Python vs. R: Which is better for data science?

If you’re trying to choose between Python and R, the project you have planned will be a deciding factor. While R is better for sta­tis­tics and vi­su­al­iz­ing results, Python has a wide variety of features and solutions.

What are Python and R?

If you want to learn pro­gram­ming and are looking for a language that’s good for research work with analyses and sta­tis­tics, you’re sure to come across Python and R sooner or later. The two pro­gram­ming languages are fre­quent­ly used in data science, pre­dic­tive analytics and data vi­su­al­iza­tion, and both have large com­mu­ni­ties of users. At first glance, they have a lot in common, but we’ll also get into their dif­fer­ences below.

What are the pros and cons of R?

R gets its name from its de­vel­op­ers, Ross Ihaka and Robert Gentleman. These two sta­tis­ti­cians at the Uni­ver­si­ty of Auckland developed and released the language in the early 1990s. Their aim was a language that could carry out and display complex sta­tis­ti­cal analyses. The original target group was people with extensive knowledge of sta­tis­tics and pro­gram­ming. R is based on the pro­gram­ming language S and is a free im­ple­men­ta­tion.

R can be compiled and runs on UNIX platforms, Linux, Windows and Mac. It’s mostly used for de­vel­op­ing sta­tis­tics software and per­form­ing deep data analysis. Thanks to its numerous libraries, R can also be used for graphic displays of data. The language is open source and part of the GNU project. Although in the past R was primarily used in academic contexts, it now boasts in­te­gra­tion with a number of other languages and programs and is used by many companies.

Pros of R

• Open source: R is a language for everyone, at least in terms of cost and avail­abil­i­ty. It’s com­plete­ly free and open source. That means it’s possible to use or build on it as your project requires.
• Scope: The fact that R is open source also means that there are a number of user adap­ta­tions that have been made freely available. The chances that there’s already a solution to your problem are rel­a­tive­ly high. De­vel­op­ers have already created around 20,000 packages based on R, which can often provide tailor-made solutions in spe­cial­ized subject areas.
• Com­pat­i­bil­i­ty: R works on a number of different platforms and has in­ter­faces with various other languages and databases. So you can easily use R for a part of your project and embed it into a larger context.
• User interface: A graphic interface was developed to increase the user friend­li­ness of the language. The interface, called Rstudio, makes it sig­nif­i­cant­ly easier to work with R code, meaning projects can be im­ple­ment­ed faster. Packages like Plotly also make it easier to create vi­su­al­iza­tions in the form of graphics and diagrams.
• Community: R has an en­thu­si­as­tic community behind it. Many R users are experts in their field and can provide valuable tips for solving your problems. The wide community also means there’s abundant doc­u­men­ta­tion and the extra packages and libraries we mentioned above.

Cons of R

• Per­for­mance: R isn’t a slow or weak language but you might ex­pe­ri­ence delays when it comes to larger data sets. One reason for this is its single thread pro­cess­ing, which can only use one CPU at a time.
• Learning curve: Since R is usually offered without a graphic interface, it can come with a hefty learning curve. It can take a while to get a handle on the various notation rules, re­stric­tions and idio­syn­crasies of the language. Knowledge of sta­tis­tics are also a key pre­req­ui­site for working with R. Take a look at our R tutorial for beginners to get a first im­pres­sion of the language.

What are the pros and cons of Python?

Python is sig­nif­i­cant­ly more well known than R and used by millions of people worldwide. The language was developed in 1991 by Guido van Rossum and has always had the goal of providing the simplest code possible. Many terms in the language are taken directly from English, making it easier to un­der­stand. Python code is also very clear and easy to read. It’s platform in­de­pen­dent and object oriented. Thanks to its large community and open-source approach, it has numerous packages in the areas of deep learning, AI and data science. Check out our Python tutorial to get a closer look at the language.

Pros of Python

• Ver­sa­til­i­ty: Python is a versatile language in every sense. It can be used in a number of areas and thus makes it possible to take a holistic approach to projects. It’s also platform in­de­pen­dent, meaning it can be used on a number of systems. And it has numerous in­ter­faces with other programs, languages and databases.
• Open source: Like R, Python is also open source and freely available. Continued de­vel­op­ment of Python is co­or­di­nat­ed by the Python Software Foun­da­tion, but every user can adapt the language for their own projects.
• Scope: Python users have developed a wide variety of packages. There are over 300,000 solutions available for download. That makes working on most projects sig­nif­i­cant­ly easier.
• Learning curve: Python is one of the simplest pro­gram­ming languages out there. Despite its im­pres­sive scope, it can be learned and used in a rel­a­tive­ly short amount of time. The code is also rel­a­tive­ly clear, which makes it easier to work in teams and implement small projects on your own.
• Community: Python has a large community that’s con­stant­ly creating doc­u­men­ta­tion and libraries. It’s known for being helpful and sup­port­ive, so if you have questions or problems you’re likely to find someone to help you.

Cons of Python

• Per­for­mance: As a dynamic language, Python could certainly be faster. That’s es­pe­cial­ly true when it comes to large data sets, leading many pro­gram­mers to look for al­ter­na­tives in that case.
• Errors: Python isn’t a par­tic­u­lar­ly error-prone language but if you’ve made a mistake in the code, you won’t find out until runtime. Regular and extensive testing are therefore very important when working with Python.
• Vi­su­al­iza­tion: Python is also lacking when it comes to vi­su­al­iz­ing sta­tis­ti­cal values and results. There are only a few tools that can deliver truly sat­is­fy­ing results.
• Mobile devices: Python isn’t optimal for use on mobile devices. While there are a few solutions for this, most app de­vel­op­ers opt for an al­ter­na­tive language with native com­pat­i­bil­i­ty for Android and iOS.

What’s the dif­fer­ence between Python and R?

Now that we’ve looked at the two languages on their own, we’ll consider some of the dif­fer­ences between Python and R.

Syntax

The dif­fer­ences between the syntaxes of the two languages can be spotted im­me­di­ate­ly. R looks like this:

$ R
> myString <- "Hello! You’re using R."
> print (myString)

Python is a bit more concise:

>>> print("Hello! You’re using Python.")

Other dif­fer­ences between Python and R

In addition to syntax, there are a few other important dif­fer­ences between Python and R.

• Uses: The two languages have very different ap­proach­es. R is primarily intended to be used for sta­tis­ti­cal analyses and vi­su­al­iza­tions and is very good at this. Python has a far more com­pre­hen­sive approach and is also suitable for pro­gram­ming software and deep learning.
• Scope and pop­u­lar­i­ty: More and more people are using R outside of academia, but the language does still have its roots in science. Python is used by sig­nif­i­cant­ly more de­vel­op­ers. That means that Python has far more packages than R.
• Per­for­mance: Neither R nor Python is the fastest language out there. Python is, however, slightly faster and more powerful than R.
• Formats: While Python can work with a variety of data formats, R is more limited. CSV, Excel and text files are the only formats it supports without ad­di­tion­al tools.

Python vs. R: Which language should you learn?

So which language comes out ahead, Python or R? They’re both very powerful languages, so the answer has a lot to do with what you intend to do. If you’re primarily looking to create and visualize sta­tis­ti­cal models, R will be the better choice. If your project goes beyond sta­tis­tics, Python will offer you far more pos­si­bil­i­ties.