R vs Python for Data Science – Which is best for programming language ?

R vs Python for Data Science – Which is best for programming language?

Here we have to discuss about the use of python or “R” for the data science. The data scientist who writes the programming for data analysis and at the end of the article you came to know the strengths and weaknesses of both languages. Additionally, you will come to conclude which language is faster, reliable and good performance. Both the languages (“R” or “Python”) are used in the machine language, data science, finance and text mining.

“R” programming language:

IDE:  “R” Studio

Packages:  dplyr, plyr, and data.table to manipulate the data’s

stringr is used for strings

zoo is for the regular and irregular time series

caret for machine language

Applications:  Data-driven decisions for the vehicle companies.

Promote the housing prices.

“Python” Programming language:

IDE:   There are so many IDE’s are available for python in open source such as Ipython notebook, Spyder, Rodeo.

Python Libraries:  Pandas are for manipulating the data’s

matplotlib is for making graphics

Scipy/Numpy is for scientific calculations

sckikit-learn is for using the machine learning methods.

statsmodel is for exploring the databases, and perform the statistical and analytical tests.

r vs python which is best?

Advantages and Disadvantages of “R” language:

Pros:

  1. R will perform the great prototyping and statistical analysis.
  2. There is a huge libraries set are available for different statistical analysis. 
  3. RStudio IDE is helpful in many ways. It makes easier to perform the complex tasks.

Cons:

  1. Sometimes the syntax could be invalid.
  2. It is complex to integrate the workflow.
  3. The documentation of the “R” language isn’t always user friendly.

Advantages and disadvantages in Python Language

Pros:

  1. Python is great for scripting language and statistical analysis.
  2. It integrates easily to workflow.
  3. The scikit-learn library is awesome for machine-learning tasks.
  4. IDEs are a powerful tool for exploratory analysis and presentations.

Cons:

  1. It isn’t made for statistical analysis.

Go through the infographics to know more on the comparison between the “R” verses “Python” – Click on the image to enlarge.

R vs python Infographics

Python vs R for data science Infographics

Dezyre providing Data science certified course, if you are interested to learn data science take the course.

Choosing the programming language based on the following rules:

Personal preference:

You have to choose the language based on the personal preferences. If you a sense is to work on mathematicians and statisticians then you have to go for “R”, whereas computer scientists or the software related works means go for “Python”. The best news is that once you learn r or python program well, it’s easy to go on with your own choices.

Project selection:

You can choose “R” or “Python” according to the project you have to be working on. If you don’t know the languages just “r” or “python” tutorials which is available free on the internet. If you have to work with a scrambled data’s in the website then you should start learning the Python and work on it.

Job market:

According to the job availability, those who knows the python language having more opportunity compared to “R” programming language.

Conclusion:

In general, you choose to learn Python first or R first for data analysis. Each language has its own advantages and limitation in their respective scenarios.  There are also some similarities in both. Based on the above mentioned rules, one can choose the programming. You can also download the “R” or “python” in pdf and go through both the programming language for the ease of use the overall data science.

Leave a Reply

Your email address will not be published. Required fields are marked *