# The Call Stack

## Overview

Teaching: 15 min
Exercises: 0 min
Questions
• What is the call stack, and how does R know what order to do things in?

• How does scope work in R?

Objectives
• Explain how stack frames are created and destroyed as functions are called.

• Correctly identify the scope of a function’s local variables.

• Explain variable scope in terms of the call stack.

### The Call Stack

Let’s take a closer look at what happens when we call `fahrenheit_to_kelvin(32)`. To make things clearer, we’ll start by putting the initial value 32 in a variable and store the final result in one as well:

``````original <- 32
final <- fahrenheit_to_kelvin(original)
``````

The diagram below shows what memory looks like after the first line has been executed: When we call `fahrenheit_to_kelvin`, R doesn’t create the variable `temp_F` right away. Instead, it creates something called a stack frame to keep track of the variables defined by `fahrenheit_to_kelvin`. Initially, this stack frame only holds the value of `temp_F`: When we call `fahrenheit_to_celsius` inside `fahrenheit_to_kelvin`, R creates another stack frame to hold `fahrenheit_to_celsius`’s variables: It does this because there are now two variables in play called `temp_F`: the argument to `fahrenheit_to_celsius`, and the argument to `fahrenheit_to_kelvin`. Having two variables with the same name in the same part of the program would be ambiguous, so R (and every other modern programming language) creates a new stack frame for each function call to keep that function’s variables separate from those defined by other functions.

When the call to `fahrenheit_to_celsius` returns a value, R throws away `fahrenheit_to_celsius`’s stack frame and creates a new variable in the stack frame for `fahrenheit_to_kelvin` to hold the temperature in Celsius: It then calls `celsius_to_kelvin`, which means it creates a stack frame to hold that function’s variables: Once again, R throws away that stack frame when `celsius_to_kelvin` is done and creates the variable `temp_K` in the stack frame for `fahrenheit_to_kelvin`: Finally, when `fahrenheit_to_kelvin` is done, R throws away its stack frame and puts its result in a new variable called `final` that lives in the stack frame we started with: This final stack frame is always there; it holds the variables we defined outside the functions in our code. What it doesn’t hold is the variables that were in the various stack frames. If we try to get the value of `temp_F` after our functions have finished running, R tells us that there’s no such thing:

``````temp_F
``````
``````Error in eval(expr, envir, enclos): object 'temp_F' not found
``````

The explanation of the stack frame above was very general and the basic concept will help you understand most languages you try to program with. However, R has some unique aspects that can be exploited when performing more complicated operations. We will not be writing anything that requires knowledge of these more advanced concepts. In the future when you are comfortable writing functions in R, you can learn more by reading the R Language Manual or this chapter from Advanced R Programming by Hadley Wickham. For context, R uses the terminology “environments” instead of frames.

Why go to all this trouble? Well, here’s a function called `span` that calculates the difference between the minimum and maximum values in an array:

``````span <- function(a) {
diff <- max(a) - min(a)
return(diff)
}

# span of inflammation data
span(dat)
``````
`````` 20
``````

Notice `span` assigns a value to variable called `diff`. We might very well use a variable with the same name (`diff`) to hold the inflammation data:

``````diff <- read.csv(file = "data/inflammation-01.csv", header = FALSE)
# span of inflammation data
span(diff)
``````
`````` 20
``````

We don’t expect the variable `diff` to have the value 20 after this function call, so the name `diff` cannot refer to the same variable defined inside `span` as it does in the main body of our program (which R refers to as the global environment). And yes, we could probably choose a different name than `diff` for our variable in this case, but we don’t want to have to read every line of code of the R functions we call to see what variable names they use, just in case they change the values of our variables.

The big idea here is encapsulation, and it’s the key to writing correct, comprehensible programs. A function’s job is to turn several operations into one so that we can think about a single function call instead of a dozen or a hundred statements each time we want to do something. That only works if functions don’t interfere with each other; if they do, we have to pay attention to the details once again, which quickly overloads our short-term memory.

## Following the Call Stack

We previously wrote functions called `highlight` and `edges`. Draw a diagram showing how the call stack changes when we run the following:

``````inner_vec <- "carbon"
outer_vec <- "+"
result <- edges(highlight(inner_vec, outer_vec))
``````

## Key Points

• R keeps track of active function calls using a call stack comprised of stack frames.

• Only global variables and variables in the current stack frame can be accessed directly.