, we’ll split the genre into two separate columns, Comedy and Drama. They will retrieve their values from the n column.
We’ll also be adding a fill_condition column. We’ll use that later to determine which color to use to fill the area between the two lines.
# Convert long-format genre counts back to wide format and compare values
movies_na_wide <- movies_na_long %>%
# Pivot genre counts from long to wide:
# Each genre becomes its own column (e.g., Comedy, Drama),
# with yearly counts as values
pivot_wider(
names_from = genre,
values_from = n
) %>%
# Create a new logical column indicating whether
# Comedy had fewer movies than Drama in that year
mutate(fill_condition = Comedy < Drama)Now we can return to the topic of why we need to use an extension for cases where the lines don’t stay separate.
Here’s what the basic visualization would look like with geom_ribbon() from ggplot2.
ggplot() +
geom_line(
aes(year, n, linetype = genre),
data = movies_na_long
) +
geom_ribbon(
aes(year, ymin = Comedy, ymax = Drama, fill = fill_condition),
data = movies_na_wide,
alpha = 0.7
)
That won’t work if we want to use the ribbon chart to show where the two categories change places, indicating which is greater.
But that’s where ggbraid’s geom_braid() comes to the rescue. The basic code is the same. We’ll only switch the geom function.
The rest of the code is to make the visualization more presentable. Note that we’ll move the legend inside the plot. We’ll use the legend.position argument inside the theme() function to do that. There’s enough white space inside the plot to accommodate the legend. This way, we gain more space to showcase the time series element of the plot.
library(ggbraid) # for geom_braid() to visualize overlapping time series
library(ggplot2) # for general plotting functions
ggplot() +
# Line plot for number of movies per genre per year
geom_line(
aes(year, n, linetype = genre),
data = movies_na_long
) +
# Braid layer to highlight which genre had more movies per year
geom_braid(
aes(year, ymin = Comedy, ymax = Drama, fill = fill_condition),
data = movies_na_wide,
alpha = 0.7
) +
# Text annotation when comedies dominated
annotate(
"text",
x = 1938,
y = 300,
size = 4,
label = "More comedies than drama",
hjust = 0.5,
color = "#F36523"
) +
# Text annotation when dramas dominated
annotate(
"text",
x = 1975,
y = 80,
size = 4,
label = "More drama than comedies",
hjust = 0.5,
color = "#125184"
) +
# Manually set fill colors for the braid based on fill condition
scale_fill_manual(values = c("#F36523", "#125184")) +
# Customize x-axis: limit, spacing, and ticks
scale_x_continuous(
expand = c(0, 1),
limits = c(1899, 2001),
breaks = seq(1900, 2000, by = 10)
) +
# Customize y-axis: limit, spacing, and ticks
scale_y_continuous(
expand = c(0, 1),
limits = c(0, 800),
breaks = seq(0, 800, by = 100)
) +
# Hide fill legend (keep linetype legend only)
guides(fill = "none") +
# Add plot title, subtitle, and axis labels
labs(
linetype = NULL,
title = "100 years of cinema",
subtitle = "Number of comedies vs. dramas throughout the 20th century",
x = NULL,
y = NULL
) +
# Use a clean black-and-white theme as a base
theme_bw() +
# Custom legend appearance and positioning
theme(
legend.direction = "horizontal",
legend.box.background = element_rect(
color = "black",
linetype = "solid",
linewidth = 0.5
),
legend.key.size = unit(2, "line"),
legend.position = c(0.19, 0.88), # relative position inside plot
legend.text = element_text(size = 10)
)
I’m not an expert on this topic either. Based on this dataset, however, there appears to be a correlation between major wars (WWI, WWII, and the Vietnam War) and the production of more comedies than dramas.
3.1.2.2 Viz #3: Helsinki temperatures, part II
Let’s take a look at another ribbon chart. We can use the data set from Section 3.1.1, which contains average daily temperatures in Helsinki from 2000 to 2024. We’ll compare the two years, 2000 and 2024. Which one had more warmer days?
First, we’ll perform similar transformations as before and convert the data into both long and wide formats.
One minor detail to note here is that we’ll need to turn the year column from numeric to factor format. It won’t work as a category for the linetype argument otherwise. We’ll again use the forcats package to do that.
temperature_hki_long <- temperature_hki %>%
# Filter data to include only the years 2000 and 2024
filter(year %in% c(2000, 2024)) %>%
# Convert `year` to a factor (useful for plotting or grouping)
mutate(year = as_factor(year)) %>%
# Keep only the necessary columns for analysis or visualization
select(avg_temperature_celsius, year, date_dummy)In this next transformation, note the use of the names_prefix argument. A column with a number as the first character of the name is not ideal. This will take care of that.
# Pivot data from long to wide format
temperature_hki_wide <- temperature_hki_long %>%
# Creates one column per year (e.g., year_2000, year_2024),
# using temperature values as the content
pivot_wider(
names_from = year,
names_prefix = "year_",
values_from = avg_temperature_celsius
) %>%
# Create a new logical column to compare the two years:
# TRUE if 2024 temp > 2000 temp for that date
mutate(fill_condition = year_2000 < year_2024)We’ll also count the number (and percentage of total) of days where the average temperature is greater in 2000 and 2024. We’ll use this information for annotations.
temperature_hki_wide %>%
# Count how many days had each condition (TRUE/FALSE)
count(fill_condition) %>%
# Calculate the percentage for each group
mutate(n_pct = round(n / sum(n), 3))# A tibble: 2 × 3
fill_condition n n_pct
<lgl> <int> <dbl>
1 FALSE 145 0.396
2 TRUE 221 0.604Looks like 2024 has more days (60.4%) that were, on average, warmer than 2000 (39.6%).
The visualization itself is like the movie example. The most significant difference is the use of two packages from the tidyverse family.
str_glue() from stringr (Wickham 2023b) features a convenient implicit line break functionality. We’ll also use it to add the degree Celsius symbol (°C) to the y-axis.
as_date() from lubridate (Spinu, Grolemund, and Wickham 2024) allows us to use the date in character format to map it to the x-axis. This helps us place the annotations in the correct position.
library(ggbraid) # for geom_braid(), visualizing area between two lines
library(ggplot2) # for general plotting functions
library(lubridate) # for working with date types
library(stringr) # for string manipulation like str_glue()
ggplot() +
# Add temperature lines for each year (2000 and 2024)
geom_line(
aes(date_dummy, avg_temperature_celsius, linetype = year),
data = temperature_hki_long
) +
# Add braided area showing difference between 2000 and 2024
# Fill based on which year was warmer (fill_condition)
geom_braid(
aes(
date_dummy,
ymin = year_2000,
ymax = year_2024,
fill = fill_condition
),
data = temperature_hki_wide,
alpha = 0.7
) +
# Annotate area where 2000 was warmer
annotate(
"text",
x = as_date("2024-03-01"),
y = -17.5,
size = 4,
label = str_glue(
"2000 was warmer
40 % of the days"
),
hjust = 0.5,
color = "#125184"
) +
# Annotate area where 2024 was warmer
annotate(
"text",
x = as_date("2024-11-15"),
y = 17.5,
size = 4,
label = str_glue(
"2024 was warmer
60 % of the days"
),
hjust = 0.5,
color = "#F36523"
) +
# Manual fill colors: blue for 2000 warmer, orange for 2024 warmer
scale_fill_manual(values = c("#125184", "#F36523")) +
# Format x-axis: monthly ticks, short month labels
scale_x_date(
date_breaks = "1 month",
date_labels = "%b",
expand = c(0, 0.1)
) +
# Format y-axis: show temperature with °C symbol
scale_y_continuous(labels = ~ str_glue("{.x} °C")) +
# Hide fill legend (keep linetype legend only)
guides(fill = "none") +
# Add plot title, subtitle, and caption
labs(
linetype = NULL,
title = "Is the temperature rising?",
subtitle = "Average daily temperatures (Celsius) in Helsinki, 2000 vs. 2024",
caption = "Data: Finnish Meteorological Institute open data, weather observations (CC BY 4.0)",
x = NULL,
y = NULL
) +
# Use a clean, minimal black-and-white theme
theme_bw() +
# Customize legend and caption styling
theme(
legend.direction = "horizontal",
legend.box.background = element_rect(
color = "black",
linetype = "solid",
linewidth = 0.5
),
legend.key.size = unit(2, "line"),
legend.position = c(0.83, 0.12), # bottom-right position
legend.text = element_text(size = 10),
plot.caption = element_text(size = 8, hjust = 1, color = "#777777")
)
And so we have another perspective on the Helsinki temperature data set.
3.1.3 Streamgraph
A streamgraph is a stacked area chart where the areas are positioned around the central axis.
3.1.3.1 Viz #4: ggplot2 dependencies
If you’ve read the book in chronological order, you’ve already encountered a streamgraph in Section 1. We will use that visualization as the example in this chapter.
The purpose of this visualization is to illustrate how the ggplot2 dependencies have evolved from a small speck in 2007 to their current state at the end of 2024. This is the exact use case for which I would use a streamgraph.
Before we proceed, I want to mention Georgios Karamanis. Their original visualization for Tidy Tuesday (see Figure 3.1) was the inspiration for my version.
If you want to know the differences, they are as follows:
- Use the initial release years instead of the latest release…
- …which meant switching the data source
- Use the ggplot2-related packages’ metadata
- Bring in a third type, Imports
- Change the color palette
- Change the fonts to Roboto Mono
- Annotate all the major ggplot2 releases
- Make the stream chart less wavy
- Other, smaller changes
We’ll cover these in more detail as we proceed.
Let’s take a look at the data we’re working with. It was gathered with pkgsearch (Csárdi and Salmon 2025).
We won’t reiterate the definitions of the different types (see Section 1 for them). But we can see that the count for each type starts small in 2007 and increases significantly by 2024.
Let’s first take a look at what the streamgraph would look like with default settings. We’ll use geom_stream() from ggstream (Sjoberg 2021) for that.
library(ggstream)
ggplot2_dependencies_by_year %>%
ggplot() +
geom_stream(
aes(
x = year,
y = n,
fill = type
)
)
That’s not bad for ten lines of code. But we can make it more presentable.
Let’s start with colors. In the previous visualizations, we’ve inserted the hex codes straight into the code. But since we need to use these colors in many places, let’s convert them into a vector so we don’t have to repeat ourselves.
color_1 <- "#F36523"
color_2 <- "#125184"
color_3 <- "#2E8B57"
colors <- c(color_1, color_2, color_3)
colors[1] "#F36523" "#125184" "#2E8B57"For the font, I wanted to use something different. Roboto is a slightly futuristic font (family) that I like. But it doesn’t come with R.
That’s why we’ll use showtext (Qiu and Raggett 2024) (read more about it in Section 9.6.2).
library(showtext)
font_add_google("Roboto Mono", "roboto")
showtext_auto()
font_family <- "roboto"Next, we’ll create the annotations that’ll be displayed on the right side of the graph.
The labels are in an HTML-style format. To be used later with ggtext (Wilke and Wiernik 2022). We’ll delve into this topic in more detail in Section 9.4.1.
annotation_numbers <- ggplot2_dependencies_by_year %>%
# Total number of packages per dependency type
summarize(
n = sum(n),
.by = type
) %>%
# Ensure consistent vertical stacking order
arrange(type) %>%
mutate(
# Manually assign y-positions for labels
y = c(390, 75, -300),
# Create HTML-styled rich labels with colored numbers
label = case_when(
type == "Depends" ~
str_glue("**<span style='color:{color_1}'>{n}</span>**"),
type == "Imports" ~
str_glue("**<span style='color:{color_2}'>{n}</span>**"),
type == "Suggests" ~
str_glue("**<span style='color:{color_3}'>{n}</span>**")
)
)Now it’s time to bring it all together.
Usually, you would start with the main geom for the plot. But we must begin with the data points and labels due to the layer order. We’re stacking layers on top of each other, and here we want the lines for the labels to stay behind the streamgraph.
The new packages we haven’t mentioned before are colorspace (Ihaka et al. 2024) and ggrepel (Slowikowski 2024).
We get back to them both in detail later, but colorspace (see more in Section 16.2.2.1)) is used for color manipulation. In this graph, we’re making the borders of the areas slightly darker than the areas themselves. ggrepel (see more in Section 5.10) is used for creating labels that don’t overlap.
library(colorspace) # for color manipulation like darken()
library(ggplot2) # for general plotting functions
library(ggrepel) # for placing non-overlapping labels
library(ggstream) # for creating streamgraphs
library(ggtext) # for text manipulation
library(stringr) # for string manipulation like str_glue()
ggplot(ggplot2_dependencies_by_year) +
# Add a small point at the origin to anchor the first version label
geom_point(
aes(x = 2007, y = 0),
data = NULL,
size = 1.5,
stat = "unique",
) +
# Add a label for ggplot2 version 0.5 (2007)
geom_label_repel(
aes(x = 2007, y = 0, label = "{ggplot2}\nver 0.5"),
data = NULL,
stat = "unique",
nudge_y = 75,
label.size = NA,
lineheight = 0.5,
family = font_family
) +
# Add a label for ggplot2 version 1.0 (2014)
geom_label_repel(
aes(x = 2014, y = 50, label = "{ggplot2}\nver 1.0"),
data = NULL,
stat = "unique",
nudge_y = 115,
label.size = NA,
lineheight = 0.5,
family = font_family
) +
# Add a label for ggplot2 version 2.0 (2015)
geom_label_repel(
aes(x = 2015, y = 125, label = "{ggplot2}\nver 2.0"),
data = NULL,
stat = "unique",
nudge_y = 140,
label.size = NA,
lineheight = 0.5,
family = font_family
) +
# Add a label for ggplot2 version 3.0 (2018)
geom_label_repel(
aes(x = 2018, y = 100, label = "{ggplot2}\nver 3.0"),
data = NULL,
stat = "unique",
nudge_y = 200,
label.size = NA,
lineheight = 0.5,
family = font_family
) +
# Add a label for ggplot2 version 3.5 (2024)
geom_label_repel(
aes(x = 2024, y = 410, label = "{ggplot2}\nver 3.5"),
data = NULL,
stat = "unique",
nudge_y = 75,
label.size = NA,
lineheight = 0.5,
family = font_family
) +
# Create a stream-like area chart showing the number of dependent
# packages by type and year
geom_stream(
aes(
x = year,
y = n,
fill = type,
# Slightly darken stream borders for contrast
color = after_scale(darken(fill))
),
# bw means bandwidth of kernel density estimation
# This is the argument you can control the waviness with
# The closer the value is to 1, the less wavy the graph
bw = 1,
linewidth = 0.1
) +
# Add annotations on the right side using rich text labels from ggtext
geom_richtext(
data = annotation_numbers,
aes(
x = 2024 + 0.2,
y = y,
label = label
),
hjust = 0,
lineheight = 0.9,
label.size = NA,
size = 5,
family = font_family
) +
# Configure the x-axis with major and minor breaks
scale_x_continuous(
breaks = seq(2008, 2024, 2),
minor_breaks = 2007:2024
) +
# Manually assign fill colors for the dependency types
scale_fill_manual(values = colors) +
# Allow annotations to extend outside the plot area
coord_cartesian(clip = "off") +
# Set plot titles and descriptions using inline color styling
labs(
title = str_glue("Number of packages on CRAN in 2024 <span style='color:{color_1}'>depending on</span>, <span style='color:{color_2}'>importing</span>, or <span style='color:{color_3}'>suggesting</span> {{ggplot2}}"),
subtitle = "Aggregated by the initial package release years. Categories may change from one version to another and were taken from the latest versions.",
caption = "Data: CRAN via {pkgsearch} | Visualization: Antti Rask | Updated: 2024-12-31"
) +
# Apply a clean, minimal theme and customize text styling
theme_minimal(base_family = font_family) +
# Customize title and caption styling
theme(
axis.text.x = element_text(
size = 14,
face = "bold",
margin = margin(10, 0, 0, 0)
),
axis.text.y = element_blank(),
axis.title = element_blank(),
legend.position = "none",
panel.grid.major.y = element_blank(),
panel.grid.minor.y = element_blank(),
plot.margin = margin(10, 50, 10, 10),
plot.title = element_markdown(
face = "bold",
size = 16,
hjust = 0.5
),
plot.subtitle = element_text(
hjust = 0.5,
margin = margin(0, 0, 20, 0)
),
plot.caption = element_text(
size = 10,
color = darken("darkgrey", 0.4),
hjust = 0.5,
margin = margin(20, 0, 0, 0)
)
)
Streamgraph, like other area graphs, isn’t the best for making detailed comparisons between groups. But it’s excellent for creating a broader picture of what’s going on in the data.