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How to Solve R Error: object not found

by | Programming, R, Tips

If you try to refer to an object that has not been defined in an R code block or before it, you will raise the error object not found. The R interpreter could not find the variable mentioned in the error message. You can check if a variable exists using ls or exists, then create the variable if it does not exists.

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


Example #1: Missing Variable definition

The easiest way to reproduce the error is to reference a variable name that we did not previously define, for example:

> x
Error: object 'x' not found

If we try to call a function and pass an undefined variable as an argument, we will also raise the error:

> mean(x)
Error in mean(x) : object 'x' not found

Solution

We can solve this error by defining the variable. Let’s look at the revised code.

> x <- c (44, 31, 20, 5, 77)
> exists("x")

In the above code, we use the c() function to create a vector of numbers. We can verify that the variable exists by using the built-in exists() method. If exists() returns TRUE, then the variable exists. Otherwise, if it returns FALSE, the variable does not exist.

[1] TRUE

We can also list all of the defined variables using ls().

> ls()
[1] "x"

We can use an if statement only to call a function if a variable exists:

> if (exists("x")) mean(x)

Let’s run the code to see the result:

[1] 35.4

We successfully calculated the mean of the numbers in the vector with the variable name “x“.

Example #2: Subset DataFrame with Missing Column Name

The error can also occur when trying to subset a data frame, and the column name we reference does not exist. Let’s look at an example of a data frame with random numbers sampled from the normal distribution.

> d <- data.frame(a = rnorm(10))
> d
             a
1  -0.71675335
2  -0.51653842
3  -0.54683104
4   0.36271733
5  -0.13695605
6   0.08996665
7   1.06901831
8  -0.32218248
9  -0.17214685
10  2.50305373

Let’s try to subset the column with the name “b” under the condition that the values are greater than zero:

> subset(d, b>0)

Let’s run the code to see the result:

Error in eval(e, x, parent.frame()) : object 'b' not found

The error occurs because column “b” does not exist in the data frame.

Solution: Check if a Column Exists in data frame using %in% and grepl

We can verify if a column exists using %in% and grepl.

Using %in%

We can use the %in% operator to identify if a column belongs to a data frame:

> "b" %in% names(d)
[1] FALSE

We can see that column “b” does not exist in the data frame, whereas column “a” does exist:

> "a" %in% names(d)
[1] TRUE

Using grepl

We can use grepl to check for a match in the column names. If grepl returns FALSE, then the column name is not present in the data frame.

grepl("^b$", names(d))
[1] FALSE

We can see that column “b” does not exist in the data frame, whereas column “a” does exist:

grepl("^a$", names(d))
[1] TRUE

We can use an if statement to check for membership in the data frame before performing subsetting. Let’s look at the revised code:

> if("a" %in% names(d)) subset(d, a > 0)
  a
4  0.36271733
6  0.08996665
7  1.06901831
10 2.50305373

We successfully retrieved the positive values in column “a“.

Summary

Congratulations on reading to the end of this tutorial! The error object not found occurs when you try to reference an undefined variable in your R code. You can check if an object exists using exists() or ls(), and you can check if a column exists in a data frame using %in% or grepl.

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!