Data Visualization in R Programming

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.

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.

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.

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 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.

Choosing the correct chart is crucial to avoid misleading interpretation.

The main title describes what the graph represents.

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

Axis labels explain what each axis represents.

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

Clear labels are essential for readability.

Colors:

• Improve readability
• Highlight differences
• Separate categories
• Make graphs visually appealing

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

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

Each point gets a different color.

barplot(scores, col = "skyblue")

Used to label data points.

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

• pos controls label position
• Helps annotate important values

Adds text in margins.

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

R automatically generates axes based on data range.

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.

Set axis limits manually:

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

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

palette()

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

Reset:

palette("default")

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

R plots index vs value.

plot(x, y)

plot(mtcars)

This creates multiple pairwise plots.

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

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

barplot(scores)

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

barplot(scores, horiz = TRUE)

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

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

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

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

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

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.