Raincloud Plots

What they are for

A raincloud plot combines:

a half violin plot or density plot
a boxplot
individual data points

This makes it useful for showing:

the distribution of values
the summary statistics
the raw observations

Use a raincloud plot when you want to compare the distribution of a numeric variable across groups.

# Install if needed:
# install.packages(c("ggplot2", "ggdist", "palmerpenguins", "dplyr"))

library(ggplot2)
library(ggdist)
library(palmerpenguins)

## 
## Attaching package: 'palmerpenguins'

## The following objects are masked from 'package:datasets':
## 
##     penguins, penguins_raw

library(dplyr)

## 
## Attaching package: 'dplyr'

## The following objects are masked from 'package:stats':
## 
##     filter, lag

## The following objects are masked from 'package:base':
## 
##     intersect, setdiff, setequal, union

penguins_clean <- penguins %>%
  select(species, body_mass_g) %>%
  na.omit()

ggplot(penguins_clean, aes(x = species, y = body_mass_g, fill = species)) +
  stat_halfeye(
    adjust = 0.5,
    width = 0.6,
    justification = -0.2,
    .width = 0,
    point_colour = NA
  ) +
  geom_boxplot(
    width = 0.12,
    outlier.shape = NA,
    alpha = 0.5
  ) +
  geom_jitter(
    width = 0.08,
    alpha = 0.5,
    size = 1.5
  ) +
  labs(
    title = "Raincloud Plot of Penguin Body Mass",
    x = "Species",
    y = "Body Mass (g)"
  ) +
  theme_minimal() +
  theme(legend.position = "none")

If you have a second category you want to show….

library(ggplot2)
library(ggdist)
penguins_clean <- penguins %>%
  select(species, sex, body_mass_g) %>%
  na.omit()

ggplot(penguins_clean, aes(x = species, y = body_mass_g, fill = sex)) +
  
  # half-eye distribution
  stat_halfeye(
    position = position_dodge(width = 0.75),
    adjust = 0.6,
    width = 0.55,
    .width = 0,
    justification = -0.2,
    point_colour = NA,
    alpha = 0.5
  ) +
  
  # boxplot summary
  geom_boxplot(
    aes(color = sex),
    width = 0.12,
    position = position_dodge(width = 0.75),
    outlier.shape = NA,
    alpha = 0.65,
    linewidth = 0.5
  ) +
  
  # raw data points
  geom_jitter(
    aes(color = sex),
    position = position_jitterdodge(
      jitter.width = 0.08,
      dodge.width = 0.75
    ),
    size = 1.8,
    alpha = 0.25
  ) +
  
  labs(
    title = "Penguin Body Mass by Species and Sex",
    subtitle = "Raincloud plot showing distribution, summary statistics, and individual observations",
    x = "Species",
    y = "Body Mass (g)",
    fill = "Sex",
    color = "Sex"
  ) +
  
  scale_fill_manual(values = c("female" = "mistyrose3", "male" = "darkseagreen3")) +
  scale_color_manual(values = c("female" = "indianred3", "male" = "seagreen4")) +
  
  theme_classic(base_size = 13) +
  theme(
    plot.title = element_text(face = "bold", hjust = 0.5),
    plot.subtitle = element_text(hjust = 0.5),
    axis.title = element_text(face = "bold"),
    legend.position = "right"
  )

library(ggplot2)
library(ggdist)
library(palmerpenguins)
library(dplyr)

penguins_clean <- penguins %>%
  filter(!is.na(species), !is.na(body_mass_g)) %>%
  mutate(species = factor(species, levels = c("Adelie", "Chinstrap", "Gentoo")))

ggplot(penguins_clean, aes(x = species, y = body_mass_g)) +
  
  # half violin (raincloud shape)
  stat_halfeye(
    adjust = 0.6,
    width = 0.6,
    .width = 0,
    justification = -0.3,
    point_colour = NA,
    fill = "#74a9cf",
    alpha = 0.7
  ) +
  
  # boxplot
  geom_boxplot(
    width = 0.12,
    outlier.shape = NA,
    fill = "white",
    color = "black",
    linewidth = 0.8
  ) +
  
  # points (aligned dots instead of jitter chaos)
  geom_dotplot(
    binaxis = "y",
    stackdir = "down",
    dotsize = 0.6,
    fill = "gray40",
    alpha = 0.7
  ) +
  
  labs(
    title = "Penguin Body Mass by Species",
    x = "Species",
    y = "Body Mass (g)"
  ) +
  
  theme_classic(base_size = 16) +
  theme(
    plot.title = element_text(face = "bold", hjust = 0.5),
    axis.title = element_text(face = "bold")
  )

## Bin width defaults to 1/30 of the range of the data. Pick better value with
## `binwidth`.

Sina Plots

A sina plot is similar to a jitter plot, but the points are spread based on the density of the data. That means:

where there are more points, the plot becomes wider
where there are fewer points, it stays narrow

This makes it a nice alternative to:

strip plots
jitter plots
violin plots

It shows both individual observations and distribution shape.

Use a sina plot when you want to show raw data points without them overlapping too much, while also giving a sense of density.

Points are not randomly scattered Wider sections indicate a greater concentration of values

# Install if needed:
# install.packages(c("ggplot2", "ggforce", "palmerpenguins", "dplyr"))

library(ggplot2)
library(ggforce)
library(palmerpenguins)
library(dplyr)

penguins_clean <- penguins %>%
  select(species, flipper_length_mm) %>%
  na.omit()

ggplot(penguins_clean, aes(x = species, y = flipper_length_mm, color = species)) +
  geom_sina(alpha = 0.7, size = 2) +
  labs(
    title = "Sina Plot of Penguin Flipper Length",
    x = "Species",
    y = "Flipper Length (mm)"
  ) +
  theme_minimal() +
  theme(legend.position = "none")

Cleveland Plots

A Cleveland dot plot is used to compare values across categories using dots instead of bars.

It is helpful because:

it avoids the visual bulk of barplots
it makes it easier to compare positions along a shared axis
it works especially well when there are many groups

Use a Cleveland dot plot when comparing one summary value across categories.

Examples:

average expression by gene
average body mass by species
percentage of students by major

This example compares the average body mass of penguin species.

# Install if needed:
# install.packages(c("ggplot2", "palmerpenguins", "dplyr"))

library(ggplot2)
library(palmerpenguins)
library(dplyr)

species_summary <- penguins %>%
  group_by(species) %>%
  summarise(mean_body_mass = mean(body_mass_g, na.rm = TRUE)) %>%
  arrange(mean_body_mass)

ggplot(species_summary, aes(x = mean_body_mass, y = reorder(species, mean_body_mass))) +
  geom_point(size = 4) +
  labs(
    title = "Cleveland Dot Plot of Mean Penguin Body Mass",
    x = "Mean Body Mass (g)",
    y = "Species"
  ) +
  theme_minimal()

If you have groups…

grouped_summary <- penguins %>%
  group_by(species, sex) %>%
  summarise(mean_bill_length = mean(bill_length_mm, na.rm = TRUE), .groups = "drop")

ggplot(grouped_summary, aes(x = mean_bill_length, y = species, color = sex)) +
  geom_point(size = 3, position = position_dodge(width = 0.4)) +
  labs(
    title = "Grouped Cleveland Dot Plot of Mean Bill Length",
    x = "Mean Bill Length (mm)",
    y = "Species",
    color = "Sex"
  ) +
  theme_minimal()

Forest Plots

A forest plot shows:

a point estimate (like a mean or effect size)
a confidence interval (uncertainty around that estimate)

Use a forest plot when:

you are comparing estimates across groups
you want to include uncertainty

library(ggplot2)
library(dplyr)
library(palmerpenguins)

summary_data <- penguins %>%
  group_by(species) %>%
  summarise(
    mean_mass = mean(body_mass_g, na.rm = TRUE),
    sd = sd(body_mass_g, na.rm = TRUE),
    n = n(),
    se = sd / sqrt(n),
    lower = mean_mass - 1.96 * se,
    upper = mean_mass + 1.96 * se
  )


ggplot(summary_data, aes(x = mean_mass, y = reorder(species, mean_mass))) +
  geom_point(size = 4) +
  geom_errorbarh(aes(xmin = lower, xmax = upper), height = 0.2) +
  labs(
    title = "Forest Plot of Mean Penguin Body Mass",
    x = "Mean Body Mass (g) with 95% CI",
    y = "Species"
  ) +
  theme_minimal()

## Warning: `geom_errorbarh()` was deprecated in ggplot2 4.0.0.
## ℹ Please use the `orientation` argument of `geom_errorbar()` instead.
## This warning is displayed once per session.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.

## `height` was translated to `width`.

If you have grouped data…

summary_grouped <- penguins %>%
  group_by(species, sex) %>%
  summarise(
    mean_mass = mean(body_mass_g, na.rm = TRUE),
    sd = sd(body_mass_g, na.rm = TRUE),
    n = n(),
    se = sd / sqrt(n),
    lower = mean_mass - 1.96 * se,
    upper = mean_mass + 1.96 * se,
    .groups = "drop"
  )

ggplot(summary_grouped, aes(x = mean_mass, y = species, color = sex)) +
  geom_point(position = position_dodge(width = 0.5), size = 3) +
  geom_errorbarh(
    aes(xmin = lower, xmax = upper),
    position = position_dodge(width = 0.5),
    height = 0.2
  ) +
  labs(
    title = "Forest Plot of Body Mass by Species and Sex",
    x = "Mean Body Mass (g) with 95% CI",
    y = "Species",
    color = "Sex"
  ) +
  theme_minimal()

## `height` was translated to `width`.

Homework

You will be using this dataset for the homework

library(ggplot2)
library(dplyr)
library(palmerpenguins)

penguins_clean <- penguins %>%
  filter(!is.na(species), !is.na(sex), !is.na(body_mass_g))

Part 1. Create one plot of your choice to visualize the relationship between:

Species
Mass
Sex

penguins_clean

## # A tibble: 333 × 8
##    species island    bill_length_mm bill_depth_mm flipper_length_mm body_mass_g
##    <fct>   <fct>              <dbl>         <dbl>             <int>       <int>
##  1 Adelie  Torgersen           39.1          18.7               181        3750
##  2 Adelie  Torgersen           39.5          17.4               186        3800
##  3 Adelie  Torgersen           40.3          18                 195        3250
##  4 Adelie  Torgersen           36.7          19.3               193        3450
##  5 Adelie  Torgersen           39.3          20.6               190        3650
##  6 Adelie  Torgersen           38.9          17.8               181        3625
##  7 Adelie  Torgersen           39.2          19.6               195        4675
##  8 Adelie  Torgersen           41.1          17.6               182        3200
##  9 Adelie  Torgersen           38.6          21.2               191        3800
## 10 Adelie  Torgersen           34.6          21.1               198        4400
## # ℹ 323 more rows
## # ℹ 2 more variables: sex <fct>, year <int>

ggplot(penguins_clean, aes(x = species, y = body_mass_g, color = sex)) +
  geom_sina(alpha = 0.8, size = 3) +
  labs(
    title = "Sina Plot of Penguin Penguin Body Magg (g)",
    x = "Species",
    y = "Body Mass (g)"
  ) +
  theme_minimal()

What plot type did you choose?
Why is this plot appropriate for this data?
What patterns do you observe?

I chose a Sina plot for my data because I feel it best represents the raw data for this chart. Using a sina plot allows me to see al the points on the graph which also allows me to clearly see the clusters and differences between the penguin sexes and species. Plus, having them side by side allows me to all groups easily.

Part 2. Create a raincloud plot showing body mass across species.

ggplot(penguins_clean, aes(x = species, y = body_mass_g, fill = species)) +
  stat_halfeye(
    adjust = 0.4,
    width = 0.5,
    justification = -0.1,
    .width = 0,
    point_colour = NA
  ) +
  geom_boxplot(
    width = 0.12,
    outlier.shape = NA,
    alpha = 0.8
  ) +
  geom_jitter(
    width = 0.07,
    alpha = 0.4,
    size = 1.3
  ) +
  labs(
    title = "Raincloud Plot of Penguin Species & Their Body Mass",
    x = "Species",
    y = "Body Mass (g)"
  ) +
  theme_minimal() +
  theme(legend.position = "none")

Which species has the highest body mass?
Which species shows the greatest variability?
What does this plot show that a boxplot alone would not?

The species with the highest body mass is the Gentoo, their body mass averages are significantly higher than the other two species.The species with the greatest variability has to go to the Gentoo once again, as their geom jitter is much more spread out when compared to the others. A box plot alone would not be able to show me individiual points nor the figures on the right that give frequency. ### Part 3. Create a forest plot. Now summarize the data and visualize uncertainty.

# Don't forget, you will need to make summary data. 
summary_data <- penguins_clean %>%
  group_by(species) %>%
  summarise(
    mean_mass = mean(body_mass_g, na.rm = TRUE),
    sd = sd(body_mass_g, na.rm = TRUE),
    n = n(),
    se = sd / sqrt(n),
    lower = mean_mass - 1.96 * se,
    upper = mean_mass + 1.96 * se
  )


ggplot(summary_data, aes(x = mean_mass, y = reorder(species, mean_mass),color=species)) +
  geom_point(size = 5) +
  geom_errorbarh(aes(xmin = lower, xmax = upper), height = 0.3) +
  labs(
    title = "Forest Plot of Mean Penguin Body Mass",
    x = "Mean Body Mass (g)",
    y = "Species"
  ) +
  theme_minimal()+
theme(legend.position = "none")

## `height` was translated to `width`.

Which group has the highest mean body mass?
Which group has the widest confidence interval? Why?
Do any groups appear clearly different (based on overlap)?

The group with the highest mean body mass is the Gentoo species. When comparing confidence intervals the chinstrap species appears to have the largest one. This is because the error bars are the largest out of the three. The Adelie species and the Gentoo species are very different as they are on polar opposites of the graph. Adelie sits on the lower end around 3700g while Gentoo sits high up with around 5000g of average body mass.

Raincloud, Sina, Cleveland and Correlation

2026-04-16