Plotting the “Average” Curve of a Set of Curves in ggplot2

In this article, we will explore how to plot the average curve of a set of curves using ggplot2 in R. We will start by generating some sample data and then walk through the individual steps involved in creating the plot.

Introduction

The concept of plotting the average curve of a set of curves is often used in signal processing and time series analysis. It involves finding the average value of all points at each x-coordinate, which can help identify trends or patterns in the data.

In this article, we will use ggplot2 to create a plot that shows the average curve of a set of curves. We will start by generating some sample data and then perform the necessary steps to create the plot.

Generating Sample Data

To begin, we need to generate some sample data that represents our set of curves. In this example, we will create 5 data frames with different x-coordinates and corresponding y-values.

# Load required libraries
library(tidyverse)

# Generate sample data
set.seed(2)
ll <- lapply(1:5, function(i) {
  data.frame(x = seq(i, length.out = 10, by = i), y = rnorm(10))
})

Combining Data Frames into a Single Data Frame

Next, we need to combine the individual data frames into a single data frame using the bind_rows function from the dplyr package.

# Combine data frames into a single data frame
dat <- bind_rows(ll, .id = "source")

Adding Interpolated Values

Now that we have our combined data frame, we can add interpolated values to it. We will use the approx function from the stats package to find the average value of all points at each x-coordinate.

# Add interpolated values
dat$avg <- with(dat, approx(x, y, xout = x))$y

Creating the Plot

Finally, we can create the plot using ggplot2. We will add a point layer to represent our original data, and a line layer to represent the average curve.

# Create the plot
ggplot(dat, aes(x, y)) +
  geom_point(aes(color = source)) +
  geom_line(aes(y = avg)) +
  geom_smooth(se = FALSE, color = "red", span = 0.3, linetype = "11")

Step-by-Step Guide

Here is a step-by-step guide to creating the plot:

Step 1: Generate Sample Data

Generate sample data using the lapply function and create individual data frames for each curve.

# Load required libraries
library(tidyverse)

# Generate sample data
set.seed(2)
ll <- lapply(1:5, function(i) {
  data.frame(x = seq(i, length.out = 10, by = i), y = rnorm(10))
})

Step 2: Combine Data Frames into a Single Data Frame

Combine the individual data frames using the bind_rows function from the dplyr package.

# Load required libraries
library(dplyr)

# Combine data frames into a single data frame
dat <- bind_rows(ll, .id = "source")

Step 3: Add Interpolated Values

Add interpolated values to the combined data frame using the approx function from the stats package.

# Load required libraries
library(stats)

# Add interpolated values
dat$avg <- with(dat, approx(x, y, xout = x))$y

Step 4: Create the Plot

Create the plot using ggplot2 and add a point layer to represent our original data, and a line layer to represent the average curve.

# Load required libraries
library(ggplot2)

# Create the plot
ggplot(dat, aes(x, y)) +
  geom_point(aes(color = source)) +
  geom_line(aes(y = avg)) +
  geom_smooth(se = FALSE, color = "red", span = 0.3, linetype = "11")

Conclusion

In this article, we walked through the individual steps involved in creating a plot that shows the average curve of a set of curves using ggplot2 in R. We generated sample data, combined it into a single data frame, added interpolated values, and created the final plot.

Last modified on 2025-02-22