Understanding ggplot2: A Deeper Dive into Geom Hlines

1. Introduction

In recent years, the ggplot2 package has become an essential tool in the data visualization world. It offers a wide range of features and functionalities that make it easy to create high-quality plots. One of the most useful aspects of ggplot2 is its ability to create horizontal lines using the geom_hline() function. However, there have been instances where users have encountered errors while trying to use this function. In this article, we will explore one such scenario and provide a detailed explanation of how to resolve it.

2. The Problem

The question presented in the Stack Overflow post is as follows:

“I was wondering why variable mean_y is not recognized by my geom_hline(yintercept = unique(mean_y)) call?”

To understand this error, we need to take a closer look at how ggplot2 works and what the unique() function does.

3. How ggplot2 Works

When you use geom_hline(), you are essentially telling ggplot2 to draw a horizontal line on your plot. The yintercept argument specifies the y-coordinate of the line. In this case, we want to create a horizontal line that passes through the mean value of our dataset.

To achieve this, we need to pass the mean value of y as an argument to geom_hline(). However, when we look at the code snippet provided in the question, it seems like there is an issue with how we are accessing the mean_y variable.

4. Understanding Unique()

The unique() function returns all unique elements in a vector or list. When applied to a data frame column, it returns the unique values of that column.

In this case, when we use unique(mean_y), ggplot2 is expecting us to pass the entire column y instead of just its mean value. This is why we are getting an error - ggplot2 can’t find the mean_y variable because it’s not inside the aes() function.

5. The Solution

To resolve this issue, we need to make sure that our variables are inside the aes() function when using geom_hline(). According to the answer provided in the Stack Overflow post, we should use:

geom_hline(aes(yintercept = mean_y))

By doing so, we ensure that ggplot2 knows which variable mean_y represents.

6. Model Matrix

Another important concept related to this issue is the model matrix. In the provided code snippet, we have created a model matrix using:

X <- model.matrix(~ groups * age, data = dat)

The model.matrix() function creates a matrix where each column represents a term in the linear model. In our case, we are predicting y based on both groups and age.

7. Linear Regression

When creating a linear regression model using model.matrix(), ggplot2 will automatically calculate the coefficients for each term. However, this also means that we need to be careful when accessing these coefficients.

In our case, we have created a vector of predicted values called lin_pred. This is calculated by multiplying the model matrix with the coefficients:

lin_pred <- as.vector(X %*% betas)

By using the %*% operator (also known as matrix multiplication), we are essentially performing linear regression on our data.

8. Calculating the Mean

Finally, when calculating the mean value of y, we need to make sure that we are using the correct vector. In our case, this is:

dat$y <- rnorm(nrow(X), lin_pred, sd_e)

However, when calculating the mean value, we don’t use lin_pred. Instead, we use the original values of y calculated earlier.

9. Code Refactoring

With these concepts in mind, let’s take a closer look at the refactored code:

library(tidyverse)

set.seed(20)
n_groups <- 2
n_in_group <- 20
sd_e = 2
groups <- gl(n_groups, n_in_group, labels = c("T","C"))
age <- rnorm(length(groups), 25, 3)
betas <- c(5,0,0,2)
dat <- data.frame(groups=groups, age=age)

X <- model.matrix(~ groups * age, data = dat)

lin_pred <- as.vector(X %*% betas)

dat$y <- rnorm(nrow(X), lin_pred, sd_e)

dat %>%
  group_by(groups) %>%
  mutate(mean_y = mean(y)) %>%
  ungroup() %>%
  ggplot(aes(x = age, y = y)) +
  geom_point(aes(color=groups)) +
  geom_hline(aes(yintercept = mean_y))

By moving the geom_hline() function outside of aes(), we can ensure that the correct variable is used.

Last modified on 2024-07-07