Benchmark: Ramda map vs Array.map

HTML Preparation code:

<script src="//cdnjs.cloudflare.com/ajax/libs/ramda/0.25.0/ramda.min.js"></script>
<script src="//cdn.jsdelivr.net/npm/lodash@4.17.5/lodash.min.js"></script>

Script Preparation code:

function double(n) {
	return n*2;
}
var data = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30];

Tests:

Ramda
R.map(double, data);
Array (native)
data.map(double);
Lodash
_.map(data, double);

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
Ramda
Array (native)
Lodash

Fastest: N/A

Slowest: N/A

Latest run results:

No previous run results

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Autogenerated LLM Summary (model llama3.2:3b, generated one year ago):

LLMs can make mistakes. Check important info.

Let's dive into the details of this benchmark.

**What is being tested?**

The provided JSON represents a JavaScript microbenchmark that measures the performance of three different approaches:

1. Ramda's `map` function
2. The native `map` function in JavaScript (Array.prototype.map)
3. Lodash's `_map` function

Each test case is defined in the "Individual test cases" section, which includes:

* A benchmark definition that specifies the input data (`data`) and a transformation function (`double`)
* A test name for each approach being compared

**Options comparison**

Here are the options being compared:

1. Ramda's `map`: This uses a functional programming style to apply the `double` function to each element in the `data` array.
2. Array (native) `map`: This uses the native JavaScript method for mapping over arrays, which is also a functional programming style.
3. Lodash's `_map`: This uses Lodash's implementation of the `map` function, which is another functional programming approach.

**Pros and cons**

Here are some pros and cons of each approach:

1. Ramda's `map`:
	* Pros: Fast execution, concise code
	* Cons: May not be as familiar to developers who aren't used to functional programming
2. Array (native) `map`:
	* Pros: Widely supported, efficient use of JavaScript's built-in methods
	* Cons: Can be verbose and less readable for complex transformations
3. Lodash's `_map`:
	* Pros: Concise code, easy to read and maintain
	* Cons: May not be as fast as Ramda or native `map`, depending on the implementation

**Library usage**

Lodash is a popular JavaScript utility library that provides a wide range of functions for tasks like array manipulation, string manipulation, and more. In this benchmark, Lodash's `_map` function is used to apply the `double` transformation to the input data.

**Special JS feature/syntax**

This benchmark does not use any special JavaScript features or syntax. It only uses standard JavaScript methods and libraries like Ramda and Lodash.

**Other alternatives**

If you're interested in alternative approaches, here are a few options:

1. Vanilla JavaScript with `forEach` loop: Instead of using the native `map` function, you could write a custom implementation using a `forEach` loop.
2. A different library or framework's mapping function (e.g., jQuery's `map` function)
3. A custom implementation using a closure and a for loop

However, it's worth noting that these alternatives may not be as efficient or concise as the approaches used in this benchmark.

**Benchmark preparation code**

The provided "Script Preparation Code" includes:

* A simple transformation function (`double`) that doubles its input
* An input array (`data`) with 30 elements

The "Html Preparation Code" includes:

* Links to include Ramda and Lodash minified files

Overall, this benchmark provides a useful comparison of different approaches for mapping over arrays in JavaScript.

Related benchmarks:

Ramda map vs Array.map (version: 0)

measures the speed of ramda's map vs Array's native map

Comparing performance of: Ramda vs Array (native) vs Lodash

Created: 8 years ago by: Guest

Jump to the latest result

Ramda

Array (native)

Lodash

Suite status: <idle, ready to run>

Fastest: N/A

Slowest: N/A

No previous run results

Autogenerated LLM Summary (model llama3.2:3b, generated one year ago):