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

by | Programming, R, Tips

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.

Table of contents


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))
    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)


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.


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)


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


Congratulations on reading to the end of this tutorial!

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

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!

Research Scientist at Moogsoft | + posts

Suf is a research scientist at Moogsoft, specializing in Natural Language Processing and Complex Networks. Previously he was a Postdoctoral Research Fellow in Data Science working on adaptations of cutting-edge physics analysis techniques to data-intensive problems in industry. In another life, he was an experimental particle physicist working on the ATLAS Experiment of the Large Hadron Collider. His passion is to share his experience as an academic moving into industry while continuing to pursue research. Find out more about the creator of the Research Scientist Pod here and sign up to the mailing list here!