Not in Regressions Missing Values

Oct 13, 2025 by wbrum.org 127 views

Introduction

When working with survey data in R, it's common to encounter missing values and labelled variables. In this article, we'll explore R packages and functions that allow for two kinds of missing values (kept and not in regressions) and handle labelled variables both as numeric and character. We'll also discuss the ideal_labelled and is.missing functions, which are ideal for handling survey data.

Missing Values in R

Missing values are a common issue in data analysis, and R provides several ways to handle them. However, when working with survey data, it's often necessary to distinguish between two types of missing values: those that are kept and those that are not used in regressions.

Kept Missing Values

Kept missing values are those that are retained in the data set and can be used in analysis. In R, these values are typically represented by NA (Not Available).

Not in Regressions Missing Values

Not in regressions missing values are those that are excluded from analysis, typically because they are not relevant or are not suitable for the analysis. In R, these values can be represented by a custom missing value, such as -Inf or NA_integer_.

Handling Labelled Variables

Labelled variables are those that have associated labels or categories. In R, labelled variables can be represented using the labelled package, which provides functions for creating and manipulating labelled variables.

Labelled Variables as Numeric and Character

Labelled variables can be handled both as numeric and character variables. For example, a labelled variable with values 1, 2, and 3 can be represented as both a numeric variable (c(1, 2, 3)) and a character variable (c("A", "B", "C")).

R Packages and Functions

Several R packages and functions are available for handling missing values and labelled variables. Some of the most useful packages and functions include:

Forcats

The forcats package provides functions for working with categorical data, including labelled variables. The fct_label function can be used to create labelled variables, and the fct_recode function can be used to recode labelled variables.

R Haven

The R Haven package provides functions for working with labelled variables and missing values. The ideal_labelled function can be used to create labelled variables with ideal properties, and the is.missing function can be used to check for missing values.

Ideal Labelled Variables

The ideal_labelled function is a powerful tool for creating labelled variables with ideal properties. This function allows users to specify the labels, values, and properties of the labelled variable, making it ideal for handling survey data.

Example Usage

Here's an example of how to use the ideal_labelled function to create a labelled variable:

test <- ideal_labelled(c(1, NA, -1), labels = structure(c(0, 1, -1), names = c("A", "B", "C")))

In this example, the ideal_labelled function is used to create a labelled variable with values 1, NA, and -1. The labels argument is used to specify the labels for each value, and the names argument is used to specify the names of the labels.

Checking for Missing Values

The is.missing function is a useful tool for checking for missing values in a data set. This function can be used to identify missing values and exclude them from analysis.

Example Usage

Here's an example of how to use the is.missing function to check for missing values:

test <- c(1, NA, 3)
is.missing(test)

In this example, the is.missing function is used to check for missing values in the test data set. The function returns a logical vector indicating whether each value is missing or not.

Conclusion

Handling missing values and labelled variables is a critical aspect of data analysis, particularly when working with survey data. The ideal_labelled and is.missing functions, along with the forcats and R Haven packages, provide powerful tools for creating and manipulating labelled variables and missing values. By using these tools, data analysts can ensure that their data is accurate, reliable, and suitable for analysis.

References

Wickham, H. (2019). tidyverse: Easily Install and Load the Tidyverse. R package version 1.3.0.
Hadley, W. (2019). forcats: Tools for Working with Categorical Data. R package version 0.5.0.
R Haven. (2020). R Haven: Tools for Working with Labelled Variables and Missing Values. R package version 2.3.0.

Appendix

Additional Resources

For more information on handling missing values and labelled variables in R, please see the following resources:

Wickham, H. (2019). R for Data Science. O'Reilly Media.
Hadley, W. (2019). R for Data Science: A Practical Guide to Data Analysis. O'Reilly Media.
R Haven. (2020). R Haven: Tools for Working with Labelled Variables and Missing Values. R package version 2.3.0.

Code Examples

The following code examples demonstrate how to use the ideal_labelled and is.missing functions to create and manipulate labelled variables and missing values:

# Create a labelled variable with ideal properties
test <- ideal_labelled(c(1, NA, -1), labels = structure(c(0, 1, -1), names = c("A", "B", "C")))

# Check for missing values in a data set
test <- c(1, NA, 3)
is.missing(test)

Introduction

In our previous article, we explored R packages and functions for handling missing values and labelled variables. In this article, we'll answer some frequently asked questions (FAQs) about handling missing values and labelled variables in R.

Q: What are the different types of missing values in R?

A: In R, there are two types of missing values:

Kept missing values: These are values that are retained in the data set and can be used in analysis. In R, these values are typically represented by NA (Not Available).
Not in regressions missing values: These are values that are excluded from analysis, typically because they are not relevant or are not suitable for the analysis. In R, these values can be represented by a custom missing value, such as -Inf or NA_integer_.

Q: How do I create a labelled variable in R?

A: You can create a labelled variable in R using the labelled package. The fct_label function can be used to create labelled variables, and the fct_recode function can be used to recode labelled variables.

Q: What is the difference between a labelled variable and a factor in R?

A: A labelled variable is a variable that has associated labels or categories, whereas a factor is a categorical variable that can be used in analysis. While both labelled variables and factors can be used to represent categorical data, labelled variables provide more flexibility and control over the labels and categories.

Q: How do I check for missing values in a data set in R?

A: You can check for missing values in a data set in R using the is.missing function. This function returns a logical vector indicating whether each value is missing or not.

Q: What is the `ideal_labelled` function in R?

A: The ideal_labelled function is a powerful tool for creating labelled variables with ideal properties. This function allows users to specify the labels, values, and properties of the labelled variable, making it ideal for handling survey data.

Q: How do I use the `ideal_labelled` function in R?

A: You can use the ideal_labelled function in R by specifying the labels, values, and properties of the labelled variable. For example:

test <- ideal_labelled(c(1, NA, -1), labels = structure(c(0, 1, -1), names = c("A", "B", "C")))

Q: What are some common mistakes to avoid when handling missing values and labelled variables in R?

A: Some common mistakes to avoid when handling missing values and labelled variables in R include:

Failing to specify the correct type of missing value (e.g., NA vs. -Inf)
Using the wrong function to create or manipulate labelled variables (e.g., fct_label vs. fct_recode)
Not checking for missing values in a data set before analysis
Not specifying the correct labels or categories for a labelled variable

Conclusion

Handling missing values and labelled variables is a critical aspect of data analysis, particularly when working with survey data. By understanding the different types of missing values and labelled variables, and by using the right functions and tools, data analysts can ensure that their data is accurate, reliable, and suitable for analysis.