DZone
Thanks for visiting DZone today,
Edit Profile
  • Manage Email Subscriptions
  • How to Post to DZone
  • Article Submission Guidelines
Sign Out View Profile
  • Post an Article
  • Manage My Drafts
Over 2 million developers have joined DZone.
Log In / Join
Refcards Trend Reports Events Over 2 million developers have joined DZone. Join Today! Thanks for visiting DZone today,
Edit Profile Manage Email Subscriptions Moderation Admin Console How to Post to DZone Article Submission Guidelines
View Profile
Sign Out
Refcards
Trend Reports
Events
Zones
Culture and Methodologies Agile Career Development Methodologies Team Management
Data Engineering AI/ML Big Data Data Databases IoT
Software Design and Architecture Cloud Architecture Containers Integration Microservices Performance Security
Coding Frameworks Java JavaScript Languages Tools
Testing, Deployment, and Maintenance Deployment DevOps and CI/CD Maintenance Monitoring and Observability Testing, Tools, and Frameworks
Culture and Methodologies
Agile Career Development Methodologies Team Management
Data Engineering
AI/ML Big Data Data Databases IoT
Software Design and Architecture
Cloud Architecture Containers Integration Microservices Performance Security
Coding
Frameworks Java JavaScript Languages Tools
Testing, Deployment, and Maintenance
Deployment DevOps and CI/CD Maintenance Monitoring and Observability Testing, Tools, and Frameworks
  1. DZone
  2. Data Engineering
  3. Big Data
  4. Python vs. R: Which Should You Choose For Your Next ML Project?

Python vs. R: Which Should You Choose For Your Next ML Project?

Let's look at Python vs. R and whether or not one is better than the other when it comes to planning a Machine Learning or data science project.

Raj Ven user avatar by
Raj Ven
·
Aug. 13, 18 · Opinion
Like (1)
Save
Tweet
Share
22.25K Views

Join the DZone community and get the full member experience.

Join For Free

Data science is all about capturing data in an insightful way, whereas Machine Learning is a key area of it. Data science is a fantastic blend of advanced statistics, problem-solving, mathematics expertise, data inference, business acumen, algorithm development, and real-world programming ability. And Machine Learning is a set of algorithms that enable software applications to become more precise in predicting outcomes or take actions to separate it without being explicitly programmed.

The distinction between data science and Machine Learning is a bit fluid, but the main idea is that data science emphasizes statistical inference and interpretability, while Machine Learning prioritizes predictive accuracy over model interpretability. And for both data science and Machine Learning, open source has become almost the de facto license for innovative new tools.

Are you planning a Machine Learning or data science project and confused between Python and R? Both are open source, free, and develop robust ecosystems of open-source tools and libraries that help to perform analytical works more easily. So, let's have a look at whether Python or R is better for data science considering Machine Learning and Artificial Intelligence are included in the term data science.

Python vs. R

For data science and Machine Learning, the best programming language that comes to mind will be R and Python, but choosing between them is always a dilemma.

Python originated in the late 1980s, as an open source scripting language with a built-in object-oriented programming. It has been used in applications such as Dropbox, YouTube, Instagram, and Quora. Python also plays a key role in Google’s internal infrastructure.

Python has more sports libraries (numpy, scipy, and matplotlib) and functions for almost any statistical operation/model building. After the introduction of pandas, it has become very strong in operations on structured data and also easy to work with time series data and data frames. With Anaconda from Continuum Analytics, the package management has become very easy to use. The notebook IDE of IPython/Jupyter is also a very right choice.

R is an open source counterpart of SAS followed by the Python’s footsteps, which has traditionally been used in research and academics and is a very cost-effective option. RCPP makes it very easy to extend R with C++. RStudio is a mature and excellent IDE. Because of the open-source nature of R, the latest updates will get released quickly.

Theoretical the difference between Python and R is large. Python is a full-service language developed by Unix scriptwriter where R is a tool for data analysis developed by GNU packages similar to the S language. Let’s discuss more about Python and R for data science and Machine Learning.

What About Python and R For Data Science/Machine Learning Projects?

Python is a general-purpose programming language, and if your project requires more than just statistics (for instance, building a functional website), it's a better choice. On the other hand, R includes fewer statistical model packages to gain a better understanding of the underlying details and build something truly innovative.

Here is an in-depth overview for whether to choose python or R:

Libraries

Python has a high number of useful libraries for data wrangling, collection, manipulation, and Machine Learning. For instance, Scikit-learn contains tools for data mining and analysis enhances Python's excellent Machine Learning usability. Another package called Pandas offers high-performance structures and data analysis tools along with a shorter development cycle. RPy2 is the right package if your development team needs one of R's major functionalities.

Just like Python, R has over 5000 libraries and tools catering to many domains, that improves its performance in Machine Learning projects. For instance, Caret gives added value to R's Machine Learning capabilities with its set of functions that make creating more efficient predictive models. With R you can take advantage of advanced data analysis packages that cover the pre-modeling, modeling, and post-modeling stages, and are directed to specific tasks such as data visualization or model validation. The network of statistical model packages for R is more extensive than in Python.

Integration

Python integrates better than R in project environments. Even if you take benefit of a lower-level language such as C, C++, or Java, along with a Python wrapper allows better integration with other components. Also, a Python-based stack can easily integrate the work into production for bringing it smoothly.

Productivity

Python is a lightweight, fast, easy-to-use binary format for file types. And its syntax is highly readable like other programming languages, whereas the syntax of R is different. As simply as possible, python push data frames in and out of memory. In contrast to R, Python's readability ensures high productivity of development teams i.e. 600 MB/s vs 70 MB/s of CSVs. Python also helps is passing data from one language to another. Using R's non-standard syntax, you risk disruptions in the programming process.

Early Adopter

Both the languages are interpreted languages. If you're at the early stages of your project and need exploratory work in statistical models, with just a few lines of code R makes it easier to write them than Python. Also, both of them have good IDEs (For instance Spyder for Python and RStudio for R).

Speed

With the introduction of R by Revolution Analytics, the initial struggle with large computations (say, like nxn matrix multiplications) is addressed. Now intensive computational operations are written in C which is rapidly fast. Being a high-level language Python is relatively slow compared to R.

Visualizations

In data science, it always tends to plot data in patterns to users. Therefore, visualizations become an important criterion in choosing a software and R completely kills Python in this regard.

Big Data Handling

One of the constraints of R is it stores the data in system memory (RAM). So, when you are handling Big Data RAM capacity becomes a constraint. Python does well, but as both R and Python have HDFS connectors, leveraging Hadoop infrastructure would give a substantial performance improvement.

Consistency

As R algorithms from third parties, you might end up with many inconsistencies. With R you need to use a new algorithm every time for development and also need to implement new ways to make predictions and model data. In a similar way, requires learning for every new package. And R's documentation limited and it doesn’t help much. All these have a negative impact on development while using R. Here Python scores with its wider developer community and flexible model.

When to Use

Python is a top pick if your project needs a flexible, multi-purpose programming language with a large community of developers and extendable with Machine Learning packages.

If your project is statistics-heavy, R is a better choice for the task. R is also an excellent choice for projects that require a one-time dive into a dataset. For instance, if you want to analyze a collection of text by deconstructing paragraphs into words or phrases and identifying patterns, R is a right choice.

R is an excellent choice if data analytics or visualization is at the core of your project. It enables rapid prototyping and working with datasets to develop Machine Learning models.

Final Verdict

When it comes to Machine Learning/data science projects, both Python and R have their advantages with the extensive availability of packages. Once you master both the languages, you can make the best of both worlds because the majority of the common tasks associated with one of these languages are feasible in both.

In data manipulation and repetitive tasks, Python performs better and it's definitely the apt pick if you're planning to build a digital product based on Machine Learning. Even so, choose R if you're at the initial stages of your project and need to develop a tool for ad-hoc analysis and dataset exploration, unless you possess a team which is well-versed in Python.

Or you can use Python for the early stages of data aggregation and then feed the data into R, which applies the well-tested, optimized statistical analysis routines built into the language. This way, you can use R as a library for Python or Python as a pre-processing library for R. Now you can decide.

Machine learning R (programming language) Python (language) Data science Big data Open source

Published at DZone with permission of Raj Ven. See the original article here.

Opinions expressed by DZone contributors are their own.

Popular on DZone

  • The 12 Biggest Android App Development Trends in 2023
  • An Introduction to Data Mesh
  • Asynchronous HTTP Requests With RxJava
  • When Scrum Feels Like Dressing for Dinner

Comments

Partner Resources

X

ABOUT US

  • About DZone
  • Send feedback
  • Careers
  • Sitemap

ADVERTISE

  • Advertise with DZone

CONTRIBUTE ON DZONE

  • Article Submission Guidelines
  • Become a Contributor
  • Visit the Writers' Zone

LEGAL

  • Terms of Service
  • Privacy Policy

CONTACT US

  • 600 Park Offices Drive
  • Suite 300
  • Durham, NC 27709
  • support@dzone.com
  • +1 (919) 678-0300

Let's be friends: