Introduction to Data Visualization

Data Visualization is the process of converting raw data into visual representations such as graphs, charts, and plots so that information can be understood quickly and clearly. Humans understand visuals far more efficiently than tables of numbers, which makes visualization a critical step in data analysis.

In R, data visualization is one of the strongest features because R was originally designed for statistical analysis and graphical modeling. Visualization is not only used to present final results, but also to explore data, identify trends, patterns, anomalies, and relationships before applying models.

Why Data Visualization is Important

Simplifies complex datasets
Reveals hidden patterns and trends
Helps detect outliers and errors
Improves communication of results
Supports decision-making

Graph Plotting in R

What is Graph Plotting?

Graph plotting refers to creating visual representations of data values using graphical elements such as points, lines, bars, or shapes. In R, graph plotting is mainly done using:

Base R graphics
Advanced systems like ggplot2, lattice

Base R graphics are foundational and widely used for learning concepts.

Generic Plotting System in R

R uses a generic plotting system, where the same function behaves differently based on the data type.

The most important generic function is:

plot()

The plot() function automatically determines:

Type of plot
Axis scaling
Labels (if available)

This behavior is called method dispatch.

Using the `plot()` Function

Basic Syntax

plot(x, y)

Example

x <- c(1, 2, 3, 4, 5)
y <- c(2, 4, 6, 8, 10)

plot(x, y)

This produces a scatter plot, showing the relationship between x and y.

Types of Plots Using `plot()`

Scatter Plot

Used to analyze relationships between two numerical variables.

plot(x, y, type = "p")

Line Plot

Used to show trends over time or ordered data.

plot(x, y, type = "l")

Combined Points and Lines

plot(x, y, type = "b")

Vertical Line Plot

plot(x, y, type = "h")

Graphical Models in R

Introduction to Graphical Models

Graphical models in R are visual representations of statistical data and relationships. They are used to:

Understand data distribution
Visualize correlations
Validate statistical assumptions
Analyze model performance

Graphical models include:

Scatter plots
Histograms
Boxplots
Regression plots
Residual plots

Example: Visualizing a Relationship

plot(mtcars$wt, mtcars$mpg)

This graph shows how car weight affects mileage, a common statistical analysis.

Charts and Graphs in R

Common Chart Types

Chart Type	Purpose
Line graph	Trends over time
Bar chart	Category comparison
Histogram	Distribution
Scatter plot	Relationship
Boxplot	Spread and outliers

Choosing the correct chart is crucial to avoid misleading interpretation.

Adding Titles to a Graph

Main Title

The main title describes what the graph represents.

plot(x, y, main = "Relationship Between X and Y")

Axis Labels

Axis labels explain what each axis represents.

plot(x, y,
     main = "Sales Growth",
     xlab = "Months",
     ylab = "Revenue")

Clear labels are essential for readability.

Adding Colors to Charts

Importance of Colors

Colors:

Improve readability
Highlight differences
Separate categories
Make graphs visually appealing

Using `col` Argument

plot(x, y, col = "blue")

Using Multiple Colors

plot(x, y, col = c("red", "green", "blue", "orange", "black"))

Each point gets a different color.

Color in Bar Charts

barplot(scores, col = "skyblue")

Adding Text to Plots

Using `text()`

Used to label data points.

plot(x, y)
text(x, y, labels = y, pos = 3)

pos controls label position
Helps annotate important values

Using `mtext()`

Adds text in margins.

mtext("Data Source: Survey", side = 1, line = 3)

Adding Axis to a Plot

Default Axes

R automatically generates axes based on data range.

Custom Axes

Disable default axes:

plot(x, y, xaxt = "n", yaxt = "n")

Add custom axes:

axis(1, at = 1:5)
axis(2, at = seq(0, 10, 2))
box()

Custom axes provide better control.

Axis Limits

Set axis limits manually:

plot(x, y, xlim = c(0, 6), ylim = c(0, 12))

Graphics Palette in R

What is a Graphics Palette?

A graphics palette defines the set of colors used when multiple colors are needed automatically.

View Current Palette

palette()

Set a Custom Palette

palette(c("red", "blue", "green", "orange"))

Reset:

palette("default")

Plotting Data Using Generic Plots

Plotting a Single Vector

v <- c(5, 10, 15, 20)
plot(v)

R plots index vs value.

Plotting Two Vectors

plot(x, y)

Plotting Data Frames

plot(mtcars)

This creates multiple pairwise plots.

Bar Charts in R

Introduction to Bar Charts

A bar chart displays data using rectangular bars. The length of each bar represents the value of a category.

Bar charts are ideal for:

Comparing categories
Displaying frequency counts
Showing grouped data

Creating a Simple Bar Chart

scores <- c(80, 90, 75)
names(scores) <- c("Math", "Science", "English")

barplot(scores)

Adding Titles and Labels

barplot(scores,
        main = "Student Performance",
        xlab = "Subjects",
        ylab = "Marks",
        col = "lightblue")

Horizontal Bar Chart

barplot(scores, horiz = TRUE)

Grouped Bar Chart

data <- matrix(c(80, 85, 90, 88), nrow = 2)

barplot(data,
        beside = TRUE,
        col = c("red", "blue"),
        legend.text = TRUE)

Stacked Bar Chart

barplot(data,
        col = c("orange", "green"),
        legend.text = TRUE)

Adding Values on Bars

bp <- barplot(scores)
text(bp, scores, labels = scores, pos = 3)

Common Mistakes in Visualization

Missing titles or labels
Overuse of colors
Incorrect chart type
Misleading scales
Overcrowded graphs

Summary

Data visualization in R is a powerful tool for exploring and communicating data. Base R graphics provide flexible and customizable plotting options. Understanding titles, colors, axes, text annotations, palettes, and bar charts ensures clear, accurate, and effective visual communication.

Data Visualization in R Programming