Python vs. R for Developing Predictive Analytics Applications

Developing predictive analytics algorithms requires an advanced knowledge of backend programming. Choosing the right language is the essential first step. Python and R are two of the languages that are most commonly used for developing predictive analytics applications. It is important to understand the nuances of each language before settling on one.

There are a number of factors that you need to take into consideration before deciding which language to invest in. Here are a few things to look at.

Depth of Community Discussions

The first thing to take a look at is the amount of resources available from developers that use each language. This is one factor that most people overlook when trying to choose a new language to learn. If you were going to invest months learning the skills to master a language, then you need to make sure that there are plenty of resources to assist you.

Which programming language community has the most resources for people learning predictive analytics? DeZyre researched the answer to this question two years ago. They did a quick Google search to compare the number of articles on using linear regression in Python and R, which was a good barometer of the number of resources on predictive analytics topics as a whole. At the time, they found that there were 25.6 million results for the search phrase “linear regression in R.” The search phrase “linear regression in Python” only yielded 392,000 results. This meant that the Python community only contributed 1.5% as many resources for their fellow Python programmers as the R community.

Since that article was published in April 2016, I decided to replicate the results and see if the finding still holds true. I found that there were now 77.1 million search results for the phrase “linear regression in R” and 6.48 million search results for their search phrase “linear regression in Python.”

This shows that the Python community now contributed 8.4% as many resources on this topic as the R community. This suggests that a growing number of people are using Python for data analysis and predictive analytics, relative to R programmers. However, R is still clearly the most popular language by far.

Which Language Is Best Designed for Data Analytics?

According to a developer I spoke with at R Drive Image, R has been a popular language for data analysis for over 20 years. It has an Integrated Development Environment specifically created for data analysis. In fact, data analytics was the primary purpose of the R programming language. R is used to teach statistics at both the graduate and undergraduate levels across the world.

Python is a decent enough programming language for data analytics. However, it is a general-purpose programming language. It doesn’t have the same functionality.

Which Language Is Most Popular for Data Analytics?

This is an important question to answer, but it shouldn’t be the only metric you focus on. If a language is popular for a particular application, then there is probably a reason for it. However, you may find that the other language is better for your specific applications.

Last year, Big Data Made Simple averaged the reviews of both languages for data analytics on both GitHub and Stack Overflow between 2012 and 2015. They found that the average review for R was scored between 12 and 15 every year. Python only received a rating of 5 for 2014 and 4 for every other year.

This shows that R is clearly far more popular for data analytics applications than Python. However, there were some caveats:

• Python is great if data analysis needs to be used in web apps or incorporated into production databases.
• Python is great for implementing algorithms for production use.
• Python is great if you need to choose from a number of IDEs, because there are more of them there.

You will need to assess your own needs before making a selection, although R is clearly more popular for data analytics overall.

Which Languages Can Connect Two Big Data Platforms?

R relies mostly on temporary memory, which is its biggest limitation. However, that does not mean that it is useless for predictive analytics modeling.

Both programming languages are capable of integrating Hadoop and other big data tools. You should be able to handle most predictive analytics challenges on either language. You just need to understand the logistics.