MyPage is a personalized page based on your interests.The page is customized to help you to find content that matters you the most.

I'm not curious

R Vs Python - Which One Is The Best For Data Analysis?

Published on 31 August 18

R and PYTHON both are popular programming languages used for statistics. Now let us have a brief analysis on the topic that which one is better for data analysis.

Truth is that both are winners, they both have advantages and disadvantages and selection of one over another depends on some parameters like usage, tools required and cost of its training.

It is an open source language created in 1995 by Ross Ihaka and Robert Gentleman. R is considered as an implementation of the programming language. R programming language was designed with the purpose of keeping statistics in mind. It delivers a better way to do data analysis and thus is the most often used statistical language. R provides a great support system because of its large community and can be easily accessed for its techniques and functionalities. It takes time to learn how to use R but once understood it won’t take much time to use it. R is usually used only in data analysis tasks like standalone computing because it is only a general purpose language.

There are various tools for using R like-
  • Download and install R studio.
  • For manipulating packages install data. Table, dplyr, and plyr.
  • For manipulation of strings- stringr
  • For data visualization of data- ggvis, ggplot2, rcharts, googlevis and lattice.
As mentioned above there are various tools for visualizing data, as R is known for effectively converting raw numbers into a picture.

Other advantages of R are-
  • A rich ecosystem- R has an active support community. R packages can be searched easily at GitHub, CRAN etc.
  • For statisticians- it is mostly adopted in data science environment as it is made for this purpose only.
  • It is a free open source code license
  • Anyone can access the R source codes and modify them.

There are some disadvantages of R. Is slow to use due to poorly written code, R packages are hard to find and it is a time-consuming process, R has a steep non- trivial learning curve etc.

python was designed to enhance code readability and productivity. It was launched in the year 1991 (earlier than R) by Guido van rossem. Python is also mainly used for statistical purposes but also finds wide applications like in the web development industry, scientific computing etc. It is often used for its syntax that is easy-to-understand. Python also includes packages. It has a scattered community. Python can be used for many purposes like implementing algorithms, integrating data analysis tasks with web applications, incorporation of codes into production database etc.

Python tools include -
  • For making it usable- download scipy and pandas.
  • For making graphics- install matplotlib
  • For machine learning – scikit- learn

Advantages of python are-

  • Relatively slower learning curve.
  • It is a flexible and simple language.
  • Easy sharing of files.
  • Python is an easy programming language to understand
  • Faster speed
  • Python framework ensures code is reusable.
  • A single tool can integrate multiple languages
  • Visualization is possible easily

Disadvantages of python include its slow speed, it is not meant for mobile applications and generally not safe. There are many errors that can occur with this software.

Both R and python has its pros and cons. So the choice is ultimately yours. We can only guide you but one can be chosen over the other based on the fact which task you are about to perform as both the languages are equally good. A decision should be made keeping the factors in mind like resources required, learning cost and need of the tasks that can be performed.
This blog is listed under Open Source , Development & Implementations and Data & Information Management Community

Related Posts:
Post a Comment

Please notify me the replies via email.

  • We hope the conversations that take place on will be constructive and thought-provoking.
  • To ensure the quality of the discussion, our moderators may review/edit the comments for clarity and relevance.
  • Comments that are promotional, mean-spirited, or off-topic may be deleted per the moderators' judgment.
You may also be interested in
Awards & Accolades for MyTechLogy
Winner of
Top 100 Asia
Finalist at SiTF Awards 2014 under the category Best Social & Community Product
Finalist at HR Vendor of the Year 2015 Awards under the category Best Learning Management System
Finalist at HR Vendor of the Year 2015 Awards under the category Best Talent Management Software
Hidden Image Url