Python Vs R: What Language Should You Use For Data Science?

R and Python are the two most popular programming languages for data scientists, and choosing which to focus on is one of the most formative, career-shaping decisions young analysts make.

R and Python have a lot in common: both are free, open source languages developed around the same time (in the 90s) and favored by the data science crowd.

Read on for a quick look at both languages, an overview of the debate, and when each would be the better choice for a specific data science project.

What is Python?

Python is an interpreted programming language used mainly for web applications. It’s high level, robust, and object-oriented.

Python features integrated dynamic semantics and dynamic typing and binding.

Applications written with Python have lower maintenance costs because of the focus on readable syntax.

The language has a fast edit-test-debug cycle which makes it useful for Rapid Application Development.

It supports module and packages, allowing for modular design and reusability of code across projects.

Debugging Python is simple, too. Instead of causing segmentation faults, bad input and bugs raise exceptions.

Data scientists have several good reasons to like Python, including:

  • Simplicity: Python is easy to learn and use, letting data scientists focus on their work rather than wrestling with arcane code.
  • Productivity: A Swiss study of programming languages found Python to be among the most productive languages around.
  • Readability: Python was explicitly designed to be both terse and readable.
  • Support: There are huge support libraries and third-party packages available.

What is R?

R is an open source programming language and software environment with a focus on statistical computing and numerical analysis. It is procedural as opposed to object-oriented.

What sets R apart is its wide variety of statistical and graphic techniques, including clustering, time-series analysis, linear and nonlinear modeling, classical statistical tests, and classification.

It supports matrix arithmetic, with packages that collect R functions into one place.

In addition, users find creating polished plots with scientific symbols and formulae easy with R.

R has a lot to offer data scientists, specifically:

  • Granularity: R offers deep insights into large data sets.
  • Flexibility: There are numerous ways to accomplish a specific goal with R.
  • Visualization: R features superior data visualization tools that help make data approachable by scientists and non-scientists alike.

Common Criticisms on Both Sides

There are drawbacks to each language. Python isn’t the best tool for mobile applications, for example. That doesn’t impact its use for data science much, but there are other considerations.

Python suffers from the slower nature of interpreted languages, which must be executed through an interpreter instead of a compiler.

Also, like all dynamically-typed languages it requires more testing to avoid runtime errors.

Some data scientists have criticized Python’s weak database layer for making it hard to interact with complex legacy data.

The language can be weak with multiprocessor or multicore workings. There’s also the fact that data analysis functions have to be added through packages.

R has its own critics. Many of them relate to complexity. R is harder to learn, and its syntax isn’t as clean as Python’s.

It can’t be embedded in a web browser. R does have more statistical analysis tools than Python, but otherwise there are many fewer libraries.

At scale R’s complexity only grows. Maintenance becomes difficult, and poor memory management causes it to slow down when too many variables are stored.

It’s sometimes slower than Python, though neither is known for speed.

Finally, R is considered less secure than Python.

The risk can be mitigated using container options on Amazon Web Services (AWS) and similar, but developers need to pay special attention to this potential weakness to avoid costly breaches.

Which to Choose and When

Using both languages will give the best results, but that isn’t always practical or even sensible.

To choose the right programming language, data scientists should consider their primary interests and purpose.

R has superior data visualization. It was specifically built with statistics and data analysis in mind. Users have created packages that cover an impressive amount of specialized statistical work.

There are more packages available for tasks like machine learning and analysis. In contrast, Python has limited package options.

Python is a general purposes programming language, so it’s more robust than R. It excels at building analytics tools and services: automating data mining, data munging, and scraping websites.

R has packages for machine learning, but in general Python better supports machine learning and deep neural networks.

So which language should data scientists use? When the task ahead is mainly mathematical and leans heavily towards statistics, use R.

When the task is engineering-heavy or involves experimenting with new methods, use Python.

There is a generous overlap between languages, but following this guideline will steer data scientists in the right direction nine times out of ten.

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