R vs Python: Which Programming Language is Best for Big Data?

If you want to work in Big Data, there are no two ways around it: you need to know programming languages in order to perform complex data analysis. Whether you’re an employer or a would-be employee, programming languages are among the essential data analytics skills you need to succeed. The question is, between R vs Python, which is most beneficial to use? We’re here to help you answer that question. Keep reading to find out more.

What is R?

R is a hugely popular programming language if the 95,000+ members of LinkedIn’s R Group are any indication. So what is R? R is an open source scripting language designed for prescriptive analytics and data visualisation. It’s also a procedural language which works by breaking down a programming task into a series of steps, subroutines, and procedures. It also has command-line scripting build for storing complex data-analyses which can be reused on similar data sets.

History

R was built by statisticians, for statisticians, and most programmers can spot that fact pretty much as soon as they look at a line of R syntax. The initial version of R was released in 1995. The name was derived from the first letter of the names of its two developers, Ross Ihaka and Robert Gentleman. Ihaka and Gentleman specifically designed R as a language for academic statisticians with advanced programming skills. With R, these statisticians could perform complex data analysis and display that information in an array of visual formats.

Pros

Because R was designed for statisticians to complete complex analysis, it’s a fantastic tool for Big Data analysts. For one thing, R’s package ecosystem is a major help. Put it this way: if there’s a statistical technique you want to use on your data set, chances are, there’s an R package for that. And if there’s not, R is an open source language and a free software, so any developer can build the tool they need. Plus, R has a strong potential for machine learning thanks to its data analysis and data generation capabilities are stellar. And since R has strong links to academia, any new research in the field probably has an R package involved, which keeps R firmly at the cutting edge.

Cons

Of course, like any programming language, R isn’t perfect. For one thing, the syntax was designed for high-level statisticians and mathematicians. So if you’re looking for a quick language, R will take some time to get used to. Since the language was developed in the 60s, R was designed on the principle that very large data sets have to be stored as physical memory. This has become less of an issue as modern computers have gained memory in leaps and bounds, but it still slows down R’s processing power. In addition, certain capabilities (like security) weren’t built into the original framework of the language. This meant that R had functionally no security over the Web, which ruled it out if you wanted to do any back-end server calculations. This problem has been lessened by newer developments in the field, however.

What is Python?

If R is a language for those who can recite advanced statistical principles in their sleep, Python is the inverse. This might be why Python has seen astronomic growth and has become one of the most in-demand languages in the industry, used by major players like Instagram, YouTube, and Spotify. Python is an object-oriented programming language, grouping data, and code into objects that can interact with, and modify, each other.

History

Python was conceptualised in the 1980s by Guido van Rossum. Unlike R, which is designed with complexity from the get-go, Python strongly emphasises readability and efficiency above all. This means that Python is a general-use programming language that’s highly accessible and easy to learn. It’s also named for Monty Python’s Flying Circus if that gives you any indication of van Rossum’s sense of humor.

Pros

Like R, Python is a free, open-source language that anyone can download and use. Since Python strongly emphasises readability, you’ll be hard-pressed to find a language that cleans up your data quite as prettily as Python. It lets you add new functions and layers as you go, helping you to separate and clean your data as you go. Another big benefit of Python is its massive libraries. There are libraries for machine learning, data collection, data manipulation, and data munging (to name a few). But unlike R, you won’t run into integration problems. In fact, many programmers wrap lower-level languages in Python for easier integration.

Cons

Python is a relatively simple programming language. Unfortunately, this is both a blessing and a curse. Think of it this way: Python is far simpler than, say, JavaScript. If you learned Python first, it can be much more difficult to transfer your knowledge of Python’s libraries and syntax to another programming language. In addition, because Python is a general-use language, it offers more options beyond statistical analysis. Again, this is both a blessing and a curse. Because it doesn’t focus solely on statistical analysis, it includes less statistical model packages than an exclusive language like R.

R vs Python: What’s the Difference?

With that in mind, let’s take a closer look at the difference between R and Python, breaking it down into the stages of the data pipeline.

Data Collection

You can get any kind of data with Python you could possibly want. If you can’t figure it out, Google Python and the dataset you’re looking for. We promise you’ll find a solution. Because of this, Python supports all kinds of data formats, whether you want to import an SQL table or source JSON. It also allows you to create your own datasets with relative ease. Almost any data you can grab from the web is something Python can simplify into a line of code. R isn’t quite as versatile as Python, but it can certainly handle data from commonly used sources like Excel, CSV, and text files. In fact, many modern R packages have been designed to address this data issue, so while it might take you a few packages to get there, you can find a way to use R for your dataset.

Data Exploration

When it comes to data exploration, it’s all about Pandas, Python’s data analysis library. Pandas is organised into data frames. These data frames can be defined and redefined throughout the project and can be cleaned by filling in non-valid values with a value that makes sense for numerical analysis (like 0). This makes it very easy to scan and clean in Python as you work. Then, there’s R. As we said, R was built by statisticians for statisticians, so you’ll have quite a few options to do complex analysis on large datasets. Basic R functionality will cover you for things like: Without ever leaving R for third-party libraries, you’ll be able to apply statistical tests and build probability distributions. So, if you want variety in your data exploration, R has a clear advantage.

Data Modeling

As for data modeling, both programs offer you a few options. In Python, you’ll have to use a combination of libraries, such as: Thankfully, all of these libraries have a pretty intuitive interface, like all things Python. For data modeling in R, you may need to rely on packages outside the language’s core functionality. It mostly depends on what you’re trying to do. You’ll mostly run into this problem with certain types of modeling analyses, like mixtures of probability laws.

What Programming Language Should You Use?

With that in mind, which programming language should you use for Big Data? That depends on what you’re trying to accomplish and what matters most to you along the way. First, you should ask whether you intend to use the analysis in academia or industry. If you’re looking for industry analyses, R certainly won’t hurt you, but more companies will be looking for Python. You also need to consider whether you’re interested in machine learning or statistical learning. There’s an important difference: machine learning is the offspring of artificial intelligence, while statistical learning comes from statistics. Their emphasis is also slightly different. Machine learning focuses more on predictive accuracy in large-scale applications, while statistical learning emphasises the interpretability and precision of models. R was designed as a statistical language, which means it’s better suited to statistical learning. Python is the better option for machine learning, as it’s far more flexible (particularly if you have any intention of incorporating your analysis with web applications).

Need to Make Sense of Big Data?

The choice of R vs Python isn’t a simple one. Then again, neither is Big Data. But you still need it in order to stay one step ahead of the competition. If you need help mastering your Big Data, we’re here to help you find the skilled employees you need. Ready to get started? Get in touch today to see how we can help.