Keywords: R | correlation | missing data
Abstract: This article explains why the cor() function in R may return NA or 1 in correlation matrices, focusing on the impact of missing values and the use of the 'use' argument to handle such cases. It also touches on zero-variance variables as an additional cause for NA results. Practical code examples are provided to illustrate solutions.
Introduction
When using the cor() function in R to compute correlations on a data frame with numeric values, users often encounter results where all entries are either 1 or NA. This can be confusing, especially for beginners. In this article, we delve into the reasons behind this behavior and provide practical solutions.
Default Behavior of cor()
The cor() function in R calculates the correlation coefficient between variables. By default, it requires complete cases for computation. If any variable contains missing values (NA), the correlation for pairs involving that variable is set to NA. This is because the standard formula for correlation relies on non-missing data points to ensure accuracy.
Why Diagonal Entries Are 1
In the correlation matrix, the diagonal entries represent the correlation of a variable with itself, which is always 1, indicating perfect positive correlation. This is a mathematical certainty and not an issue.
Handling Missing Values with the use Argument
To address the NA results, R provides the use argument in the cor() function. Common options include:
use = "complete.obs": Uses only complete observations, omitting any rows with NA.use = "pairwise.complete.obs": Computes correlations using pairwise complete cases.- Other options like
"everything"or"na.or.complete"are also available; refer to the documentation for details.
For example, to compute correlation while ignoring NAs, you can use:
cor(data$price, data$exprice, use = "complete.obs")Or for a full matrix:
cor(data, use = "complete.obs")Additional Considerations: Zero Variance
Another scenario that can lead to NA in correlation is when a variable has zero variance, meaning all values are identical. In such cases, the standard deviation is zero, making the correlation computation undefined. R issues a warning in this situation. For instance:
cor(cbind(a = runif(10), b = rep(1, 10)))This will produce a matrix with NA for the correlation involving variable b, and a warning message about zero standard deviation.
Best Practices
When working with correlation analyses in R, it's essential to inspect your data for missing values and zero-variance variables. Use the use argument appropriately based on your analysis goals. Additionally, consider data imputation or other methods to handle missing data if necessary.
Conclusion
Understanding why cor() returns NA or 1 is crucial for accurate statistical analysis in R. By leveraging the use parameter and being aware of data issues like missing values and zero variance, users can obtain meaningful correlation results.