Dormammu, I’ve Come to Iterate: R vs. Python in the Infinite Loop

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Numbers around us

Published

February 4, 2025

Dormammu, I’ve Come to Iterate: R vs. Python in the Infinite Loop

“Dormammu, I’ve come to bargain!” But instead of an endless struggle, what if we could break the loop with the right approach? In programming, iteration is an unavoidable part of working with data—whether it’s looping through rows in a dataset, applying transformations, or optimizing performance. Like Doctor Strange’s battle against Dormammu, iteration can either be a brute-force, time-consuming process or a carefully optimized strategy.

In this article, we’ll explore how R and Python approach iteration differently. R, like Strange, often relies on clever, optimized solutions (vectorization, apply(), and functional programming) to break out of the loop quickly. Python, on the other hand, often embraces explicit control (for loops, list comprehensions), giving developers more flexibility but sometimes at a cost of efficiency. Which approach is better? And how can we iterate smarter instead of harder? Let’s step into the loop and find out.

1. Looping in R vs. Python: The First Approach to the Time Loop

Doctor Strange didn’t immediately master the time loop—he started with trial and error, just like how programmers first learn iteration through loops. In both R and Python, for and while loops are the fundamental tools for repeating operations. However, their implementations and efficiency differ significantly.

For Loops: A Structured Approach

In both languages, for loops allow us to iterate over a collection of elements. Here’s a direct comparison:

for (i in 1:5) {
  print(i)
}

for i in range(1, 6):
    print(i)

At first glance, they seem similar. However, R’s for loop is not the most efficient tool for iteration. Unlike Python, which optimizes looping at the interpreter level, R was designed with vectorization in mind—meaning loops can often be replaced with functions that operate on entire datasets at once.

While Loops: Holding the Loop Until Conditions Change

Another way to iterate is using while loops, where repetition continues until a condition is met—just like Strange’s infinite time loop, except with a clear exit strategy.

x <- 1
while (x <= 5) {
  print(x)
  x <- x + 1
}

x = 1
while x <= 5:
    print(x)
    x += 1

Again, both languages execute this in a similar way, but in R, using while loops for large-scale operations is rarely the best choice. Instead, vectorized operations and apply functions are preferred for performance reasons.

Loop Efficiency: Who’s Stuck in the Time Loop?

If iteration is unavoidable, how do these loops perform when handling larger datasets? Let’s compare execution times for looping over 1 million elements:

R (slower approach):

library(tictoc)
tic("R for loop")
for (i in 1:1e6) {
  x <- i * 2
}
toc()

Python (more optimized loops):

import time
start = time.time()
for i in range(int(1e6)):
    x = i * 2
end = time.time()
print("Execution time:", end - start)

Python’s compiled nature allows it to handle loops more efficiently, while R’s interpreter struggles with raw iteration. However, the real power of R lies beyond loops—in vectorized operations and functional programming.

💡 Key Takeaway:

Python’s for and while loops are well-optimized and often necessary.
R’s loops are slower and should be avoided for large data processing tasks.
The best approach? In R, avoid explicit loops and use vectorized operations whenever possible.

2. Iterating Over Data Structures: Lists, Data Frames, and More

Doctor Strange’s loop against Dormammu wasn’t just about repeating the same thing—it was about adapting. Similarly, iteration isn’t always about looping over numbers. In real-world programming, we often need to iterate over lists, data frames, and other complex data structures. This is where the differences between R and Python become more pronounced.

Iterating Over Lists: Different Paths to the Same Outcome

Lists are a fundamental data structure in both R and Python. Let’s compare how iteration works in each language:

Basic List Iteration

R (using a for loop):

my_list <- list("apple", "banana", "cherry")

for (item in my_list) {
  print(item)
}

Python (for loop on a list):

my_list = ["apple", "banana", "cherry"]

for item in my_list:
    print(item)

✅ Observation: Python’s iteration is more natural for lists, while R’s for loop is functional but not the most efficient.

Iterating Over Data Frames: A Major Difference

One of the biggest differences between R and Python is how they handle data frames (data.frame in R, pandas.DataFrame in Python). In Python, explicit iteration is common, while in R, iteration is usually avoided in favor of vectorized operations or apply() functions.

Looping Over Data Frames (Not Recommended in R)

🔴 Inefficient R approach:

df <- data.frame(name = c("Alice", "Bob", "Charlie"), age = c(25, 30, 35))

for (i in 1:nrow(df)) {
  print(paste(df$name[i], "is", df$age[i], "years old"))
}

✅ Python’s more natural approach:

import pandas as pd

df = pd.DataFrame({"name": ["Alice", "Bob", "Charlie"], "age": [25, 30, 35]})

for _, row in df.iterrows():
    print(f"{row['name']} is {row['age']} years old")

💡 Key Takeaway: In Python, looping through a data frame is natural with iterrows(), but in R, loops over data frames are slow and should be avoided.

Efficient Data Frame Iteration in R: `apply()` and Friends

Since R is optimized for vectorized operations, a better way to iterate over data frames is using apply() functions, which work much faster than loops.

✅ R’s optimized approach using apply():

apply(df, 1, function(row) paste(row["name"], "is", row["age"], "years old"))

✅ R’s dplyr approach (even better!):

library(dplyr)

df %>% mutate(info = paste(name, "is", age, "years old")) %>% pull(info)

✅ Python’s alternative using apply():

df["info"] = df.apply(lambda row: f"{row['name']} is {row['age']} years old", axis=1)
print(df["info"])

💡 Key Takeaway:

Python naturally supports row-wise iteration with iterrows(), but apply() is often faster.
R should avoid loops over data frames—use apply(), mutate(), or vectorized solutions instead.

Final Verdict: Who Wins the Iteration Battle?

Feature	R (Base)	R (Optimized)	Python
List Iteration	✅ Works but slow	✅ Works but slow	✅ Natural and fast
Data Frame Loops	❌ Slow & not recommended	✅ `apply()`, `dplyr`	✅ `iterrows()`, `apply()`
Best Approach	🚀 Avoid loops, use vectorization	🚀 `apply()` & `mutate()`	🚀 `apply()` or `iterrows()`

3. Functional Programming Alternatives: Breaking the Loop Smarter

Doctor Strange didn’t just brute-force his way through Dormammu—he found a smarter way to escape the infinite loop. Likewise, instead of using traditional loops, both R and Python offer functional programming techniques that make iteration faster, cleaner, and more efficient.

In this chapter, we’ll compare how R’s apply() family and purrr package stack up against Python’s list comprehensions and map() function.

Replacing Loops with `apply()` in R

One of the most powerful ways to avoid explicit loops in R is by using the apply() family of functions, which allow you to apply a function to every element of a data structure.

Iterating Over a Vector: sapply() vs. List Comprehension

R (sapply() - functional programming)

numbers <- 1:5
sapply(numbers, function(x) x^2)  # Squaring each element

Python (List comprehension - more concise)

numbers = [1, 2, 3, 4, 5]
[x**2 for x in numbers]  # Squaring each element

💡 Key Takeaway: Python’s list comprehensions are more concise than R’s sapply(), but both serve the same purpose: replacing explicit loops with functional operations.

Mapping Over Lists: `lapply()` vs. `map()`

When dealing with lists instead of vectors, R’s lapply() and Python’s map() function behave similarly.

R (lapply() - returns a list):

my_list <- list(1, 2, 3, 4, 5)
lapply(my_list, function(x) x^2)

Python (map() - returns an iterator):

my_list = [1, 2, 3, 4, 5]
list(map(lambda x: x**2, my_list))

💡 Key Takeaway: map() in Python and lapply() in R both apply a function to each element, but map() returns an iterator that must be converted to a list.

Advanced Functional Programming: The Power of `purrr::map()`

The purrr package in R extends the apply() family by offering even more functional flexibility.

✅ Best purrr equivalent to map() in R:

library(purrr)
my_list <- list(1, 2, 3, 4, 5)
map(my_list, ~ .x^2)  # Shorter syntax than lapply()

✅ Python equivalent using map():

from functools import partial

squared = map(lambda x: x**2, my_list)
list(squared)

💡 Key Takeaway:

purrr::map() is more powerful and readable than base R’s lapply().
Python’s map() is effective but often less readable than list comprehensions.

Final Verdict: Functional Programming for the Win!

Feature	R (`apply()` family)	R (`purrr::map()`)	Python (List Comprehension)	Python (`map()`)
Readability	⚠️ Medium	✅ High	✅ High	⚠️ Medium
Performance	✅ Fast	✅ Fast	✅ Fast	✅ Fast
Ease of Use	⚠️ OK	✅ Very easy	✅ Very easy	⚠️ Less readable
Best Use Case	Matrices, Data Frames	Lists, Data Frames	Lists, Iterables	Lists, Iterables

🚀 Recommendation:

Use purrr::map() over loops in R.
Use list comprehensions in Python whenever possible.
Avoid raw loops for performance reasons.

4. Vectorization: The True Escape from the Time Loop

Doctor Strange didn’t just loop indefinitely—he optimized the loop to achieve his goal with minimal effort. Likewise, the best way to iterate efficiently isn’t looping at all—it’s vectorization.

Vectorization allows operations to be applied to entire datasets at once, avoiding explicit iteration. In both R and Python, this is the fastest and most efficient way to process large amounts of data.

What is Vectorization?

Vectorization means applying an operation to an entire array, list, or column without using explicit loops. This is massively faster because the computation is optimized at a low level (C, Fortran, or optimized Python/R internals).

Traditional Loop (Slow)

numbers <- 1:5
for (i in numbers) {
  print(i * 2)
}

numbers = [1, 2, 3, 4, 5]
for i in numbers:
    print(i * 2)

Vectorized Approach (Fast!)

numbers * 2  # Directly multiplying the entire vector

import numpy as np
numbers = np.array([1, 2, 3, 4, 5])
print(numbers * 2)  # Directly multiplying the entire array

💡 Key Takeaway: Vectorized operations eliminate the need for loops, making them faster and more readable.

Final Verdict: Who Escapes the Time Loop?

Feature	R (Base)	R (`data.table`)	R (`dplyr`)	Python (NumPy)	Python (Pandas)
Vectorized Math	✅ Fast	✅ Fastest	✅ Fast	✅ Fastest	✅ Fast
Data Frame Ops	⚠️ OK	✅ Fastest	✅ Fast	✅ Fast	✅ Fast
Ease of Use	✅ Easy	⚠️ Learning Curve	✅ Easy	⚠️ Learning Curve	✅ Easy
Best Use Case	Vectors	Large Data	Tidy Workflow	Numerical Ops	Tabular Data

🚀 Recommendation:

In R, use data.table for speed or dplyr for readability.
In Python, prefer NumPy for numerical operations and Pandas for data frames.
Avoid loops for large datasets—vectorization is always faster!

6. Breaking Out of the Time Loop

Doctor Strange didn’t fight Dormammu by brute force—he outsmarted him. The key to efficient iteration isn’t looping endlessly; it’s knowing when to loop, when to map, and when to vectorize.

Through our journey, we’ve seen how R and Python approach iteration differently:

Loops (for, while) are fundamental but often inefficient for large-scale operations.
Functional programming (apply(), map()) provides cleaner, more efficient alternatives.
Vectorization (data.table, dplyr, NumPy, Pandas) is the ultimate performance booster.

When to Use What?

Scenario	Best for R	Best for Python
Basic iteration	`for` loop (but avoid for large data)	`for` loop (optimized, but still slow)
Applying functions to elements	`apply()`, `lapply()`, `purrr::map()`	List comprehensions, `map()`
Processing tabular data	`data.table`, `dplyr::mutate()`	Pandas `.apply()`, `.map()`
Mathematical operations	Vectorized base R, `data.table`	NumPy vectorized arrays
Text processing	`stringr`, `map()`	List comprehensions, `re` (regex)
Simulations & iterations over parameters	`replicate()`, `map()`	NumPy, list comprehensions

💡 General Advice:

Use loops sparingly. In both R and Python, loops should be a last resort for performance-heavy tasks.
Favor functional programming (apply(), map()). This approach is more readable and often faster.
When working with large datasets, always prefer vectorized solutions (data.table, NumPy).

Who Wins? R or Python?

The real answer: It depends.

If you work heavily with tabular data, R’s data.table and dplyr are incredibly efficient.
If you need fast numerical computations, Python’s NumPy is unmatched in speed.
If you value concise, readable iteration, Python’s list comprehensions feel more natural than R’s apply().
If you want functional programming with clean syntax, R’s purrr package is more expressive than Python’s map().

At the end of the day, both languages offer powerful tools for iteration. The key is choosing the right tool for the job.

Final Thought: Dormammu, We’ve Won the Iteration Battle

Iteration doesn’t have to feel like an endless fight. By understanding how R and Python handle iteration, we can break free from inefficient loops and embrace smarter, faster solutions.

So the next time you find yourself stuck in an iteration loop, remember:
🚀 There’s always a better way to iterate.

Dormammu, I’ve Come to Iterate: R vs. Python in the Infinite Loop

1. Looping in R vs. Python: The First Approach to the Time Loop

For Loops: A Structured Approach

While Loops: Holding the Loop Until Conditions Change

Loop Efficiency: Who’s Stuck in the Time Loop?

2. Iterating Over Data Structures: Lists, Data Frames, and More

Iterating Over Lists: Different Paths to the Same Outcome

Iterating Over Data Frames: A Major Difference

Efficient Data Frame Iteration in R: apply() and Friends

Final Verdict: Who Wins the Iteration Battle?

3. Functional Programming Alternatives: Breaking the Loop Smarter

Replacing Loops with apply() in R

Mapping Over Lists: lapply() vs. map()

Advanced Functional Programming: The Power of purrr::map()

Final Verdict: Functional Programming for the Win!

4. Vectorization: The True Escape from the Time Loop

What is Vectorization?

Final Verdict: Who Escapes the Time Loop?

6. Breaking Out of the Time Loop

When to Use What?

Who Wins? R or Python?

Final Thought: Dormammu, We’ve Won the Iteration Battle

Efficient Data Frame Iteration in R: `apply()` and Friends

Replacing Loops with `apply()` in R

Mapping Over Lists: `lapply()` vs. `map()`

Advanced Functional Programming: The Power of `purrr::map()`