Displaying statistical measures such as the mean in a plot can greatly enhance the interpretability of your data visualization. In R Programming Language the base plotting system offers a flexible and powerful way to create custom plots. This article will guide you through the steps to display the mean of your data with an underline in a base R plot.

Introduction to Base R Plotting

Base R plotting is one of the fundamental tools for data visualization in R. It provides a range of functions to create various types of plots, from simple scatter plots to complex multi-panel figures. Unlike ggplot2, which is based on the Grammar of Graphics, base R plotting functions are more straightforward and imperative.

Key Functions in Base R Plotting

Before we delve into the specifics of displaying the mean with an underline, let’s review some key base R plotting functions:

• plot(): Creates a scatter plot or other types of plots depending on the arguments.
• lines(): Adds lines to an existing plot.
• abline(): Adds straight lines to a plot.
• points(): Adds points to a plot.
• text(): Adds text to a plot.
• arrows(): Adds arrows to a plot, which can be used to create custom annotations.

Displaying the Mean in a Plot

To display the mean of a dataset in a plot, we need to:

1. Calculate the mean
2. Add a line to the plot to represent the mean.
3. Optionally, annotate the mean with text.

Let’s walk through an example where we have a dataset, and we want to display the mean with an underline.

Step 1: Prepare Your Data

First, we need some data to work with. Let’s create a simple numeric vector:

# Sample data
data <- c(5, 7, 8, 9, 10, 12, 15, 18, 20)

Step 2: Calculate the Mean

Next, calculate the mean of the data:

# Calculate mean
mean_value <- mean(data)

Step 3: Create the Base Plot

Now, let’s create a basic scatter plot of the data:

# Basic scatter plot
plot(data, main="Scatter Plot with Mean", xlab="Index", ylab="Value")

Step 4: Add the Mean Line

To add a line representing the mean, use the abline() function:

# Add mean line
abline(h=mean_value, col="blue", lwd=2)

Step 5: Underline the Mean with an Arrow

To underline the mean, we can use the arrows() function to draw a horizontal line just below the mean line:

# Add underline to mean
arrows(x0=1, y0=mean_value - 0.5, x1=length(data), y1=mean_value - 0.5, col="red", lwd=2)

Step 6: Annotate the Mean

Finally, add a text annotation to indicate the mean value:

# Add text annotation
text(x=length(data) / 2, y=mean_value - 1, labels=paste("Mean =", round(mean_value, 2)), col="red")

Here is the complete code snippet:

# Sample data
data <- c(5, 7, 8, 9, 10, 12, 15, 18, 20)

# Calculate mean
mean_value <- mean(data)

# Basic scatter plot
plot(data, main="Scatter Plot with Mean", xlab="Index", ylab="Value")

# Add mean line
abline(h=mean_value, col="blue", lwd=2)

# Add underline to mean
arrows(x0=1, y0=mean_value - 0.5, x1=length(data), 
                                  y1=mean_value - 0.5, col="red", lwd=2)

# Add text annotation
text(x=length(data) / 2, y=mean_value - 1, 
                         labels=paste("Mean =", round(mean_value, 2)), col="red")

Output:

Display mean with underline in base R plot

Display mean with underline on in built dataset

To display the mean with an underline in a base R plot, you can use the text() function to add text annotations to your plot.

Here’s an example using the built-in mtcars dataset:

# Load the mtcars dataset
data(mtcars)

# Create a base plot of mpg vs wt
plot(mtcars$wt, mtcars$mpg, main = "Scatter Plot with Mean Underlined",
                            xlab = "Weight (1000 lbs)", ylab = "Miles per Gallon")

# Calculate the mean of mpg
mean_mpg <- mean(mtcars$mpg)

# Display the mean value on the plot with an underline
# Adjust the x and y coordinates to position the text
text(x = 3.5, y = mean_mpg, labels = paste("Mean MPG:", round(mean_mpg, 2)),
                                                              pos = 3, cex = 0.8)

# Add an underline below the text
underline_x <- c(3.3, 3.7) # Adjust these values as needed for positioning
underline_y <- rep(mean_mpg - 0.5, 2) # Adjust this value for positioning

lines(underline_x, underline_y, lwd = 2)

Output:

Display mean with underline in base R plot

• Load Data: Load the mtcars dataset which comes built-in with R.
• Base Plot: Create a scatter plot of mpg versus wt.
• Calculate Mean: Compute the mean of the mpg variable.
• Add Text Annotation: Use the text() function to add a label indicating the mean value. The pos = 3 argument places the text above the specified coordinates.
• Underline: Use the lines() function to draw a horizontal line below the text. Adjust the underline_x and underline_y vectors to position the underline correctly.

Conclusion

Displaying the mean with an underline in a base R plot is a straightforward process that enhances the readability of your data visualizations. By following the steps outlined above, you can create a clear and informative plot that highlights the mean value effectively. Base R plotting functions offer the flexibility needed to customize your plots to meet specific needs, making it a valuable tool for data analysis and presentation.