R vs. Python: What’s the Real Difference Between R and Python?

Generally, the ease of learning would primarily depend on your background. 

R is quite hard for beginners to master due to its non-standardized code. The language looks clunky and awkward even to some experienced programmers. On the other hand, Python is easier and features a smoother learning curve, though statisticians often feel that this language focuses on seemingly unimportant things.

So, the right programming language for your data science project will be the one that appears closer to the way of thinking about data you’re used to.

For instance, if you prefer ease and time-efficiency over everything else, then Python might seem more appealing to you. The language demands less coding time, thanks to its syntax that’s similar to the English language.

It’s a running joke that the only thing that pseudo-code needs to become a Python program is saving it in a .py file. This allows you to get your tasks done quickly, in turn giving you more time to work with Python. Additionally, R’s coding requires an extended learning period.

Python and R are both popular. However, Python is used by a broader audience than R. R in comparison to Python is considered a niche programming language. Many organizations, as stated earlier, use Python for their production systems.

R, on the other hand, is generally used in the academia and research industry. Though industry users favor Python, they are starting to consider R due to its prowess in data manipulation.

Both R and Python offer thousands of open-source packages you can readily use in your next project.

R puts forward a CRAN and hundreds of alternative packages to perform a single task, but they are less standardized. As a result, the API and its usage greatly varies, making it hard to learn and combine.

Additionally, the authors of highly specialized packages in R are often scientists and statisticians and not programmers. This means the outcome is simply a set of specialized tools designed for a specific purpose, such as DNA sequencing data analysis or even broadly defined statistical analysis.

However, R’s packages are less mix-and-match than Python’s. Currently, some attempts are being made to orchestrate suites of tools, like tidyverse, which gather packages working well together and using similar coding standards. When it comes to Python, its packages are more customizable and efficient, but they’re typically less specialized toward data analysis tasks.

Nevertheless, Python does feature some solid tools for data science like scikit-learn, Keras (ML), TensorFlow, pandas, NumPy (data manipulations), matplotlib, seaborn, and plotly (visualizations). R, on the other hand, has caret (ML), tidyverse (data manipulations), and ggplot2 (excellent for visualizations).

Furthermore, R has Shiny for rapid app deployment, while with Python, you will have to put in a bit more effort. Python also has better tools for integrations with databases than R, most importantly Dash.

In simple words, Python will be the ideal choice if you’re planning to build a full-fledged application, though both choices are good for a proof of concept. R comes with specialized packages for statistical purposes, and Python is not nearly as strong in this particular field. Additionally, R is very good at manipulating data from most popular data stores.

Another aspect worth mentioning here is maintainability. Python allows you to create, use, destroy, and duplicate a wild and vibrant menagerie of environments, each with different packages installed. With R, this happens to be a challenge, only exacerbated by package incompatibilities.

Experts often use Jupyter Notebook, a popular tool for scripting, rapid exploration, and sketch-like code development iterations. It supports kernels of both R and Python, but it’s worth mentioning that the tool itself was written and originated in the Python ecosystem.

R was explicitly created for data analysis and visualization. Hence, its visualizations are easier on the eyes than Python’s extensive visualization libraries that make visualizations complex. In R, ggplot2 makes customizing graphics far simpler and more intuitive than in Python with Matplotlib.

However, you can overcome this issue with Python using the Seaborn library that offers standard solutions. Seaborn can help you achieve similar plots to ggplot2 with relatively fewer lines of code.

Overall, there are disagreements about which programming language is better for creating plots efficiently, clearly, and intuitively. The ideal software for you will depend on your individual programming language preferences and experience. At the end of the day, you can leverage both Python and R to visualize data clearly, but Python is more suited for deep learning than data visualization.

Python is a high-level programming language, meaning it’s the perfect choice if you’re planning to build critical applications fast. On the other hand, R often requires longer code for even simple processes. This significantly increases development time.

When it comes to execution speed, the difference between Python and R is minute. Both programming languages are capable of handling big data operations.

Though either R or Python aren’t as fast as some compiled programming languages, they circumvent this issue by allowing C/C++-based extensions. Additionally, communities of both languages have implemented data-managing libraries leveraging this feature.

This means data analysis in Python and R can be done at C-like speed without losing expressivity or dealing with memory management and other low-level programming concepts.

• R is a comfortable and clear language for professional programmers, since it was mainly created for data analysis. Therefore, most specialists are familiar with how the language works.
• Checking statistical hypotheses only takes a few lines of code with R, as many functions necessary for data analysis come as built-in language functions. (But remember that this does come at the cost of customizability.)
• RStudio (IDE) and other essential data processing packages are easy to install.
• R has many data structures, parameters, and operators that involve many things—from arrays to matrices, recursion, and loops alongside integration with other programming languages like Fortran, C, and C++.
• R is primarily used for statistical computations. One of its primary highlights is a set of algorithms for machine learning engineers and consultants. In addition, it is used for classification, linear modeling, time series analysis, clustering, and more.
• R puts forward an efficient package repository and an extensive array of ready-made tests for almost all types of data science and machine learning.
• There are multiple quality packages for data visualization for various tasks. For example, users can build two-dimensional graphics and three-dimensional models.
• Basic statistical methods are executed as standard functions that boost the development speed.
• With R, you can find numerous additional packages for every taste—whether you want a package with data from Twitter or one for modeling pollution levels. Every day, more and more packages reach the market, and all of them are collected under a single roof: the special CRAN repository.

Like any other programming language, R comes with a few disadvantages.

• Typically, the R programming language offers low performance, though you’ll still be able to find packages in the system that allow a developer to improve the speed.
• Compared to other programming languages, R is highly specialized, meaning skills in it can’t be as easily applied to other fields than data processing.
• As most of the code in R is written by people who aren’t familiar with programming, the readability of quite a few programs is questionable. After all, not every user sticks to the guidelines of proper code design.
• R is the perfect tool for statistics and standalone applications. However, it doesn’t work that well in areas where traditional general-purpose languages are used.
• You can use the same functionalities of R in various ways, but the syntax for several tasks isn’t entirely obvious.
• As there’s an extensive number of R libraries, the documentation of a few less popular ones can’t be considered complete.

Python is widely used for its simplicity, but that doesn’t mean it has low functionality.

• Being a multipurpose language, Python is great for data processing. The language comes in handy there especially because it facilitates easy development of a data processing pipeline where the results are incorporated into web applications.
• Programmers find Python particularly beneficial due to its interactivity that’s crucial for testing hypotheses interactively in data science.
• Python is being actively developed. With every new version, the performance and syntax keep improving. For instance, version 3.8 featured a new walrus operator, which is quite the event when it comes to any language. In other languages like Java and C++, the rate of change is comparatively slower—changes need to be approved by a special committee that holds meetings every few years. Python changes are proposed by PEPs, and make it into the language often even after a single release cycle, which is one year. In simple words, this means Python is evolving faster than R.

• When it comes to choosing software for data analysis, visualization is a vital capability you should consider. However, while Python has an extensive list of libraries for visualization, choosing a single option can be too overwhelming. Furthermore, visualization in Python is often more complicated than in R, and its results are also not entirely clear sometimes.
• Python lacks alternatives for most R libraries, which makes statistical data analysis and/or R-to-Python conversion challenging.