Understanding Eval and Environments in R: A Deep Dive into the Mystery of Missing Variables

In R, eval() is a powerful function that allows you to evaluate expressions within the context of an environment. However, when working with environments and variables, there can be unexpected behavior and errors. In this article, we will delve into the world of eval and environments in R, exploring why eval() cannot find a variable defined in the environment where it evaluates the expression.

Introduction to Eval and Environments

In R, an environment is a collection of variables and functions that are accessible within a specific scope. When you call eval(), R creates a new execution context, which allows you to evaluate expressions within that context. The environment in which eval() is called determines the visibility of variables and functions.

For example, if we have the following code:

# Define an environment with a variable 'x'
env <- new.env()
env$x <- 5

# Create a new function that uses eval() to access 'x'
my_func <- function() {
  # Create a new expression using eval() and env$
  expr <- paste("x + ", env$x, sep = "+")
  
  # Evaluate the expression
  result <- eval(expr)
  
  return(result)
}

# Call my_func()
print(my_func())  # Output: [1] 6

In this example, eval() is used to evaluate an expression within the context of env$. The variable x is defined in env$, and its value (5) can be accessed using eval(). When we call my_func(), it creates a new expression that uses eval()to access the variablex` from the environment.

The Problem: Missing Variables

Now, let’s examine the code provided in the Stack Overflow question. We have a function cpdist() from the bnlearn package, which takes a logical expression as an argument. In our example:

# Create a new data frame with variables 'A', 'C'
new.data <- data.frame(A = c("a", "b", "a", "b"), C = c("a", "a", "b", "b"))

# Define the function getPosterior()
getPosterior <- function(fitted, target, ev.nodes, ev.values) {
  # Create a new expression using paste() and sapply()
  ev <- paste("(", ev.nodes, "=='",
              sapply(ev.values, as.character), "')",
              sep = "", collapse = " &amp; ")

  # Use eval(parse(text = ev)) to evaluate the expression
  posterior <- cpdist(fitted, "D", eval(parse(text = ev)))

  return(posterior)
}

# Call getPosterior() with new.data
for(i in 1:nrow(new.data)) {
  print(getPosterior(fitted, "D", names(new.data), new.data[i, ]))
}

Here’s where things start to go wrong. When we remove the variable ev from the global environment, the function getPosterior() cannot find it anymore.

Why Can’t Eval() Find the Variable?

The issue here is that when we assign a value to ev inside getPosterior(), it creates a new local variable ev. However, this local variable ev does not shadow the global variable ev. The function is trying to access the global variable ev from outside its own scope, which is why it fails.

In R, when you assign a value to an object using <-, it creates a new binding for that object in the current environment. However, this binding only exists within that specific environment and does not affect other environments.

To fix this issue, we need to make sure that ev has the correct visibility within the scope of getPosterior(). One way to do this is by using the local() function to create a new local variable ev within the function’s scope.

# Define the function getPosterior()
getPosterior <- function(fitted, target, ev.nodes, ev.values) {
  # Create a new expression using paste() and sapply()
  local(ev)
  ev <- paste("(", ev.nodes, "=='",
              sapply(ev.values, as.character), "')",
              sep = "", collapse = " &amp; ")

  # Use eval(parse(text = ev)) to evaluate the expression
  posterior <- cpdist(fitted, "D", eval(parse(text = ev)))

  return(posterior)
}

By using local(), we create a new local variable ev within the scope of getPosterior(). This ensures that ev has the correct visibility and can be accessed by eval().

Conclusion

In this article, we explored the mystery of missing variables in R when working with eval and environments. We discovered that when using eval(), R creates a new execution context, which allows you to evaluate expressions within that context. However, this context is limited to the scope of the function call, and variables defined in other environments may not be accessible.

To fix issues like this, we need to understand how environments work in R and use functions like local() to create new local bindings for variables within a specific scope. By doing so, we can ensure that our code behaves as expected and avoid unexpected behavior due to missing variables.

Additional Resources

Last modified on 2024-10-28