R vs Python for Data Science

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R vs Python for Data Science :

We have all heard about R and Python. Both languages are widely used in the field of data

science. You might be confused about which one is best.

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Here, let us compare both of them and analyze them in detail.

R vs Python for Data Science

What is R?

You might have heard a lot about R and would have wondered what actually it is.

So, R was developed to focus on delivering a better and more user-friendly way to do data

analysis, statistics, and graphical models.

It is one of the fastest-growing statistical languages in the corporate world.

What is Python?

Python is a very commonly used language. Hence it is easy for us to infer that python

emphasizes productivity and code readability. It’s a flexible language that has focussed

on simplicity. Python for data science is rapidly claiming a more dominant position in the

Python universe. More innovative data science applications will have their origin here.

Python vs R for Data Science

R is found only in the data science environment; whereas, python being a general-purpose

language is widely used in many fields, such as web development. This often biases the

ranking results in favor of Python.

When and how to use R?

R is mainly used when the data analysis task requires analysis on individual servers. It’s

handy for almost any type of data analysis because of the huge number of packages and

readily usable tests.

Setting up R and the necessary packages

When getting started with R, it is a good practice to install RStudio IDE.  Once this is done,

It is advisable to have a look at the following popular packages:

1. dplyr, plyr and data.table: helps you to easily manipulate packages.

2. stringr : helps you to manipulate strings.

3. zoo : helps you to work with regular and irregular time series.

4. ggvis, lattice, and ggplot2 : helps you to visualize data.

5. caret : helps you to machine learning.

When and how to use Python?

Python is extremely suitable to be used when your data analysis tasks need to be integrated

with web apps or if statistics code needs to be incorporated into a production database. As it

is a fully-fledged programming language, it’s a great tool to implement algorithms for

production use.

Useful packages in python for data science:

1. NumPy /SciPy: helps you in scientific computing

2. pandas (data manipulation) : helps you to make Python usable for data analysis. 

3. matplotlib : helps you to make graphics

4. Scikit-learn: helps you for machine learning.

Let us Compare R and Python 

R Language	Python Language
Visualized data can often be understood more efficiently and effectively than the raw numbers alone. R and visualization has a hold on each other.	Visualizations are important criteria when choosing data analysis software. R has comparatively better visualization packages which gives it a better edge on representation.
R is developed by statisticians for statistics. It is not necessary to have a computer science background.	Python brings people from different backgrounds together. It is easy to understand the language and is known by lots of programmers too.
R was developed to make the life of statisticians easier, not the life of your computer. R is very slow due to poorly written code,	Python is a general-purpose language that is easy and simple. You require less time to code and it is more understandable too
R’s learning curve is non-trivial. Even finding packages takes a lot of time if you’re not familiar with it.	Python is a general-purpose language that is easy and simple. You require less time to code and it is more understandable too. Python code is reusable and dependable.

R vs Python for Data Science

Conclusion

When we have to choose from among the two ( R and Python ), we need to look keenly on our data and the kind

of model we are aiming to. Both languages have pros and cons. It is up to us on what we

should take up in order to prosper in our future. Both languages are beautifully crafted

ones. Choose wisely so that you can excel in the emerging technology.

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Blog contributed by - Nandana Sreeraj

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