Visualizing Data in R with ggplot2: Histograms

Asst. Prof. Dr. Somsak Chanaim

International College of Digital Innovation, CMU

June 24, 2026

What is the ggplot2 package?

The ggplot2 logo

The ggplot2 package is one of the most popular and powerful data visualization packages in R.

It is based on the “Grammar of Graphics”, a framework that breaks down graphs into components such as scales, layers, and themes.

This approach allows users to build complex and customized plots in a systematic and consistent way.

Benefits of Using ggplot2:

  • Consistency: The grammar of graphics approach makes it easier to build and understand plots.

  • Flexibility: It allows for extensive customization, from simple plots to complex multi-layered visualizations.

  • Community Support: As one of the most widely used R packages, ggplot2 has a large community, extensive documentation, and numerous tutorials and examples.

The Fundamental Input: Data Frames

To create a visualization in ggplot2, the primary requirement is that your data must be structured as a Data Frame. Unlike base R plotting functions that might accept vectors or matrices, ggplot2 is designed around the Grammar of Graphics, which treats data as a structured object where columns represent variables and rows represent observations.

  • Format Requirement: The data argument in the ggplot() function must explicitly be a data.frame or a tibble.

  • Variable Mapping: Within a histogram, you map a specific column from your data frame to the x-axis using the aes() (aesthetics) function.

  • Tidy Data: For the best results, your data should be in a “tidy” format, meaning each variable has its own column.

Install and Load the Package

Open your R console or RStudio and run the following command:

install.packages("ggplot2")

This command will download and install ggplot2.

Load the Package

After installation, you need to load the package into your R session using the library() function:

library(ggplot2)

Load the Packages for this Topics.

library(ggplot2)
library(dplyr)
library(DT)

The Structure of Code in ggplot2

The basic structure typically involves the following components:

  • Data: The dataset (data frame) that you want to visualize.

  • Aesthetics (aes): Mappings of data variables to visual properties like x and y coordinates, colors, sizes, and shapes.

  • Geometries (geom): The type of plot or visual elements to represent the data (e.g., points, lines, bars).

  • Facets: Optional; used to create multiple plots based on subsets of the data.

  • Scales: Optional; used to control the mapping of data to aesthetics.

  • Coordinates: Optional; control the coordinate system.

  • Themes: Optional; used to customize the appearance of the plot.

Basic Structure of ggplot2 Code

ggplot(data = <DATA>) +
   aes(<MAPPINGS>) +
  <GEOM_FUNCTION>() +
  <FACET_FUNCTION>() +
  <SCALE_FUNCTIONS>() +
  <COORDINATE_FUNCTION>() +
  <THEME_FUNCTION>()

Example Breakdown

Let’s break down an example of creating a scatter plot using ggplot2:

The Histogram with ggplot2

Basic Structure

After load the package ggplot2

Explanation:

  • geom_histogram() creates the histogram.

  • bins = 30 Specifies the number of bins instead of using

  • fill = "skyblue" sets the fill color of the bars.

  • color = "black" outlines the bars in black.

Modified the ggplot object with the labs() Function

After create the ggplot object if we need to modifies/add titles, axis labels, legends, or captions etc.

We use the labs() function to customize the labels of various elements in a plot, including titles, axis labels, legends, and captions.

labs(
  title = NULL,      # Title of the plot
  subtitle = NULL,   # Subtitle of the plot
  x = NULL,          # Label for the x-axis
  y = NULL,          # Label for the y-axis
  caption = NULL,    # Caption at the bottom of the plot
  tag = NULL,        # Tag for the plot (like a figure number)
  fill = NULL,       # Label for fill legend (if applicable)
  color = NULL,      # Label for color legend (if applicable)
  size = NULL,       # Label for size legend (if applicable)
  shape = NULL       # Label for shape legend (if applicable)
)

Question:

Add, the title is “The histogram of N(0,1)”

the caption is ‘Your Name’.

ggplot Themes

set.seed(1)
mu <- 0; sigma <- 1
# Random data from N(mu,sigma^2)
Data <- data.frame(
     x = rnorm(n,
          mean = mu,
          sd=sigma))
ggplot(data = Data) +
aes(x = x) +
geom_histogram(fill = "skyblue",
        color = "black",
        bins = 30) +
theme_xxx()


The default theme is theme_gray().

Exercise 1: Fundamental Histogram Plotting

Complete the ggplot2 script to visualize the single-variable distribution of miles per gallon, adjusting the analytical bin width configuration.

Target output

Complete the code

library(ggplot2)
(mtcars) +
aes(x = mpg) +
(binwidth = 2) +
labs(title = "Histogram of Miles Per Gallon",
x = "Miles Per Gallon",
y = "Frequency")

Exercise 2: Histogram Aesthetic Customization

Modify the baseline histogram script by injecting parameters to alter the visual fill and structural boundary colors of the data bars.

Target output

Complete the code

library(ggplot2)
ggplot(mtcars) +
aes(x = mpg) +
geom_histogram(binwidth = 2, = "lightblue", = "black") +
labs(title = "Histogram of Miles Per Gallon",
x = "Miles Per Gallon",
y = "Frequency")

Exercise 3: Density Curve Superimposition

Complete the script to scale the y-axis to statistical density and overlay a smoothed density line layer onto the histogram.

Target output

Complete the code

library(ggplot2)
ggplot(mtcars) +
aes(x = mpg, y = (density)) +
geom_histogram(binwidth = 2, fill = "lightblue", color = "black", alpha = 0.6) +
(color = "red") +
labs(title = "Histogram and Density of Miles Per Gallon",
x = "Miles Per Gallon",
y = "Density")

Exercise 4: Multi-Panel Faceted Histograms

Complete the script to segment the single-variable distribution of miles per gallon across discrete conditioning grids based on cylinder categories.

Target output

Complete the code

library(ggplot2)
ggplot(mtcars) +
aes(x = mpg) +
geom_histogram(binwidth = 2, fill = "lightgreen", color = "black") +
labs(title = "Histogram of Miles Per Gallon by Number of Cylinders",
x = "Miles Per Gallon",
y = "Frequency") +
(. ~ )

Exercise 5: Fixed Bin Count Configuration

Complete the ggplot2 script to explicitly partition the miles per gallon variable into a predefined number of mathematical intervals.

Target output

Complete the code

library(ggplot2)
ggplot(mtcars) +
aes(x = mpg) +
geom_histogram( = 10, fill = "orange", color = "black") +
labs(title = "Histogram of Miles Per Gallon with 10 Bins",
x = "Miles Per Gallon",
y = "Frequency")

Density Plot with Iris Data

Density Plot the ggplot2

We just change the function goem_histogram() to geom_density(). If you want to plot the histogram and density plot into the same graph. In the aes() function, we set the argument y = after_stat(density)

Question

From the code, if you start with a density plot and follow it with a histogram, what happens?

Separate histogram or density by fill

Example if we have to know about histogram of income between gender male and female from this data.

Question and remark

Or remove The argument position = "identity", what happens?

The argument position = "identity" is very important don’t forgot.

From the code, if you move the argument fill = gender from aes() function to geom_histogram(), what happens?

Change the color in the arugumet fill

We change any color by add the scale_fill_manual() function, an the argument inside is values = <vector of color>

Remark: The order of colors follows the order of the characters or factors.

Legend position

Position

theme(legend.position = <position>) , <position> = “top”, “bottom”, “right”. “left”, or “none”

set.seed(1)
male <- rnorm(n =500, mean = 18000, sd = 2000)
female <- rnorm(n =500, mean = 25000, sd = 1500)
Data <- data.frame(gender = rep(c("male","female"), each = 500),
                   income = c(male, female))

Data |> ggplot() +
        aes(x = income, fill = gender) +
        geom_histogram( color ="black", 
                        alpha = 0.7, bins = 30, 
                        position = "identity") +
        scale_fill_manual(values =c("blue","red")) +
        theme(legend.position = "xxx")

The facet() function

In ggplot2, a facet is a way to create multiple plots (panels) based on the levels of one or more categorical variables, allowing you to compare different subsets of the data side by side.

Faceting is especially useful when you want to visualize the same relationship across different groups in the data.

How Faceting Works

  • Facet by a Single Variable: You can create separate panels for each level of a single categorical variable.

  • Facet by Two Variables: You can create a grid of panels, where rows correspond to one variable and columns correspond to another.

facet_grid(rows ~ cols)

  • This function creates a grid of panels based on two variables.

  • The rows correspond to levels of one variable, and the columns correspond to levels of another variable.

  • Useful when you want to explore the interaction between two categorical variables.

Why Use Facets

  • Comparison: Faceting allows you to easily compare different subsets of your data.

  • Clarity: By splitting data into separate panels, facets can make complex plots easier to read.

  • Exploration: Faceting helps explore how relationships in the data change across different groups.

Summary

Facets in ggplot2 are a powerful tool for visualizing multi-panel plots, enabling comparisons across different groups or categories in your data, faceting enhances the ability to understand complex relationships in your data by breaking them down into more manageable, comparable pieces.

Example

Exercise 6: Faceted Highway MPG Distribution

Complete the data pipeline to map highway mileage using a piped structural workflow and condition the subsets row-wise by categorical cylinder values.

Target output

Complete the code

mpg
ggplot() +
aes(x = hwy) +
geom_histogram(binwidth = 2, fill = "blue", color = "black") +
(. ~ cyl) +
labs(title = "Histogram of Highway MPG by Cylinder Count",
x = "Highway MPG",
y = "Count")

Exercise 7: Filtered Overlapping Density and Aesthetic Customization

Construct a data pipeline using the native pipe operator to subset high-displacement vehicles, map discrete classes to density functions, and adjust the global layout positioning parameters.

Target output

Complete the code

library(dplyr)
library(ggplot2)
mpg_filtered <- mpg |> filter(displ > 3)
mpg_filtered |>
ggplot() +
aes(x = hwy, fill = class) +
geom_density(color = "black", = "identity", alpha = 0.5) +
scale_fill_brewer(palette = "Set3") +
labs(title = "Histogram of Highway MPG for Cars with Displacement > 3",
x = "Highway MPG",
y = "Count",
fill = "Class") +
(legend.position = "top")

Exercise 8: Conditional Filtering, Faceting, and Manual Scale Mapping

Construct a multi-layered visualization pipeline. Apply vector matching logic to filter cylinder observations, partition data into a discrete grid matrix by drive type, and override global color aesthetics using custom manual value strings.

Target output

Complete the code

library(dplyr)
library(ggplot2)
mpg_filtered <- mpg |> (cyl %in% c(4, 6))
mpg_filtered |>
ggplot() +
aes(x = hwy, fill = drv) +
geom_histogram(binwidth = 2, color = "black") +
facet_grid(. ~ drv) +
(values = c("4" = "red", "f" = "green", "r" = "blue")) +
labs(title = "Histogram of Highway MPG by Drive Type",
x = "Highway MPG",
y = "Count",
fill = "Drive Type")

Exercise 9: Advanced Spatiotemporal Filtering and Stacked Grid Faceting

Construct a comprehensive ggplot2 analytics pipeline. Filter the temporal observation layer by a specific calendar year subset, split the structural viewport column-wise by manufacturing entity, and layer categorical classification vectors into aesthetic stacks mapped to a custom pastel brewer matrix.

Target output

Complete the code

library(dplyr)
library(ggplot2)
mpg_filtered <- mpg |> filter(year == 2008)
mpg_filtered |>
ggplot() +
aes(x = hwy, fill = class) +
geom_histogram(binwidth = 2, color = "black") +
(. ~ ) +
scale_fill_brewer(palette = "Pastel1") +
labs(title = "Stacked Histogram of Highway MPG by Manufacturer (2008)",
x = "Highway MPG",
y = "Count",
fill = "Class") +
theme(legend.position = "bottom")

Exercise 10: Density Superimposition with Multi-Stage Filtering and Custom Aesthetics

Construct a bounded pipeline to evaluate engine displacement limits, map historical data distribution counts onto statistical densities, and override group aesthetics manually across categorical facet panels.

Target output

Complete the code

library(dplyr)
library(ggplot2)
mpg_filtered <- mpg |>
filter(displ >= 2) |>
filter(displ <= 4)
mpg_filtered |>
ggplot() +
aes(x = hwy) +
geom_histogram(aes(y = (density)), binwidth = 2, fill = "lightblue", color = "black") +
geom_density(aes(fill = class), alpha = 0.4) +
facet_grid(. ~ class) +
(values = c("compact" = "red", "suv" = "green", "pickup" = "blue", "minivan" = "orange")) +
labs(title = "Histogram and Density Plot of Highway MPG by Class",
x = "Highway MPG",
y = "Density",
fill = "Class")