How to Choose Python or R for Data Analysis?

If you are a newbie data scientist, you may be split between Python and R because these two languages are literally on everyone’s lips. What fans of these languages haven't found time to explain to you, however, is that all of them are perfect, but each would be appropriate for specific applications. Also, while they’re all perfect for both typical and advanced data science projects, each has its own strengths and weaknesses that must be considered when opting for one.

It can be fairly easy to choose between these two near-identical languages, you just need to consider five core factors:

1. Popularity – which language between these two is popular among your colleagues?
2. Nature of your project – what type of problem do you want to solve?
3. Cost factor – How much could either language cost you to learn? (consider time and financial cost)
4. Type of tools – What are the most popular tools in your area of specialization?

So, what are Python and R used for in data science?

Python:

This language is a darling of programmers who simply want to apply lots of statistical techniques in their projects. Also, if you are a programmer or developer who is about to try out data science for the first time, this is the best language to start with.

Another reason why programmers love Python so much its reputation for being a production-ready language. It comes as a single tool capable of integrating with virtually all parts of your workflow.

R:

R is king in research and academics because of its prowess in exploratory data analysis. Because the enterprise world has a lot of data analysis to do, R has started to be the priority data science language there as well.

However, nothing is more admirable about R than being a simple laidback language that requires minimal skills. That’s why engineers, scientists, statisticians, and engineers with limited programming skills adore it. So, basically, R is a language you would recommend for anyone in finance, academia, media, pharmaceuticals, and marketing, especially if that person isn’t so much into programming.

The Usability Factor

Python:

If you have preexisting software knowledge, you will find Python to be more natural and easier to use compared to R. Also, coding and debugging in Python is usually easier.

Note that the indentation of Python code can affect its meaning. However, a piece of functionality never changes – it can be written in the same manner all the time.

R:

Are you a newbie in coding? Start your data science journey with R. You can write large statistical models using a few lines of R code. Another advantage is that one function can be written in multiple ways.

What is each language’s Ecosystem like?

Python:

A brief interaction with Python will amaze you with how the language mimics English in its syntaxes. This means commands are much easier to read and write; something like print (“Hello World!”)

If your projects will involve machine learning products or pipelines that must be integrated with web frameworks, Python is the best language for you. However, the process of installing libraries and dealing with dependencies can be a bit tricky, so watch out!

Python is supported by two robust repositories that may be useful: Anaconda and PyPi (Python Package Index). You can contribute to these repositories although it can be a bit complicated.

R:

Data analysis sometimes relies a lot on your ability to string your workflow together. R happens to be really good at this function, thanks to its rich ecosystem of effective interface packages that assist in communication between any open-source languages.

R too is supported by a few famous repositories. You can find its packages at GitHub, Bioconductor, and CRAN (Comprehensive R Archive Network).

The Flexibility Factor

Python:

If your projects will involve creating things that have never been made or tested before, Python is the best language for you. Better yet, you can use it to script websites and a few other applications.

R:

It is much easier to use intricate functions using R. You will be provided with an array of models and statistical tests ready for use in virtually any type of project.

Which Is Easier to Learn?

It depends on your learning approach:

Python:

If you are looking for a language that puts emphasis on simplicity and readability, I’d recommend Python in a second. Focus on simplicity and readability makes Python’s learning curve to be linear and smooth.

Also, when it comes to learning, many data scientists consider this language to be one of the best entry-level coding languages.

R:

Yes, R is easy to learn for starters but things tend to get a little trickier when advanced functionalities begin to creep in. As such, it is a difficult language to learn when developing complex expert systems.

What Are Each Language’s Data Handling Capabilities?

Python:

Effective data analysis in this language requires you to install special packages. With years of increasing popularity, these packages have improved considerably. It comes with two equally effective tools for data analysis: pandas and NumPy. There are a few more but not as popular.

R:

R is the perfect data analysis language because it comes with a wealth of packages, a bunch of tests ready for use, and allows you to use formulas any time you please.

Unlike Python, you don’t need to install packages to handle basic data analysis tasks. Big datasets, however, will require such packages as dplyr and data tables.

What Are the General Pros and Cons of Each Language?

Well, there are many. Here are a few major ones:

Python:

Pros

• It is a general-purpose language that goes beyond data analysis
• It puts extra focus on code speed, readability, and many functionalities
• Python code is easier to deploy and reproduce
• It is highly practical for mathematical computation and understanding how algorithms work

Cons

• Python has considerably fewer libraries compared to R
• Rigorous testing is this language is a turn off for many programmers

R:

Pros

• R produces the most beautiful visualizations and graphs can ever have
• It has a wealth of functionalities that support data analysis
• It beats Python by a wide mark when it comes to statistical analysis
• Although it is built around the command-line interface, most users work in RStudio, an enviable environment that combines debugging support, window to accommodate graphics, and a data editor – three things that matter more to a data scientist

Cons

• It can be an ordeal to find the right package to use
• There are a lot of dependencies between most if R libraries
• R was made for statisticians, so learners will find it hard to learn as things get more complex
• If you write your R code poorly, it will take ages to execute