Both R and Python are much used open-source programming languages. New libraries/tools are adding to their respective catalogs on a regular basis. R is useful for statistical analysis. But, whereas Python offers a more complete data science approach.
In terms of data science languages, R and Python are at the top of the list. Of sure, knowing both of them would be the best option. Both are time-consuming to learn, and not everyone has that luxury. Python is a powerful programming language with an easy-to-understand syntax. The creation of R, but, was by statisticians and includes their special language.
R
The creation of R has been over two decades by academics and statisticians. R now has one of the most diverse ecosystems for data analysis. CRAN contains around 12000 packages (open-source repository). You may locate a library for any type of analysis you would like to conduct. R is the preferred choice for statistical analysis. So, particular for specialist analytical work, due to its extensive library.
The result is the most significant distinction between R and other statistical software. R provides excellent tools for communicating results. Knitr is a library that comes with Rstudio. The creation of this package was by Xie Yihui. He managed to make reporting both simple and elegant. It's simple to communicate the findings through a presentation or even a document.
Python
Python can perform many of the same activities as R . So, it includes data manipulation, engineering, feature selection, web scraping, and app development. Python is a programming language. It can be useful to deploy & install machine learning on a big scale. Python code is more maintainable and robust than R code. It didn't have many data analysis & machine learning libraries a few years ago. It has recently caught up and now offers cutting-edge APIs for ML and AI. Numpy, Pandas, Scipy, Scikit-learn, and Seaborn are five libraries. So, it that can be useful to perform most data science tasks.
Python, but, is more replicable and accessible than R . It is the ideal choice if you're using the results of your study on a website or application.
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Important Differences:
R is mostly useful for statistical analysis. But, whereas Python offers a more comprehensive data science approach.
R's major goal is data analysis & statistics. But, whereas Python's primary goal is deployment and production.
Scholars & R&D professionals are the majority of R users. But, whereas Programmers and Developers constitute the majority of Python users.
R allows you to leverage pre-existing libraries. But, that's not possible in Python.
R is tough to understand at first, but another one is linear & easy to pick up.
So, R has integration with Run, but Python is well-integrated to applications.
Both can handle big databases.
Python is useful with the Spyder & Ipython Notebook IDEs, whereas R is useful with the R Studio IDE.
Python has packages and libraries. For example, pandas, scipy, scikit-learn, TensorFlow, and caret. But, whereas R has packages and libraries. Such as tidyverse, ggplot2, caret, and zoo.
Popularity metric
The IEEE Spectrum ranking is a metric that measures a programming language's popularity. The left column depicts the ranking in 2017, while the right column depicts the ranking in 2016. The rank of Python was first in 2017 after coming in third the year before. R is now in 6th place.
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Use of R or Python
Python was designed in 1991 by Guido van Rossum. It contains many useful libraries for math, statistics, and AI. A python is an act as a pure Machine Learning player. But, it isn't quite ready for econometrics & communication (yet). It is the greatest tool for integrating and deploying ML. But, maybe not for business analytics.
The good news is that the creation of R was with the help of academics and researchers. It's made to solve challenges in statistics, ML, & DS. Because of its extensive libraries, R is an ideal tool for data science. R also comes with many tools for performing time series analysis, and data mining. Furthermore, R is the best tool accessible.
If you're new to data science and don't have the proper statistical background. So, we advise that you ask yourself the following two questions:
Is it important for me to understand how the algorithm works?
Do I wish to put the model into action?
If you answered yes to both questions, you should probably start with Python. On the one hand, it has excellent libraries for matrices and programming algorithms. A beginner may find it easier to learn how to design a model. So, before moving on to functions from ML libraries. But, if you already know the algorithm and want to go right into data analysis, both are fine to start with. If you're going to focus on statistical methods, R has one advantage.
Second, it is a superior choice if you want to do more than statistics. For example, deployment and precision. If you need to generate a report or develop a dashboard, R is a better choice.
In a word, the statistical disparity between both is narrowing. Both languages can handle the majority of the work. You should pick the one that best meets your needs as well as the tool that your co-workers are utilizing. It is preferable if you all speak the same language. Learning the second programming language is easier if you've mastered the first.
Responsibilities of R and Python
The following are the duties of an R Developer:
An R expert creates simulations, analyses data, and outcomes using several R programs.
An R developer contributes to the statistical model's new architecture.
The developer also works with the end-user to build. Also, deploy an analytics solution for project proposals.
The ability to update data and report on it is also vital.
R links to data sources and delivery systems to output data files in various forms.
An R developer's job is to aid other developers in carrying out their ideas. But, while keeping the client's objectives in mind.
R programmers must write code that includes a quality variable. As well as data processing and statistical processes.
A Python programmer's responsibilities are as follows:
To do server-based sums, a developer has to be able to build server-side logic.
A programmer's other major duty is the creation of asset management software.
A developer should be able to create scalable adhesive code. So, to combine different software systems.
The developer is to write reusable & testable code to improve operational quality.
One of the needs of a software solution is data security and software protection. As a result, a Python developer must be able to deal with this.
Event-driven programming is a must for a Python coder.
Another crucial job responsibility is to perform effective unit testing and debugging.
Choosing for a Career
The following are some of the industries that hire R developers:
Academia
Finance
Banking
Healthcare
Manufacturing
E-commerce
IBM, Airbnb, Uber, Twitter, and various public statistical institutions. So, they are among the firms that are hiring R engineers.
The following are some of the industries that hire Python developers:
Development of software
Manufacturing
Robotics
Embedded Systems are computer programs.
Testing and Automation
Hacking with Integrity
Google, Amazon, Dropbox, Pinterest, and many other firms are seeking Python developers.
R's Pros
The major benefit of R is that it is open-source. As a result, you can use R without a license without paying any costs. Because R is open-source, you can add to the customization of R packages. Moreover, for issue resolution.
R is a fantastic tool for manipulating data. Dplyr and reader are capable of translating unstructured data into a proper manner.
R facilitates libraries that seem to be versatile. It is useful for any field that uses data, with over 10,000 items in its CRAN repository.
R includes many useful skills for graph plotting and graph augmentation. Popular libraries like ggplot2 and plotly. They provide users with a wide selection of graph customization chances.
It's a cross-platform programming language. So, it can run on Windows, Linux, and Mac.
R is a programming language designed for statistical modeling. It is the basic tool for developing statistical data science tools. R has a significant edge over other programming languages. Such as Python, because of this.
R is a dynamic language. It has many cutting-edge features. So, they update every time a new algorithm launches.
It boasts a vibrant and dynamic society. R programmers can get advice. Also, support from a variety of online societies in R.
Also, there are many boot camps & online seminars available to prospective R programmers.
R's Cons
The R programming language has roots in the S programming language. So, it is older. As a result, R lacks several modern programming language capabilities. Such as support for dynamic or 3D graphics.
R demands that its objects save in physical memory. When compared to other statistical tools, R's programs demand greater memory. When dealing with Big Data, R is not a good option because it requires the full data to place into its memory.
R does not have basic skills like security because it depends on older technologies. R can't set in web applications. Also, it can't be useful as a backend computation language. E.g., Java, or Node.js.
The learning curve for R is quite severe. R would be perfect for people who have a background in statistics. As a result, those who are new to data science may perceive R to be a challenging language to learn.
R packages are slower than those written in competitive languages. Like Python and MATLAB.
The majority of R algorithms implements in a variety of packages. Because of the transfer of packages, applying algo to issues without prior knowledge of the relevant package is challenging.
Python's Pros
Like R, is a free and open-source programming language. It is a free programming language. Additionally, you can change, customize, and contribute to libraries.
It is a multi-purpose programming language. Also, it is useful for a variety of activities. It is widely useful in software development, robotics, and other fields.
TensorFlow, PyTorch, Keras, and NumPy are a few of the state-of-the-art. APIs that is to build artificial neural networks.
It's an easy-to-use language. One of the key reasons it is the standard language in universities is because of this.
So, it is a safe programming language. It is for these server-side sums. But, since it provides many frameworks for web application development.
It excels at working with huge datasets. It works with Big Data ecosystems and can load data files faster.
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Python's cons
It is slower than other interpreter-based languages. For example, C, C++, and Java because it is interpreter-based language.
When it comes to statistical analysis, it lags behind R . Though it has come a long way, it still lacks some statistical packages when compared to R.
It's dynamically type nature renders it prone to runtime problems.
When compared to JDBC, it's database access layer has no developement.
It has a problem with tasks that need a lot of memory. It's flexible data types add to the language's high memory usage.
Conclusion
The R vs Python debate has come to a close. We went through all the key aspects to help you grasp the differences between both. I hope this info has helped you decide which path to take to become a data scientist.
Programming language clashes are an chance for people to push their preferred language. While mocking those who use anything else. So let me be clear. I have no interest in sparking another internet debate over both for data research.
I hope I've persuaded you that, while both are strong choices for data science. Other concerns such as work history, challenge you to work on. Also, industry culture can change your pick.
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