Author: Michael LaFleur
The 90’s were responsible for a number of incredible developments including the internet, which forever changed the world. 90’s culture isn’t often seen in a positive light, but don’t forget it was the decade that bought both Python and R into the world. These two programing languages gave data scientists an immense amount of power to operationalize risk models, and in turn created the Python vs. R debate that’s still argued 30 years later.
When it’s time to choose the right programing option for your next risk model wouldn’t it be nice if selecting a coding language was as simple as Neo’s choice in the Matrix?
“You take the blue pill—the story ends, you wake up in your bed and believe whatever you want to believe. You take the red pill—you stay in Wonderland, and I show you how deep the rabbit hole goes.”
After all, when it comes to risk analysis the answer is easy, you need the red pill to get answers. The red option lets you jump into the data rabbit hole, analyze the information, and get the answers you need to solve your risk questions. So, what does that mean for Python vs. R? It means the question is, “would you like this red pill or this other red pill?”
Choosing Your Medicine—Which pill will answer your risk questions?
R and Python are two of the most popular programming languages in the analytical domain and are considered close contenders by many data analysts and scientists. Take a look at what they have in common:
-they’re supported by active communities
-they offer open source tools and libraries
As awesome as these similarities are the fact they both tick all three boxes can often make it difficult to pick one over the other.
In the Matrix, which we’d like to point out was another stellar 90s creation, Morpheus gave Neo the pill for a specific use—to identify his body’s signal from millions of others, then use that information to collect him. It’s not unlike a risk model, where you need the right code to collect and analyze the required data. So, with both Python and R offering powerful programming that can grant you entry to the data rabbit hole the real question is: Which red pill offers the easiest route to the data and provides the results in a useable way?
So, it’s not just the capabilities of a program that influence the preference of R or Python, it’s also the context it’s being used in. R’s strength is in statistical and graphical models, and it sees more adoption from academicians, data scientists, and statisticians. Where as Python, which focuses more on productivity and code readability, is popular with developers, engineers, and programmers. As a general-purpose language, Python is widely used in many fields including web development. It’s also gaining popularity across investment banking and hedge funds, and is deployed by banks for pricing, risk management, and trade management platforms. Yet, surprisingly, unlike R, knowing Python is not yet a common requirement for tech talent working in most areas of financial services. So, in the Python vs. R debate, data scientists with a heavy software engineering background may prefer Python, while statisticians may rely more on R.
Having said that, there are a couple of other differences between Python and R:
Python has acquired a positive response from data scientists involved in machine learning. Since the learning curve is low for its users, Python’s real strength lies in its simplicity, unmatched readability, and flexibility—all powered by a precise and efficient syntax. Since it is a full-fledged programming language, Python is great for implementing algorithms for production use as well as for integrating web apps in data analytical tasks.
On the other hand, R is great for exploratory work and is suitable for complex statistical analysis, owed to its growing number of packages. But the drawback for R beginners is that R has a steep learning curve and often makes the search for packages difficult. This can prolong the data analysis process and cause delays in implementation. While R is a great tool, it is limited in terms of what it can accomplish beyond data analysis. Many of the user libraries in R are poorly written and often considered slow, which can be an issue for users.
Library and Packages
Python has extensive libraries that significantly reduce the time span between project commencement and meaningful results. The repository of software for the Python programming language is so rich that the Python Package Index (PyPI) currently comprises of 130,641 packages. The library has a variety of environments to test and compare machine learning algorithms.
The packages offer solutions that are not only intuitive but also flexible. A good example is PyBrain, which is a modular machine learning library offering powerful algorithms for machine learning tasks. Considered to be a popular machine learning library, Scikit-learn offers data-mining tools to bolster Python’s existing superior machine learning usability.
In comparison, CRAN (Comprehensive R Archive Network) remains a huge repository with 10,000 packages that can be easily installed in R. Active users contribute in the growing repository on a daily basis and many of the capabilities of R (like statistical computing, data visualization) are unmatched. While the learning curve for beginners is steep, once a user knows the basics, it becomes much quicker to learn advanced techniques. For many statisticians, implementation and documentation in R are more approachable than in Python.
But newly installed packages in both Python and R are alleviating the weaknesses that each suffers. For example, Altair for Python and dplyr for R support the traditional flow of data visualization and data wrangling.
Data Visualizations is an integral part of data analysis and can simplify complex information by identifying patterns and correlations.
R’s visualization packages include ggplot2, ggvis, googleVis, and rCharts. Visualizations through R can efficiently and effectively, make the most complex raw data set look informative and pleasing to the eye.
When compared to R, Python has a huge amount of interactive options like Geoplotlib and Bokeh and picking the best and most relevant can sometimes get exhausting and complex. Data visualization is delivered better through R and appears less complicated.
Choosing Between R and Python
So far, Python is considered a challenger to R and remains more popular due to its wide-usability and because it can implement production code. But to be fair, both R and Python come with their own set of pros and cons, and the decision to deploy the right one primarily depends on what kind of data set you are looking at and what problem you need to solve.
Both are constantly developing at a rapid pace and there is currently no universal standard for picking one over the other.
How to Integrate Risk Models Without Wasting Time and Money
Whether they choose Python, R, or another option, companies spend huge amounts of time developing risk models to figure out which customers provide the least risk for their business. One of the biggest challenges businesses face is how to operationalize these models quickly and efficiently. This can be especially difficult with complex models that are made possible with R and Python as many risk ‘solutions’ require the models to be translated into code that it can understand. If your business is using one of these solutions you’ve probably already experienced the high cost and excessive time needed to connect your latest model to your risk decisioning process.
As simple solution to these pain points is to use a model-agnostic risk decisioning solution. With a model-agnostic risk platform you’re free to choose the risk model that helps you navigate through the rabbit hole and secure the risk answers that you need to keep your business moving forward. The Provenir Risk Decisioning Platform is a great example of this model-agnostic approach. By using simple wizards risk models developed in a variety of tools can easily be imported, mapped and validated. Provenir automatically generates a list of the data fields; all you need to do is pick the data Provenir needs to send to the model, as well as the data the model should send back to Provenir to drive the decisioning. This entire process takes just a few minutes, which means you not only gain an effective way to maximize the value of your models, but can also instantly adapt risk decisioning processes whenever a model changes.
If your business is wasting time and money implementing risk models take some advice from Morpheus in the Matrix, “What are you waiting for? With Provenir you're faster than this.”
What? That’s exactly how it went in the movie…