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How to Solve R Error: missing values are not allowed in subscripted assignments of data frames

by | Programming, R, Tips

This error occurs when you attempt to subscript a data frame that contains NA values. You can solve this error by excluding the NA values during the subscript operation, for example:

data[data$col1<0 & !is.na(data$col1),]$col2

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

Table of contents

Example

Consider the following 100 row data frame:

set.seed(0)

data <- data.frame(x=c(NA, rnorm(99)), y=c(1:5))

head(data)

The first variable contains an NA value and samples from the normal distribution. The second variable has five integer values that serve as categories. Let’s look at the head of the data frame:

          x y
1         NA 1
2  1.2629543 2
3 -0.3262334 3
4  1.3297993 4
5  1.2724293 5
6  0.4146414 1

Next, we will try to replace the value in the x column that are less than zero with zero.

data[data$x<0,]$x <- 0

Let’s run the code to see the result:

Error in `[<-.data.frame`(`*tmp*`, data$x < 0, , value = list(x = c(0,  : 
  missing values are not allowed in subscripted assignments of data frames

The error occurs because the x column contains NA values.

Solution

We can solve this error by using the is.na function in the subscript operation. 

data[data$x<0 & !is.na(data$x),]$x <- 0

head(data,n=20)

By using !is.na() we are excluding the NA values during the subscript operation. Let’s run the code to see the result:

          x y
1         NA 1
2  1.2629543 2
3  0.0000000 3
4  1.3297993 4
5  1.2724293 5
6  0.4146414 1
7  0.0000000 2
8  0.0000000 3
9  0.0000000 4
10 0.0000000 5
11 2.4046534 1
12 0.7635935 2
13 0.0000000 3
14 0.0000000 4
15 0.0000000 5
16 0.0000000 1
17 0.0000000 2
18 0.2522234 3
19 0.0000000 4
20 0.4356833 5

We successfully substituted the values less than zero with zero in the x column.

Summary

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!