Basic Understanding:
Day by Day we are going ahead where we are facing
big data, it is critical to handle and visualize it in appropriate manner. Different
programming languages handles different kind of problem such as (C++, C#) for
desktop application, mobile application, web base application, but in the
domain of programming language R and Python are old languages R designed on August
1993; and Python in 1991 but its impressive libraries emphases us to use
it for big data (analysis, and predication) where we need to predict and
visualization using Artificial Intelligence, machine learning, deep learning
also for Data Science (statistical purpose).
In the usage war, both programming language waging
each other, action, rule and predication action these two has own importance
according to the modern era both language ratio in usage, in 2017 R highlighted
in the world by its usage and it unique libraries.
Introduction:
For statistical calculation and graphic R
programming language has been used. The R GNU based, which is similar to S
language, where it environment designed at Bell Laboratories (formerly
AT&T, now Lucent Technologies) by John Chambers and colleagues. Different designing
and it implementation of S can be considered as R. Both not be, same there are
many difference between S and R in other work. Also, R bestrew big range of
different statistical related modeling, classical statistical test, time s
series analysis, classification, clustering function etc…and related graphical
techniques, which are nightly extensible, whereas S provide the research
statistical methodology, and R provides the open source route to participation
in the activity.
For R, it is very easy to formulate the
mathematical symbols and drawing the plot using well designed publication.
These graphic design by default running possibilities present in window, Unix
and related platforms available on it.
R as Programming Language: It is the Open source
counterpart of SAS, traditionally been used in academics and research. It is
open source nature basically, latest techniques get released quickly. There is
a lot of documentation available over the internet and it is a very
cost-effective option.
Python as Programming Language: It is an open source scripting language
as like others, It is one of the growing
language. These days, it stunning libraries (scipy, numpy and matplotlib) and
functions for almost any statistical operation / model building you may want to
do. Also using of pandas showing the file handing in very smart way for data
handing and others related mathematical models work.
R is traditionally open source programming
language, which is not only used in data science field but also in different
field of life.
In term
of differences and opponent there are a lot of differences between them to
shows the grow of that language in the different field in this ear, such as
data science, machine learning, data science and data analyst, but In the
Article I am going to discuss TEN differences which effect to grow and
popularity reason of R and Python in the globe.
Let's understand these differences:
History
Python is an interpreted high-level programming
language for general-purpose programming, which has been created by Guido van
Rossum in 1991, The two version (2.0 and 3.0) of python create conflict between
the user, and from these two others sub version is going to created time to
time (2.7 or 3.2), right now the current latest version (as of Fall 2018) is
Python 3.6.4. Whereas in R is a programming language as like other it is
free environment for statistical computing and also support for graphic which
has been designed by Ross Ihaka and Robert Gentleman in 1995, the latest version of R which is
running in market is 3.5.1 which is
available on the official website of R to download.
Community
The main purpose of community to provide help
as quick service. There are different community available to provide stunning
performance to produce stage as community like on R website community, Stackoverflow, Mailing list, user contributed
code and documentation also different developer and programmer create own
community to held it up. Whereas in R community Mailing list, user contributed
code and documentation and Active stackoverflow members few data scientist and
statisticians also provide the huge community as compare to python which grow
more result then python popularity.
Purpose
Python always force on productivity and code
readability and usability because it is an Object oriented language. It is
English related keyword supported language (logical operator AND, OR AND NOT).
Also not only folks use it to make better programming also use it for gaming by
using PyGame library. Whereas in R, it is focus on Data to fix, predict,
analysis and deep concept of statistic and graphic model for graph related plotting.
Usability
As we discuss about the syntax of Python for
coding is easy because several keyword and writing tool related to English easy
word, that’s why running and debugging is easy. Also, Indentation of code effect
on it meaning, we must follow the pattern of python in term writing function
(in the predefine way). Where as in R statistic related model can be written in
few lines. Also, function can be written using several way as compare to
python. R is generally suitable for any type of data analysis. The numerous
number of packages and readily usable tests make starting any analysis quite
easy as computer to python.
Flexibility
In term
of flexibility of python, it is more flexibility, we do new any time on it
which is never did on before. Many user use it as scripting language also for
web development. Python use to analysis project is part of a bigger project
that involves many complexities easily. Where as in R, for data science in deep
many developer prefer to use R then Python due to its powerful libraries also
helpful to analysis complex data using huge list of R packages and statistical
model.
Ease of
learning
In the
field of computer programming, many developer prefer to build their logic
first, for this many computer scientist to learn first Python, that’s why in
different educational centers python has been taught as level first. For ease
in the python learning, readability and usability make it easier for basic and
easy to learn as compare to R. But also R is not hard for experience
programmer.
Job
Scenario
According
to the latest trends and report, R and Python are the competitive to each
other, Last TWO Year the Popularity of Python is grow up then R, but we cannot
ignore the popularity of R as well. Most of the companies follow the usage of R
and python both for big data handling and data analysis. Accordingly the jobs
setup change time to time in term of salary and also work on.
The jobs, ad in USA in 2017, through the
website have a look!
Search by "Data Scientist" finds
3,558 jobs
Search by "Data Scientist" Python
finds 2,407 jobs (68% of all)
Search by "Data Scientist" R finds
2,179 (61% of all).
Search by "Data Scientist" Python R
finds 1,906 jobs (54% of all) and
Search for "Data Scientist" -Python
-R finds 892 jobs (25% of all)
Also,
the job and salaries graphs demonstrate this senses is easy way: The first one
is showing the popularity of Python over R, second for Python as data science
also third one shows for the same in ratio in June of 2017.
Data
Handling
For data
handling I do not see any big differences between them which shows the realist
effect on any programming language. Both handle the data by using libraries,
Python use Numpy and Pandas to cover and handle the data
in easy and sufficient way, whereas in R we do not need to install libraries
for basic data handling but for big data and other advance work we need to
install it like data.table and dplyr and others.
IDE
support
IDE
means integrated development environment,
for support of programming language coding and environment to run we need
IDE according the related programming language like C/C++ Dev, C# Visual Studio. As like these For Python there
are different IDE to code few of
them which are most famous these days, Pycham,
IPython Notebook, Spyder and Rodeo, where as in R the famous one is
only RStudio.
Libraries
Library is the collection of classes which
provide the short way to code and get to required output using few line of
code. In Python there are different libraries in this Article we will discuss
main and famous of them, Pandas which is used to manipulate the date, Scipy and Numpy are used for scientific work and calculation. For graph we
used matplotlib, and using statsmodels we explore the data, create
the statistical modem and also perform different statistical test and unit
test. Whereas in R different packages play a vital rule to make R more
attractive in term of usage, dplyr, plyr and data.table are used to manipulate the date as like python, stringr is used to manipulate the string. Zoo not for (zoo) J but for working in regular
and irregular time series. Ggvis, lattice and ggplot2 are used to visualize the date and caret for working on machine learning.
These all are the few
differences which I observed
in the field and working by
Research!
I am hopeful it will be
informative for all of you!!!
