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This is not the usual R vs Python post you can find online, in fact I won’t discuss whether one is better than the other. I will show to you why a learner who wants to learn data science will have an advantage by starting with R.

## Vectors

What are vectors? If you know matrices, you know vectors. They can be seen as rows or columns of matrices, so what we have is a one-dimensional “list” of numbers. Usually vectors are used as columns for data frames, that is because we are sure that in a column we have data of the same type.

Float, integer, string, categorical, etc a vector has always only one type. This is important because we can make our code faster and clearer: the interpreter will have to check the type of the first record and that’s it. As you may know in R vectors are native, actually even a scalar is a vector.

vec <- c(5, 3, 4)

class(vec)
[1] "numeric"

class(3)
[1] "numeric"

## Vectorization

When performing data analysis or machine learning, I will often work with data in a tabular format, or at a lower level, with a series of vectors. If I want to multiply every record in a vector by 2 it’s pretty natural to do:

vec * 2
[1] 10  6  8

In Python you can use lists to store your vectors, so let’s try the same with Python 3 (the fact you have to worry about 2 vs 3 is all another issue)

>>> [5, 3, 4] * 2
[5, 3, 4, 5, 3, 4]

WAT…

It turns out that the only way to get the same result in native Python is to perform a for loop:

>>> for num in [5, 3, 4]:
...     num * 2
...
10
6
8

You may want to store the result in a list as the input, so you have to initialize an empty list out of the loop and append results to it:

>>> res = []
>>> for num in [5, 3, 4]:
...     res.append(num * 2)
...
>>> print(res)
[10, 6, 8]

The same code in R would be:

vec <- c(5, 3, 4) * 2
vec
[1] 10  6  8

I would stress that it isn’t much about less typing, but more about the formation of the “right” mental model. Many people complain because their R code is slow, 99% of the time this is because they didn’t vectorize their code by coding “Python style” with loops, either hidden or explicit.

## Random Walk Example

We will perform a random walk in R and Python, for the latter the examples are taken from “From Python to NumPy” book.

Let’s start from the most basic approach by looping:

>>> import random # random module needed

>>> def random_walk(n):
...     position = 0  # initialize the position variable
...     walk = [position]  # initialize a list
...     for i in range(n):
...         position += 2*random.randint(0, 1)-1 # update position value
...         walk.append(position)  # append results to walk list
...     return walk
...

This code can get slow for very large objects, we can improve it by using the itertools module:

>>> from itertools import accumulate
>>> import random

>>> def random_walk_faster(n=1000):
...     steps = random.sample([1, -1]*n, n)
...     return list(accumulate(steps))
...

Anyway, this isn’t vectorized yet. It’s just a more efficient way to loop. To reach full vectorization we need NumPy:

>>> import numpy as np

>>> def random_walk_fastest(n=1000):
...     steps = 2*np.random.randint(0, 2, size=n) - 1
...     return np.cumsum(steps)
...

Take a close look at the methods derived from NumPy.

The same R code:

rw <- cumsum(sample(c(-1, 1), 1000, TRUE))

No imports, no real need to define a function or a method, code packed in one line.

## Conclusion

If you want to be a data “something”, or if you want to teach someone start with R. After reaching confidence with R, start with Python.

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