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Base R Graphics

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Introduction

Base R graphics provide a powerful and flexible system for creating a wide variety of plots and charts. This module will cover the basics of creating and customizing plots using base R functions. By the end of this section, you will be able to create simple plots, customize them, and understand the different types of plots available in base R.

Key Concepts

  1. Plotting Functions: Functions like plot(), hist(), boxplot(), etc.
  2. Customizing Plots: Adding titles, labels, legends, and customizing colors and symbols.
  3. Types of Plots: Scatter plots, line plots, bar plots, histograms, box plots, etc.

Basic Plotting Functions

Scatter Plot

The plot() function is one of the most versatile plotting functions in base R. It can be used to create scatter plots, line plots, and more.

# Example: Scatter Plot
x <- rnorm(100)
y <- rnorm(100)
plot(x, y, main="Scatter Plot", xlab="X-axis", ylab="Y-axis", col="blue", pch=19)

Explanation:

  • x and y are vectors of data points.
  • main sets the title of the plot.
  • xlab and ylab set the labels for the x and y axes.
  • col sets the color of the points.
  • pch sets the plotting character (symbol) for the points.

Line Plot

To create a line plot, you can use the type argument in the plot() function.

# Example: Line Plot
x <- seq(1, 10, by=1)
y <- x^2
plot(x, y, type="l", main="Line Plot", xlab="X-axis", ylab="Y-axis", col="red")

Explanation:

  • type="l" specifies that the plot should be a line plot.

Bar Plot

The barplot() function is used to create bar plots.

# Example: Bar Plot
counts <- c(5, 10, 15, 20)
barplot(counts, main="Bar Plot", xlab="Categories", ylab="Counts", col="green", names.arg=c("A", "B", "C", "D"))

Explanation:

  • counts is a vector of bar heights.
  • names.arg sets the names for the bars.

Histogram

The hist() function is used to create histograms.

# Example: Histogram
data <- rnorm(1000)
hist(data, main="Histogram", xlab="Values", col="purple", border="black")

Explanation:

  • data is a vector of values to be plotted in the histogram.
  • border sets the color of the border around the bars.

Box Plot

The boxplot() function is used to create box plots.

# Example: Box Plot
data <- list(A=rnorm(100), B=rnorm(100, mean=1), C=rnorm(100, mean=2))
boxplot(data, main="Box Plot", xlab="Groups", ylab="Values", col=c("orange", "yellow", "pink"))

Explanation:

  • data is a list of numeric vectors.
  • col sets the colors for the boxes.

Customizing Plots

Adding Titles and Labels

You can add titles and labels to your plots using the title() function.

# Example: Adding Titles and Labels
plot(x, y)
title(main="Main Title", sub="Subtitle", xlab="X-axis Label", ylab="Y-axis Label")

Adding Legends

The legend() function is used to add legends to your plots.

# Example: Adding Legends
plot(x, y, col="blue", pch=19)
points(x, y+1, col="red", pch=17)
legend("topright", legend=c("Group 1", "Group 2"), col=c("blue", "red"), pch=c(19, 17))

Explanation:

  • legend specifies the position of the legend.
  • legend argument specifies the text for the legend.
  • col and pch specify the colors and symbols for the legend.

Customizing Colors and Symbols

You can customize the colors and symbols in your plots using various arguments in the plotting functions.

# Example: Customizing Colors and Symbols
plot(x, y, col=rainbow(100), pch=16)

Explanation:

  • col=rainbow(100) sets a rainbow color palette for the points.
  • pch=16 sets the plotting character to a filled circle.

Practical Exercises

Exercise 1: Create a Scatter Plot

Create a scatter plot using the plot() function with the following data:

  • x <- rnorm(50)
  • y <- rnorm(50)

Add a title, x and y labels, and customize the color and symbol of the points.

Solution:

x <- rnorm(50)
y <- rnorm(50)
plot(x, y, main="Scatter Plot Exercise", xlab="X-axis", ylab="Y-axis", col="darkgreen", pch=18)

Exercise 2: Create a Histogram

Create a histogram using the hist() function with the following data:

  • data <- rnorm(500)

Add a title, x label, and customize the color of the bars.

Solution:

data <- rnorm(500)
hist(data, main="Histogram Exercise", xlab="Values", col="skyblue", border="white")

Exercise 3: Create a Box Plot

Create a box plot using the boxplot() function with the following data:

  • data <- list(Group1=rnorm(50), Group2=rnorm(50, mean=2), Group3=rnorm(50, mean=4))

Add a title, x and y labels, and customize the colors of the boxes.

Solution:

data <- list(Group1=rnorm(50), Group2=rnorm(50, mean=2), Group3=rnorm(50, mean=4))
boxplot(data, main="Box Plot Exercise", xlab="Groups", ylab="Values", col=c("lightblue", "lightgreen", "lightcoral"))

Common Mistakes and Tips

  • Mistake: Forgetting to label axes and add titles.
    • Tip: Always add meaningful titles and labels to make your plots more informative.
  • Mistake: Overlapping points in scatter plots.
    • Tip: Use transparency (alpha parameter) or jittering to avoid overlapping points.
  • Mistake: Using too many colors or symbols.
    • Tip: Keep your plots simple and avoid using too many different colors or symbols.

Conclusion

In this section, you learned how to create and customize various types of plots using base R graphics. You should now be able to create scatter plots, line plots, bar plots, histograms, and box plots, and customize them with titles, labels, legends, colors, and symbols. In the next module, we will explore more advanced data visualization techniques using the ggplot2 package.

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