Python Vs R

In Rplus Analytics, we have delivered so many data science projects in last one decade by using most popular programming tools like Python and R which are amazing tools.

The State of Being Contrast Python and R

Python Overview

?Python is developed by Guido van Rossum in 1989 at the National Research Institute for Mathematics and Computer Science in the Netherlands, Python was a successor of ABC language. Python is derived from many other languages including ABC, Modula-3, C, C++, Algol-68, Small Talk and Unix shell.

Python development process is conducted largely through the Python Enhancement Proposal (PEP) process. PEP is a committee which falls under the Python software foundation. Python Software foundation holds all the IP rights and releases for Python software.

Python is especially great for deploying machine learning at a large scale, as it has libraries with tools like TensorFlow, Scikit learn and keras which enable the creation of sophisticated data models that can be plugged directly into a production system.

Additionally, A Lot of Python libraries support data science tasks listed below:

Astropy: A library featuring functionalities that are ideal for use in astronomy.

Biopython: A collection of non-commercial python tools to represent biological sequences annotations.

Bokeh: Python interactive visualization library that helps create interactive plots, dashboards and data applications quickly.

DEAP: Computation framework perfect for rapid prototyping and testing of ideas.

R Overview

R is a free, open source project designed for statistical computing and data visualization. R was created as an implementation of the programming language S, with scheme-inspired semantics merged into the language. Ross Ihaka and Robert Gentleman were the first to develop this alternative approach to S and formally opened sourced this alternative approach as R in 1993. Rooted in the academic world like many other languages, R has found its way to many organizations and industries as its preferred language for data science.?

The basic installation of the R programming language provides many powerful functions. Its main strength comes from the many add on packages that have been developed by the R programming community.

The popularity of R is constantly increasing and especially in the fields of statistics and social sciences R is becoming the major software tool.

The basic installation of R is already quite powerful when it comes to data visualization. The most popular and important framework for graphics in R is provided by the?ggplot2package.

R Packages are collections of R functions, data, and compiled code. While R comes with a set of packages by default, there are many more packages that can be added to extend the capabilities of R.

Whether you are using R to optimize portfolios, analyse genomic sequences or to predict component failure times, experts in every domain have made resources, applications and code available for free, online.?

Key Differences:

• R is mainly used for statistical analysis, while Python provides a more general approach to data science.
• The primary objective of R is data analysis and statistics, whereas the primary objective of Python is Deployment and Production.
• R users mainly consists of scholars and R&D professionals, while Python users are mostly programmers and developers.
• R is difficult to learn at the beginning, while Python is Linear and smooth to learn.
• R can be used on the R studio IDE while Python can be used on Spyder and Ipython Notebook IDEs.
• Both R and Python can handle size of database.
• R provide flexibility to use available libraries, whereas Python provides flexibility to construct new models from scratch.

Advantages and Disadvantages of Python and R:

In favour of Python:

• Python, by design, a general purpose programming language. The easy syntax increases the speed at which you can write a program.
• Multipurpose applications for a language. Python is not limited to just data science tasks. Other applications such as website development, GUIs, data visualization/dashboards and more are built using Python.
• A low barrier to entry with a simple ease of access to testing ideas immediately with little friction.
• 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.?

Against Python:

• The general purpose of Python can also be seen as a disadvantage. Because it was not designed for a specific use case, it can often be overpowered by other languages like R that more focused on its approach.
• Python lacks alternatives for most R libraries, which makes statistical data analysis and R to python conversion challenging.
• 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.

?In favour of R:

• Rstudio(IDE) and other essential data processing packages are easy to install.
• R has many data structures, parameters and operators that involve many things such as from arrays to matrices, recursion and loops alongside integration with other programming languages like Fortran and C++.
• 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.
• 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.

?Against R:

• Compared to other programming languages, it is highly specialized, meaning skills in it can’t be as easily applied to other fields than data processing.
• 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.
• 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.

Applications Python Vs R:

Learning Curve:

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.

Popularity:

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.

?Packages:

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.

R’s packages are less mix-and-match than Python’s. Currently, some attempts are being made to orchestrate suites of tools, like tidy verse, 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.

?Visualizations:

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, 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.

?Speed and performance:

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.

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.

Final Thoughts, Python Vs R:

Python is relatively easy to learn, as it focuses on simplicity. So, provided you have access to the right tools and libraries, the language can effortlessly take you from statistics to data science and beyond to a full-fledged production app. In fact, this is one of the most significant advantages of using Python.

R’s the most significant advantage is the presence of highly specialized packages that can take you effortlessly through the not so customizable pipelines of data manipulation. However, R was created for statistical computing and people without prior experience find it hard to work with the language initially.