**R vs Python**

If you are someone who wishes to make a career in Data Science, then the ultimate question you have to face is, which programming language you should learn and why? There have been numerous discussions on public forums with people advocating for R or Python for plenty of reasons.

Though it completely depends on your choice, comparing both the languages on some grounds will surely help you make the right decision.

**What are R and Python?**

**R** is an **open-source programming language** developed for **statistical analysis and computations**.

Like R, **Python** is also an **open-source programming language** that was initially developed as a general-purpose programming language, and later branched out to be a language for **Statistical Analysis** **and** **Machine Learning Modeling**.

Let’s understand the difference between these two highly popular Data Science languages:

**Ease of Installation**

Well to start with, R packages are solely managed by **CRAN** **(The Comprehensive R Archive Network)** repository that manages the updated versions, their installations, and related documentation of R Packages. All the packages you install in R are stored in CRAN. Also, any new package to be added in R should be submitted to CRAN. Currently CRAN has over 16000 additional statistical packages. This is why it is easier to install R.

On the other hand, Python has two package management platforms, **Conda** and **PyPI** (Python Package Index) that include over 100k Python packages. There have been inconsistencies found in Packages, Libraries, and Versions while installing Python due to two repositories. Due to this reason, it is a little tedious to install Python.

*Want to read about Python in detail? Read this **riveting blog**!*

**Robustness and Flexibility**

R has all the features that Python has in terms of programming ability, statistical computing and modeling, but Python is more flexible and robust. Python is a better option when it comes to integrating it with web applications and production.

However, R is less robust and versatile, which is why it is limited to statistical computing and mathematical modeling.

**Ease of Learning**

One of the most frequently asked questions is “**Which between R and Python is easy to learn**?”**.**

Both R and Python have almost similar features, but when it comes to syntax, **R is a little complicated** and is better for someone who is already familiar with other programming language. On the other hand, **Python has a relatively simpler and readable syntax** and hence, for anyone who is about to start-off with a programming language, Python is a good option.

However, when the model building becomes complicated, it requires someone who is proficient in Python.

**Speed of Processing**

Usually **Python is 8 times faster than R till there are up to 1000 iterations**. When the number of iterations increases, R typically surpasses Python’s speed. In comparison to Python, R requires more lines of codes to perform a certain task, which make the programs more complex and bulkier.

**Statistical and Analytics Ability**

R was designed for statistical computation and Modeling purposes and hence it performs better for any level of complex computation. **R has better statistical packages and libraries for dashboard than Python**. Python being a general programming language somehow lacks the packages and libraries for Data Science. Python is better suited for modeling and machine learning, which is complicated in R.

**Suitable Area**

The focus of R is primarily into statistical analysis and hence it is better suited to academia and research. On the other hand, Python being the programming language for all purposes is suitable for tech industry. However, Python also comes with packages that can create an environment similar to R.

There are plenty of other grounds that differentiate R from Python. The following table will further clarify that!