Think Python AND R and not just PYTHON OR R: Creating vectors


In this post I will talk a little bit about how python and R work with vectors. I am using python 2.7.13 and R 3.4.3, both 64-bit on a Ubuntu 16.04 and I am also using the free book called A Hands-On Introduction to Using Python in the Atmospheric and Oceanic Sciences by prof. Johnny Lin as a guide.

Creating vectors

The first thing about a vector is how to create one. There are several ways to create a vector in R. For example, if one needs to create a logical vector:

a = vector(mode = "logical", length = 5)
b = c(FALSE, FALSE, FALSE, FALSE, FALSE)
c = rep(FALSE,5)
## [1] FALSE FALSE FALSE FALSE FALSE
## [1] FALSE FALSE FALSE FALSE FALSE
## [1] FALSE FALSE FALSE FALSE FALSE

or a numerical vector:

a = vector(mode = "numeric", length = 5)
b = c(0, 0, 0, 0, 0)
c = rep(x=0, 5)
## [1] 0 0 0 0 0
## [1] 0 0 0 0 0
## [1] 0 0 0 0 0

The vector() function produces a vector of the given length and mode, the c() function is a generic function which combines its arguments and rep() function replicates the values in x also returning a vector.

In python we can use the numpy package which has lots of methods to create and manipulate arrays/vectors. Thus importing the package numpy and generating the same vectors in python:

import numpy as np
a = np.array([False,False,False,False])
b = np.full(4, False, bool)
print a 
print b
## [False False False False]
## [False False False False]

or a numerical vector:

import numpy as np
a = np.array([0,0,0,0])
b = np.full(4, 0, float)
print a
print b
## [0 0 0 0]
## [ 0.  0.  0.  0.]

Why are they different? Remember the dynamical typing? Vector a is a vector of integers and b is a vector of float. The attribute dtype gives the data-type of the array’s elements:

c = np.array([0.0,0.0,0.0,0.0])
print a.dtype  
print b.dtype
print c 
print c.dtype
## int64
## float64
## [ 0.  0.  0.  0.]
## float64

Indexing Vectors

One major difference between python and R is how they address the element in the vector. In python the element addresses start with zero, so the first element of vector a is a[0], the second is a[1], etc.

import numpy as np
d = np.array(range(1,5))
print d
print d[0], d[3] 
## [1 2 3 4]
## 1 4

In R the element addresses follows the ordinal value thus starting from one. Consequently the first element of vector a is a[1], the second is a[2], etc.

d = seq(4)
print(d)
cat(d[1], d[4], sep=" ")
## [1] 1 2 3 4
## 1 4

Be careful!!!!

Python and R have the same method range() but they do different things. In python range() returns a list containing an arithmetic progression of integers. range(i, j) returns ([i, i+1, i+2,\ldots , j-1]) and the default is i=0.

f = range(5)
g = range(2,5)
print f
print g
## [0, 1, 2, 3, 4]
## [2, 3, 4]

In R the methods similar to range() are seq(), seq_along(), seq_len() (please check the R documentation to see the differences between them) which generates regular sequences. However the default starting value is 1.

f = seq(5)
g = seq(2,5)
print(f)
print(g)
## [1] 1 2 3 4 5
## [1] 2 3 4 5

The method range() in R returns a vector containing the minimum and maximum of all the given arguments.

range(f)
range(5)
## [1] 1 5
## [1] 5 5

If you have any question, suggestion or opinion about this post please feel free to write a comment below.

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  1. Reblogged this on Stack of Post.

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