Understanding Function Plots in R

Introduction to ggplot and Stat_function

R’s ggplot package is a popular data visualization library that provides a powerful and flexible way to create a wide range of visualizations. One common type of plot produced by ggplot is the function plot, which displays a mathematical function over a specific interval.

The stat_function function in ggplot2 allows users to add a function plot to their ggplot objects. This function takes several arguments, including the data frame containing the x-values for the function, the function itself, and various options for customizing the appearance of the plot.

Creating Function Plots with Different Ranges

In this section, we’ll explore how to combine two function plots in R that have different ranges.

One way to achieve this is by using the xlim argument within the stat_function function. This allows us to specify a specific range for the x-axis of each plot.

For example:

library(ggplot2)
myfun <- function(x) {
  1/(1 + exp(-x))
}

ggplot(NULL, aes(x)) +
  stat_function(data = data.frame(x = c(0, 20)), fun = myfun, geom = "line") +
  stat_function(data = data.frame(x = c(10, 20)), fun = 1/myfun, geom = "line")

However, as the user pointed out in their Stack Overflow question, using 1/myfun instead of myfun for the second function can lead to incorrect results.

To fix this issue, we need to create two separate functions with different ranges:

library(ggplot2)
myfun <- function(x) {
  1/(1 + exp(-x))
}

ggplot(NULL) +
  stat_function(data = data.frame(x = c(0, 10)), aes(x, color = "blue"), fun = myfun, xlim = c(0, 10)) +
  stat_function(data = data.frame(x = c(10, 20)), aes(x, color = "red"), fun = myfun, xlim = c(10, 20)) +
  scale_color_manual(labels = c("blue", "red"), values = c("blue", "red"))

This will produce a plot with two different ranges for the function.

Creating Multiple Function Plots with Different Ranges

Using Separate Functions

As mentioned earlier, one way to combine two function plots in R is by creating separate functions with different ranges.

Let’s take a closer look at this approach:

library(ggplot2)
myfun1 <- function(x) {
  1/(1 + exp(-x))
}

myfun2 <- function(x) {
  1/(1/(1 + exp(-x)))
}

ggplot(NULL) +
  stat_function(data = data.frame(x = c(0, 20)), aes(x), fun = myfun1, geom = "line") +
  stat_function(data = data.frame(x = c(10, 20)), aes(x), fun = myfun2, geom = "line")

This code creates two separate functions myfun1 and myfun2, each with a different range.

Customizing Function Plots

Adding Customizations to Function Plots

We can customize function plots in several ways, including:

• Changing the line color and style using the color and linetype arguments within the stat_function function.
• Adding labels to the x-axis and y-axis using the axis.title argument.
• Modifying the appearance of the plot grid lines.

For example, let’s add a custom label to the x-axis:

library(ggplot2)
myfun <- function(x) {
  1/(1 + exp(-x))
}

ggplot(NULL) +
  stat_function(data = data.frame(x = c(0, 20)), aes(x), fun = myfun, geom = "line") +
  scale_x_continuous(labels = function(x) paste("x =", round(x * 10) / 10, sep = ""))

Conclusion

Combining Function Plots in R

In this article, we explored how to combine two function plots in R using different approaches. We covered creating separate functions with different ranges, customizing function plots, and adding labels to the x-axis.

Whether you’re working on a simple data visualization project or a complex statistical analysis, understanding how to create and customize function plots is essential for effective communication of your results.

By following the tips and techniques outlined in this article, you can create high-quality function plots that accurately represent your data.

Last modified on 2024-01-22