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Histogram, Density, Box and Violin Examples

Source: vignettes/articles/examples-distribution.qmd

Distribution plots describe the shape of one variable. A histogram is walked bin by bin, a density curve point by point along the smoothed curve, and a box plot as its five-number summary plus outliers, with sonification tracing the rise and fall of each. Histograms, box plots and density curves are stable plot types in both ggplot2 and Base R, and violin plots are stable in ggplot2 (geom_violin()); the Base R reading of vioplot::vioplot() is an experimental plot type (see “Supported plot types” and “Experimental Plot Types” in the README), so this page shows only the ggplot2 violin example. The examples hub lists every other plot family.

Histogram

A histogram shows the frequency distribution of a continuous variable by grouping values into bins. The height of each bar indicates how many observations fall within that range.

ggplot2 

exam_scores <- data.frame(score = c(
  rnorm(500, mean = 72, sd = 10),
  rnorm(500, mean = 85, sd = 8)
))

p <- ggplot(exam_scores, aes(x = score)) +
  geom_histogram(bins = 25, fill = "skyblue", color = "white") +
  labs(
    title = "Distribution of Exam Scores",
    x = "Score",
    y = "Frequency"
  ) +
  theme_minimal()

p

Base R 

scores <- c(rnorm(500, mean = 72, sd = 10), rnorm(500, mean = 85, sd = 8))
hist(scores,
  breaks = 25,
  col = "skyblue",
  border = "white",
  main = "Distribution of Exam Scores",
  xlab = "Score",
  ylab = "Frequency"
)

KDE (Kernel Density Estimation) Plots

A density curve shows the estimated probability distribution of a continuous variable. It provides a smooth alternative to histograms for understanding the shape of data.

ggplot2 

density_values <- data.frame(value = c(
  rnorm(400, mean = 25, sd = 5),
  rnorm(600, mean = 40, sd = 8)
))

p <- ggplot(density_values, aes(x = value)) +
  geom_density(fill = "lightblue", alpha = 0.5, color = "steelblue") +
  labs(
    title = "Age Distribution of Survey Respondents",
    x = "Age",
    y = "Density"
  ) +
  theme_minimal()

p

Base R 

ages <- c(rnorm(400, mean = 25, sd = 5), rnorm(600, mean = 40, sd = 8))
d <- density(ages)
plot(d,
  col = "steelblue", lwd = 2,
  main = "Age Distribution of Survey Respondents",
  xlab = "Age",
  ylab = "Density"
)
polygon(d, col = rgb(0.678, 0.847, 0.902, 0.5), border = "steelblue")
Plot

Box Plot

A box plot summarizes a distribution using its five-number summary: minimum, first quartile (Q1), median (Q2), third quartile (Q3), and maximum. Outliers appear as individual points.

ggplot2 

p <- ggplot(iris, aes(x = Species, y = Sepal.Length)) +
  geom_boxplot(fill = "lightblue", alpha = 0.7) +
  labs(
    title = "Sepal Length by Iris Species",
    x = "Species",
    y = "Sepal Length (cm)"
  ) +
  theme_minimal()

p

Base R 

boxplot(Sepal.Length ~ Species,
  data = iris,
  col = "lightblue",
  main = "Sepal Length by Iris Species",
  xlab = "Species",
  ylab = "Sepal Length (cm)"
)

Violin Plot

A violin plot combines kernel density estimation (KDE) curves with box-summary statistics, providing a richer view of the data distribution than a box plot alone. MAIDR renders each violin as two navigable layers: a box layer (min, Q1, median, Q3, max) and a KDE layer (density curve).

Note: Stable violin support is ggplot2 only. vioplot::vioplot() is read as an experimental Base R type; see the experimental table in the README.

p <- ggplot(mtcars, aes(x = factor(cyl), y = mpg)) +
  geom_violin(fill = "lightblue", alpha = 0.7) +
  labs(
    title = "MPG Distribution by Cylinder Count",
    x = "Cylinders",
    y = "Miles per Gallon"
  ) +
  theme_minimal()

p