How to Solve R Error in unique.default(x, nmax = nmax): unique() applies only to vectors

This error occurs when we try to use the ave() function without specifying the FUN argument.

We can solve this error by specifying the FUN argument explicitly. For example,

average <- ave(data$x, data$group, FUN=mean)

This tutorial will go through the error in detail and how to solve it with code examples.

---

Example

Let’s look at an example to reproduce the error. First, we will create a data frame.

data <- data.frame(x = sample(1000,12),  # Create example data frame
                    group = rep(letters[1:6], each = 2))
data 

    x group
1  363     a
2  292     a
3   44     b
4  130     b
5  208     c
6  938     c
7  440     d
8  832     d
9  639     e
10 386     e
11 905     f
12  33     f

Next, we will try to get the average value for each group in the data frame using the ave() function. In order to average over the groups, we need to provide the grouping variable data$group.

average <- ave(data$x, data$group, mean)

average

Let’s run the code to see the result:

Error in unique.default(x, nmax = nmax) : 
  unique() applies only to vectors

The error occurred because we did not specify the FUN argument explicitly when calling the ave() function.

Solution

We can solve this error by specifying the FUN argument. We want to calculate the average across the groups, therefore we need the mean function. Let’s look at the revised code:

average <- ave(data$x, data$group, FUN=mean)

average

Let’s run the code to get the average across the groups in the data frame as a numeric vector.

[1] 327.5 327.5  87.0  87.0 573.0 573.0 636.0 636.0 512.5 512.5 469.0 469.0

Summary

Congratulations on reading to the end of this tutorial!

For further reading on R-related errors, go to the articles: 

• How to Solve R Error: mapping should be created with aes() or aes_()
• How to Solve R Error: Could not find function “%”
• How to Solve R Error in cov.wt(z) : ‘x’ must contain finite values only 

Go to the online courses page on R to learn more about coding in R for data science and machine learning.

Have fun and happy researching!