Python vs R language for analysis

Python vs R language

The two most popular programming tools for data science work are Python and R at the moment. Increased data availability, more powerful computing, and an emphasis on the analytics-driven decision in business has made it a heyday for data science.

So, for most data analysis projects, your goal is going to be to create the highest quality analysis in the least amount of time. If you understand the principles of natural language processing, data cleaning, and machine learning, you can implement an automated text summarizer in R or Python.

Both are the free and open source and were developed in the early 1990s. But, still, differences exist between the two. So, it’s important to understand that these are not all the same thing. Here we will explain how R and Python language both are different from each other.

So, let's see -


Python is a popular programming language. It was created in 1991 by Guido van Rossum. It's used for web development (server-side), software development, mathematics, system scripting. Python can be used on a server to create web applications. It can be used alongside software to create workflows. It can connect to database systems. It can also read and modify files. Python can be used to handle big data, rapid prototyping, or for production-ready software development.


R is rapidly becoming the leading language in data science and statistics. Today, R is the tool of choice for data science professionals in every industry and field. Whether you are a full-time number cruncher or just the occasional data analyst, R will suit your needs. R was created by Ross Ihaka and Robert Gentleman at the University of Auckland, New Zealand, and is currently developed by the R Development Core Team, of which Chambers is a member. R is named partly after the first names of the first two R authors and partly as a play on the name of S. R is based on S that stands for statistical language and S was an open source language. A GNU package, the source code for the R software environment is written primarily in C, Fortran and R itself and is freely available under the GNU General Public License.

Python vs R language

  • If you want to create a web service to enable other people to upload datasets and find outliers, Python is better. Python is a general-purpose programming language, which means that people have built modules to create websites, interact with a variety of databases, and manage users.

  • Python was released in 1989 with a philosophy that emphasizes code readability and efficiency. And R was developed in 1992 and was the preferred programming language of most data scientists for years.

  • Python is an object-oriented programming language, which means it groups data and code into objects that can interact with and modify one another. On the other side, R is a procedural language which works by breaking down a programming task into a series of steps, procedures, and subroutines.

  • Python supports all kinds of different data formats. And another side, you can import data only from Excel, CSV, and from text files into R.

  • Python works on different platforms (Windows, Mac, Linux, Raspberry Pi, etc).

  • R is based on S, a proprietary statistical tool built by Bell Labs.

  • Python has a simple syntax similar to the English language.

  • Python has syntax that allows developers to write programs with fewer lines than some other programming languages.

  • Python runs on an interpreter system, meaning that code can be executed as soon as it is written. This means that prototyping can be very quick.

  • R might not be as versatile at grabbing information from the web like Python is, it can handle data from your most common sources.

  • R was built with statistics and data analysis in mind, so many tools that have been added to Python through packages are built into base R.

  • Python can be treated in a procedural way, an object-oriented way or a functional way.

  • R is better for data visualization.

  • You’ll have to use Pandas to analysis the data in Python. In contrast, R was built to do a statistical and numerical analysis of large data sets, so you’ll have many options while exploring data with R. No need to the external library in R.

  • Python provides data scientists with various packages like Theano and TensorFlow, which makes it one of the best languages for deep learning. R can also use some of these packages.

  • RStudio IDE is the obvious choice for working in an R development environment.  R packages like dplyr, plyr and data.table are highly preferred for manipulating packages, stringr for string manipulation, ggvis and ggplot2 for data visualization, and caret for machine learning.

  • Python, on the other hand, comes with Spyder, IPython Notebook, and Rodeo are good to start with. As for popular libraries, Python gives you numerous options to choose from; NumPy /SciPy for scientific computing, matplotlib to make graphs, scikit-learn for machine learning and pandas for data manipulation.

  • R as a language has a rich community of more than 2 million users and that includes thousands of developers spread across the world. The community has packages widespread across actuarial analysis, finance, machine learning, web technologies, pharmaceuticals that can be of great help to predict component failure times, analyze genomic sequences, and optimize portfolios. On the other side, the Python community is also gaining acceptance of the good number of StackOverflow members. General-purpose coding in Python continues to grow with remarkable user-contributed code and documentation by developers and programmers, data scientists, researchers, and students across the world.

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

  • Python is more commonly used to build modules to create websites, interact with a variety of databases, and manage users.


In the battle of "best" data science tools, python and R both have their pros and cons. Selecting one over the other will depend on the use-cases, the cost of learning and other common tools required. Python and R both are good for startups and companies looking for cost efficiencies.

Tell us which one you want to choose for your projects? Python or R? You can share your experiences with us in the comment section, Thank you!

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