How do you do random?

const random = 7;

Math.random() produces a random float (all JavaScript numbers are of type float) from 0 to 1.

Adding the requirement of 0-10 means you have to multiply by 10 or * 10.

The last requirement was the number must be an Integer, which is the math.floor.

Of course, if you only wanted a random float between 0 and 1, it would be just one call.

And in PHP if you wanted a random float between 0 and 1, you would also have to do more work: Random Float between 0 and 1 in PHP

So the complexity actually comes from the requirements you give it.

But asking for a random integer is a common task. So it's handy to create function and keep it in your library. Don't go sprinkling Math.floor(Math.random() * 10) randomly around your codebase :D

const pseudoRandomInteger = (start, end) =>
  start + Math.floor(Math.random() * (end - start))

P.S. I prefixed this with pseudo because there is no true Random in JavaScript. This is very important when it comes to cryptography. Google PRNG if you want to learn more.

For a cryptographically strong random value, you might want to look into Crypto.getRandomValues()

Cheers!

There is a subtle bug to consider with your code, though. If you want to guarantee an even spread among the numbers in the set of [0, 10], you'll have problems with 10 never showing up in your histogram. The reason for this is that the random value will almost never be exactly 1, as most PRNGs will actually output something in the range of [0, 1) (note the non-inclusiveness of 1 in the return values).

To correct that problem, you have to be a little smarter about how you map the [0,1] interval to buckets of integers:

const rand = (max) => Math.floor(Math.random() * (max + 1 - Number.EPSILON));

In the case of 10, you'll be multiplying a number in the [0,1] range to something that's just under 11. Number.EPSILON is the smallest float fraction that JavaScript numbers can express, so it's as close as the JavaScript number precision gets. And because the number 11 is never a value that appears in the output, Math.floor will never return anything above 10, guaranteed.

Of course, if your PRNG never returns 1 in its output, it's safe to just do max + 1. :)

Here's an illustration:

repl.it/@KrofDrakula/safeRandom

Python:

import random
random.randint(0, 10)

Ruby:

rand(11)

Go:

import "math/rand"

func main() {
    rand.Intn(11)
}

None of these are cryptographically secure though

Python has a few options, including the numpy module. Regardless of language, however, it often only takes a ~20 lines to define a pseudo-random (in numerical math we avoid saying "random") number generator method. Here is an implementation of the so-called Mersenne twister:

def rando(n, seed, S = [0, 10, 'integer']):
    i = int(16807)
    j = int(2147483647)
    u_list = []
    if S[2] == 'real':
        for k in range(n):
            seed = (i * seed) % j
            u = (S[1] - S[0]) * (float(seed) / float(j)) + S[0]
            u_list.append(u)
        return u_list
    elif S[2] == 'integer':
        for k in range(n):
            seed = (i * seed) % j
            u = int((S[1] - S[0] + 1) * (float(seed) / float(j)) + S[0])
            u_list.append(u)
        return u_list
    else:
        print('Set of numbers to draw from is undefined')

In this method, we declare two integers, i (16807 = 7^5), and j (the Mersenne prime). In specifying the arguments n (number of psuedo-random numbers desired), seed (for repeatability of the pseudo-random numbers), and S (min, max, types to be generated), what is repeatedly happening (depending on n) is that we are taking the remainder of (seed * i) and j, and dividing by j; over and over. This implementation of the Mersenne Twister is good for about 2³⁰ random numbers before it repeats itself. :)

APL:

⍝ `? n` generates random number between 1 and n, both inclusive
(? 10) -1
⍝ If we need more than 1 number, use `⍴` function like:
(? 4⍴ 10) -1
⍝ In above code, x⍴y gives you an array of y's of length x

I am a beginner in this language but it is fascinating at how succinct and elegant some constructs are.

Others responded on-topic, so I would add an off-topic :)

I do not build a JS project without Lodash (or something similar), it covers the basic needs and lacks of JS, acting like a Standard library in other languages. A few of them are related to random

_.random([lower=0], [upper=1], [floating])

_.sample(collection) //get a random element
_.sampleSize(collection, [n=1])

But, statistically speaking, taking a float random and truncating it to just a small integer range it's like buying a Ferrari for commuting, just saying :D Most of the times is a waste of resources.

Devs should be more careful at the distributions, most of the cases the code will be used for a few values and a small range of values, which will lead to very not-so-random results.

If the random results will affect the User Experience you should think twice of using it, the screwed distribution will affect your sales and KPIs in unknown ways, most likely negative, and there are always better alternatives, that require more resources to implement of course.

In PL/SQL:

A random number.

select DBMS_RANDOM.random from dual;
--Result: -860942438

A random round number.

select round(DBMS_RANDOM.random) from dual;
--Result: 1096364454

A random number between two values.

select round(DBMS_RANDOM.value(0, 100)) from dual;
--Result: 54

From a terminal can be(no for encrypt things):

> shuf -i 0-10 -n 1
4
>