Codeavail experts will help you to understand which programming language (R or Python) is better for use in Machine Learning.

R or Python

What is Python?

Python is a general-purpose interpreted interactive object-oriented and high-level programming language.

  • It was first introduced in 1991 by Guido van Rossum, a Dutch computer programmer.
  • The language places a strong emphasis on code reliability and simplicity so that the programmers can develop applications rapidly.
  • Python is a multi-paradigm programming language, which allows the user to code in several different programming styles.
  • Python supports cross-platform development and is available through open source.
  • Python is widely used for scripting in Game menu applications effectively.
  • Python can be easily integrated with C/C++, CORBA, ActiveX, and Java.
  • CPython is a python integrated with C/C++ language.
  • Similarly, JPython is a purely integrated language where Java and Python code can interchangeably use inline in the program.
  • Need any Python Programming Assignment Help, submit your requirements to our experts.

Advantages of Python

  • Most programs in Python requires considerably less number of lines of code to perform the same task compared to other languages like C. So, less programming errors and reduces the development time needed also.
  • Though Perl is a powerful language, it is highly syntax oriented. Similarly, C also.

What is R?

R is a language and environment for statistical computing and graphics. It is a GNU project which is similar to the S language and environment which was developed at Bell Laboratories (formerly AT&T, now Lucent Technologies) by John Chambers and colleagues. R can be considered as a different implementation of S. There are some important differences but much code written for S runs unaltered under R.

R provides a wide variety of statistical (linear and nonlinear modeling, classical statistical tests, time-series analysis, classification, clustering,…..) and graphical techniques, and is highly extensible. The S language is often the vehicle of choice for research in statistical methodology, and R provides an Open Source route to participation in that activity.

One of R’s strengths is the ease with which well-designed publication-quality plots can be produced, including mathematical symbols and formulae where needed. Great care has been taken over the defaults for the minor design choices in graphics, but the user retains full control.

R is available as Free Software under the terms of the Free Software Foundation’s GNU General Public License in source code form, it compiles and runs on a wide variety of UNIX platforms and similar systems (including FreeBSD and Linux), Windows and MacOS.

The R environment

R is an integrated suite of software facilities for data manipulation, calculation, and graphical display. It includes

An effective data handling and storage facility,

A suite of operators for calculations on arrays, in particular, matrices,

A large, coherent, integrated collection of intermediate tools for data analysis,

Graphical facilities for data analysis and display either on-screen or on hardcopy, and

A well-developed, simple and effective programming language which includes conditionals, loops, user-defined recursive functions and input, and output facilities.

The term “environment” is intended to characterize it as a fully planned and coherent system, rather than an incremental accretion of very specific and flexible tools, as is frequently the case with other data analysis software.

R, like S, is designed around a true computer language, and it allows users to add additional functionality by defining new functions. Much of the system is itself written in the R dialect of S, which makes it easy for users to follow the algorithmic choices made. For computationally-intensive tasks. C, C++ and Fortran code can be linked and called at run time. Advanced users can write C code to manipulate R objects directly.

Many users think of R as a statistics system. We prefer to think of it as an environment within which statistical techniques are implemented. R can be extended (easily) via packages. There are about eight packages supplied with the R distribution and many more are available through the CRAN family of Internet sites covering a very wide range of modern statistics.

R has its own LaTeX-like documentation format, which is used to supply comprehensive documentation, both online in a number of formats and in hardcopy.

If you are looking for the R programming assignment help at the cheapest price, submit your requirements or details now.


  • R is the most comprehensive statistical analysis package available. It incorporates all of the standard statistical tests, models, and analyses, as well as providing a comprehensive language for managing and manipulating data.
  • R is a programming language and environment developed for statistical analysis by practicing statisticians and researchers.
  • The graphical capabilities of R are outstanding, providing a fully programmable graphics language that surpasses most other statistical and graphical packages.
  • R is free and open-source software, allowing anyone to use and, importantly, to modify it. R is licensed under the GNU General Public License, with the copyright held by the R Foundation for Statistical Computing.
  • R has over 4800 packages available from multiple repositories specializing in topics like analysis, and bioinformatics.
  • R is cross-platform. R runs on many operating systems and different hardware. It is popularly used on GNU/Linux, Macintosh, and Microsoft Windows, running on both 32 and 64-bit processors.

Which language is better to use for machine learning (R or Python)?

R and Python are the two most popular programming languages used by data analysts and data scientists. Both are free and open-source and were developed in the early 1990s-R for statistical analysis and Python as a general-purpose programming language. For anyone interested in machine learning, working with large datasets, creating complex data visualizations, they are godsends.

But which of these programs is best to learn? It is the question that has been asked more frequently. Though you could just try to learn both R and Python, each requires a significant time investment- particularly if you have never coded before.

Both Python and R are good it depends upon the interest of the person.

Python is better for data manipulation and repeated tasks, while R is good for ad hoc analysis and exploring datasets.

With Python when working on elections coverage, since it was a relatively routine, predictable process.

From pulling the data, to running automated analyses over and over, to producing visualizations like maps and charts from the results, Python was the better choice. “ Whereas in R, you would have to switch to a different tool to create the website and automate the process, but Python also works well for those things”.

R, by contrast, is good for statistics-heavy projects and one-time dives into a dataset. Take text analysis, where you want to deconstruct paragraphs into words or phrases and then identify patterns.

R has a steep learning curve, and people without programming experience may find it overwhelming.

Python is generally considered easier to pick up.

Another advantage of Python is that it is a more general programming language: For those interested in doing more than statistics, this comes in handy for building a website or making sense of command-line tools.

The way Python works reflects the way computer programming think. R, on the other hand, reflects it’s origins in statistics. Many programmers find the design of R irritating because it’s so different from what they’re used to. For someone interested in becoming a general-purpose programmer, Python is a better choice.

But for data analysis, the differences between R and Python are starting to break down. Most of the common tasks once associated with one program or the other are now doable in both. They are similar enough, in fact, that if most of your colleagues are already using R or Python, you should probably just pick up that language.

So the great R-versus-Python debate is settled. If all you’re doing is data analysis, it doesn’t really matter which one you use.

Also, read…

How to Get Good Grades

Java vs Kotlin​​​​​​​



Now you know better which programming language is better for use in machine learning. If you need any programming assignment help related to both languages, hire our computer science assignment help or computer science homework help experts at an affordable price. Submit your requirements to codeavail experts now.

Leave a Reply

Fill in your details below or click an icon to log in: Logo

You are commenting using your account. Log Out /  Change )

Google photo

You are commenting using your Google account. Log Out /  Change )

Twitter picture

You are commenting using your Twitter account. Log Out /  Change )

Facebook photo

You are commenting using your Facebook account. Log Out /  Change )

Connecting to %s