R Graphics · Histogram

Understanding Frequency Distributions with Histograms in R

Explore bin selection, density scaling, group comparison, reference curves, and ggplot2 styling.

6 examplesReproducible R code

Before you begin

Choose the variation that serves the analysis

Histograms divide a continuous scale into intervals. Their appearance depends on bin width, so responsible interpretation requires trying meaningful alternatives rather than accepting a default blindly.

Examples: 06
Levels: Beginner + Intermediate

Example 01Base RBeginner

Build a Basic Histogram

A histogram counts observations within adjacent numeric intervals.

hist() chooses reasonable breaks automatically, while col and border improve visual separation.

RRun this code

hist(faithful$waiting, col = "#2563eb", border = "white",
     main = "Waiting times", xlab = "Minutes")

Example 02Base RBeginner

Control the Number of Bins

Changing the bins changes how much detail the histogram reveals.

Too few bins hide structure; too many emphasize random variation. Compare several defensible choices.

RRun this code

hist(faithful$waiting, breaks = 20, col = "#60a5fa",
     border = "white", main = "Twenty bins", xlab = "Minutes")

Example 03Base RBeginner

Scale the Histogram to Density

Density scaling makes the total bar area equal to one.

Set probability = TRUE when comparing the histogram with a probability-density curve.

RRun this code

x <- faithful$waiting
hist(x, probability = TRUE, col = "#bfdbfe", border = "white")
lines(density(x), col = "#dc2626", lwd = 3)

Example 04Base RIntermediate

Compare Two Histograms

Transparent overlapping histograms compare two distributions on common bins.

Use identical breaks and transparency. For substantial overlap, separate panels or density curves may be clearer.

RRun this code

a <- iris$Sepal.Length[iris$Species == "setosa"]
b <- iris$Sepal.Length[iris$Species == "versicolor"]
breaks <- seq(4, 7.2, .3)
hist(a, breaks, col = adjustcolor("#2563eb", .5), xlim = c(4, 7.2))
hist(b, breaks, col = adjustcolor("#f59e0b", .5), add = TRUE)

Example 05Base RIntermediate

Display a Cumulative Histogram

A cumulative histogram shows how many observations fall at or below each boundary.

Calculate histogram counts without plotting, accumulate them with cumsum(), then draw the resulting bars.

RRun this code

h <- hist(faithful$waiting, plot = FALSE)
h$counts <- cumsum(h$counts)
plot(h, col = "#2563eb", border = "white",
     main = "Cumulative frequency", ylab = "Cumulative count")

Example 06ggplot2Intermediate

Create a Histogram with ggplot2

ggplot2 separates binning, aesthetics, labels, and themes into reusable layers.

Specify binwidth explicitly so the analytical choice is visible and reproducible.

RRun this code

library(ggplot2)
ggplot(faithful, aes(waiting)) +
  geom_histogram(binwidth = 5, fill = "#2563eb", color = "white") +
  labs(title = "Waiting-time distribution", x = "Minutes", y = "Count") +
  theme_minimal()