The world is becoming more and more data-driven, so it’s essential to make data science more understandable and accessible to all people besides data scientists and software engineers. Thanks to data science analytical and predictive opportunities, entrepreneurs can skip the “trial and error” stage when managing their business.
Data science deals with the methods of statistics, data analysis, data mining, machine learning, and other data-related fields. It allows making systems smarter than their creators and to build such systems, developers use Python or R programming language.
This article will walk you through the role of Python and R in data science. It will help you understand the difference between R and Python, discover the advantages and disadvantages of using these languages in data science projects, and define which language suits your business needs.
- How Is Data Science Good for Business?
- A Brief Overview of Python and R History
- R versus Python: What Statistics Says
- Is R or Python Better for Data Science?
- R vs. Python for Machine Learning
- Summing Up
How Is Data Science Good for Business?
Suppose you are in a car rental business. You can make it more profitable by defining what cars are going to be in demand in the near future and what factors will influence people’s preferences. First, a data scientist harvests data on how people used your services previously, explores, refines, and modifies it. Then, by applying various mathematical models and machine learning algorithms, a developer teaches the system how to make predictions. These predictions would allow you to define:
- most loaded hours/days/months at your car rental spot,
- most wanted cars based on a variety of parameters: car model/time of the year/client’s gender and age, and other,
- how the events happening in your area can influence the popularity of services and others.
In case of deep learning, a programmer teaches the system how to learn and make decisions based on data analysis. For instance, predictive analysis and simulation technology allow defining the most customer-friendly constructive solutions for your premises and increase its security. Also, to improve customer service and automate other specific processes in your company, you can choose to implement artificial intelligence chatbots, etc.
A Brief Overview of Python and R History
Python is a general-purpose programming language with a rich 30-year history that is now widely used by programmers who work with data science methodologies. Since 1991, Python has significantly broadened its borders and become an integral part of big data projects that require processing a tremendous amount of information in a single software product. Besides data handling, Python deals with a wide range of web and mobile development projects.
R first appeared in 1993; however, its stable fully-fledged version came out in 2000. In contrast to Python, R is a more narrowly-used language that derived from the S statistical programming language. It primarily focuses on computational statistics, linear and non-linear modeling, graphical data, and other data manipulation processes. Now, it is the second most widely used language for data science after Python.
R versus Python: What Statistics Says
Although both languages are utterly different, they are the most utilized languages in data science projects. In 2018, Kaggle presented a survey on data science by their popularity based on responses of computer science students, young data scientists, and experienced software engineers. According to the results, Python takes the lead in the overall scope of languages with over 75% of respondents leaving R the second on the list with 12.5%.
The reason why Python and R take top positions on the list is their affordability in contrast to other computational environments and languages like SAS, Stata, or MATLAB. They are open source programming languages with the enormous number of libraries that do tons of work for developers and let them concentrate on code quality. Since R is a highly specialized and relatively complicated language, more versatile Python wins by a big margin.
Why Not Use Both Languages?
Python and R can be used in the same project simultaneously. It is possible to integrate Python’s capabilities into R and vice versa. However, the data passage between the two languages can be tricky. As Python’s frameworks, libraries, and packages for data science get better and their number continues to grow, this language is usually enough to accomplish diverse data-related tasks. That’s why most programmers prefer Python. In addition, using only one language is more reasonable in terms of further software maintenance.
On the other hand, the number of programmers who know both languages and use them regularly is small compared to that of Python developers. Another recent survey by Kaggle on the usage of Python, R, or both programming languages by industry proves this point. Most specialists commit to Python rather than combine both languages in their practice or use only R on a regular basis.
Is R or Python Better for Data Science?
The statistics highlights that Python is a prevalent language. Some may ask then, ‘Is Python better than R if it’s more popular?’, but the answer to this question isn’t straightforward. Each of the two languages can be particularly good for certain tasks, depending on what you want to develop. Let us conduct a benchmark analysis of these languages to see which one will win the battle of Python vs. R for data analysis and machine learning.
Developers love Python, statisticians love R, business owners love… Python! Python programming is more flexible and business-oriented. Its varied data managing tools make it easy to get the project developed and deployed faster. R is also a mighty tool for statistics, data visualization, and building of theoretical models for product implementation. However, these models are more difficult to put in production because of R’s narrow scientific focus and complexity.
Score: Python - 1, R - 0.
Python is a versatile language, which means that it emphasizes the principles of simplicity and code readability. It has a wider range of usage beyond data analysis and offers effective tutorials on a plethora of projects. Respectively, the process of learning Python is easy for novice programmers and intuitive for experienced developers.
Without prior statistical language programming experience or data science background, the R learning curve may be steep for a developer. If a specialist doesn’t use R on a regular basis, each time they get back to programs written with R, they have to spend more time orienteering in the system. This is caused by a highly specialized nature of the language.
Score: Python - 2, R - 0.
Both languages use plotting libraries that help data scientists construct graphs and charts for convenient data representation. Python has powerful seaborn, bokeh, plotly, and matplotlib libraries for that purpose, but they haven’t outdone the extensive visualization capabilities of R yet. R is especially praised for its ggplot2 package that helps with plots building.
Score: Python - 2, R - 1.
Packages and Libraries
Packages and libraries are tools that shorten development time because they contain ready-made functionalities. Both Python and R can boast a wealth of libraries that a developer can install within seconds and begin coding.
Apart from standard libraries for data manipulation like Pandas, there are specialized numeric and scientific Python program packages such as SciPy and NumPy. R has advanced built-in libraries like dplyr, BioConductor, etc. that primarily deal with data handling tasks. So, by the criteria of the ecosystems presented around these two languages, there’s no obvious leader.
Score: Python - 2.5, R - 1.5.
Language performance highly depends on features to be implemented and the developer’s skills. In most cases, Python runs code faster than R. Considering that R wasn’t developed as a general-purpose language, some programmers might spend more time figuring out how to accelerate R’s code execution.Thus, if you want to make a project faster and be able to follow the progress at any stage of development, the team that works with Python is your choice.
Score: Python - 3.5, R - 1.5.
To fix a specific problem, Python has over 100K external packages that can be downloaded via PyPI. Despite R’s extensive standard libraries, some third-party packages may not be compatible with the latest version of R. Contributors can stop maintaining some packages without notice, that is why the lifespan of those packages is often short. It means that a developer will have to spend more time on figuring out how to solve a particular problem without those tools.
Score: Python - 4.5, R - 1.5.
Considering a great number of specialists using Python for science and business, Python has a more active community. Many R followers come from an academic background and often use this language when working on specific scientific projects. Still, both Python and R specialists have already contributed a variety of convenient frameworks and libraries for data science projects and constantly help each other on the Internet.
Score: Python - 5, R - 2.
R vs. Python for Machine Learning
Experts claim that Python has a slight advantage over R regarding machine learning (ML). The two main reasons why Python is better than R are:
- Python libraries for ML outnumber R capabilities,
- Python is better in terms of memory usage.
The Kaggle comparison of ML libraries usage for both languages also shows that specialists prefer to use Python-oriented packages. SciPy stack, TensorFlow, PyTorch, and Keras libraries are well-tested and trusted for their dynamic computational abilities and efficient memory usage. The ML models made with R are of high quality, but their implementation is time-consuming because of the higher complexity and slower performance of R compared to Python.
Score: Python - 6, R - 2.
Both languages are efficient for data science implementations in different ways:
- Python is a widespread language, easy to use, and business-efficient,
- R has a scientific orientation and is convenient for handling statistical data,
- Python is more concentrated on the practical side of software implementation,
- R focuses on building theoretical models.
They can be used together in the same project, but usually, there’s no need to do so. Our company uses Python in data science projects for different reasons. For instance, a growing number of frameworks, packages, and libraries that go along with Python offer more functionalities to implement. Also, it’s reasonable to use Python to avoid further maintenance issues.
The main distinction between Python and R lies in their purpose. R was made by statisticians for statisticians. It has a scientific focus and is vastly used by academicians in their research. Due to its advanced data visualization capacities, it can be also used in some data analysis projects. However, experienced software developers select Python over R, since it aims at:
- putting simplicity of the language first,
- developing a quality project in short time,
- helping a product idea owner to control the development process,
- making it easier to release and maintain the software product,
- providing advanced solutions to machine and deep learning projects,
- ensuring reliable performance at each stage of development.