R Vs Python: Which is the Best Programming Tool for Machine Learning and Data Science Applications?

Harikrishna Kundariya, Linux.com Contributor

Data science is one of the most promising career options today. It’s also clear that data is a new force.

Businesses all over the world receive tons of data from their customers, from various metrics and other sources. Analyzing this data to make data-driven decisions is critical to beating your competition.

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Data science and data analysis are essential, and if you want to become a skilled data scientist, you need to master at least one programming language.

For example, SQL, Structured Query Language, is a universal language for almost all relational databases. So you have to learn it. It is a prerequisite.

However, SQL only allows you to retrieve data. To process or analyze data, you need to learn R or Python. Sometimes companies face the dilemma of hiring Python or R developers.

This blog simplifies the confusion. We will discuss the two languages ​​to help you choose the right machine learning and data science tool and the intended application.

Before discussing what language data scientists need, let’s briefly learn about the two languages.

What is the Python language?

Python is one of the most popular and preferred programming languages, allowing for higher throughput and better code readability.

Created by Guido Van Rossum in 1991, the Python language is widely used by data scientists for statistical purposes. It is a very versatile and flexible language with a low learning curve.

In addition to this, Python also offers some amazing packages such as PyPi. Also, it has community libraries where users can make suggestions and contributions.

Python is one of the most popular programming languages ​​for data scientists due to its simplicity and readability.

What is R?

R is an open source programming language founded by Ross Ihaka and Robert Gentleman in 1995. It started as an open source implementation of the S programming language along with the semantics of the lexically classified Scheme programming language.

The main goal of developing R was to provide a language for developers that helps with data analysis, statistics, and data science. Previously, the use of R was restricted to academics and business research, but today it is one of the fastest growing languages ​​for data analysis and statistical analysis.

R has a very large community where users contribute a lot. You can find support documents, mailing lists, and the very active Stack Overflow group.

R also has packages like CRAN. It allows developers to access the latest data science technologies and features without writing code.

Comparison between R and Python

This comparison will give you an answer on whether you want to hire Python developers or R developers for your project.

Use in data science and data analysis

One of the main differences that you need to understand is how these open source languages ​​are used in the field of data science.

Python is not just data science. It is a language similar to Java and C++ which can be used in other fields such as web and application development.

Most of the time, developers use Python for machine learning and data analysis in higher production environments. For example, if you want to create a facial recognition feature in your phone app, you can use Python.

On the other hand, R is a programming language that you will only find in the field of data science. It is intended for statistical data analysis only. The language is developed by professional statisticians and has superior statistical models and specialized analytics.

R offers great benefits such as data visualization, in-depth statistical analysis, genomic research, and consumer behavior analysis.

The two main differences are that R is a dedicated tool Data science programming languageAnd Python is a file A versatile programming language.

Data collection

When it comes to data formats, Python supports almost all data formats, such as data from JSON, comma separated values, and others. In addition, it also allows developers to import SQL tables into Python code.

On the other hand, R is specially designed for data scientists and analysts as it allows importing data from Microsoft Excel, Google Sheets, CSV, and text files. In addition, you can also convert SPSS files to R dataframes.

Here, Python is more versatile and flexible in extracting data from the Internet.

mining data

Pandas is a Python data analysis library used for data mining. With it, you can easily filter, sort and display data.

On the other hand, R can be used to analyze data quickly even for larger data sets. Moreover, you have a wide range of options for data exploration.

You can use machine learning techniques, data mining, and standard analytics. You can also apply various data statistics tests and generate probability distributions.

In short, R is more flexible for data exploration than Python.

data modeling

There are three main Python libraries for data modeling as shown below:

• Numpy for Numerical and Statistical Data Modeling Analysis
• SciPy for scientific and analytical calculations and calculations
• Scikit-Learn for Machine Learning Algorithms

On the other hand, when using R, you may need to rely on external packages to model the data. R contains Tidyverse, which is a set of data analysis packages for importing, visualizing, modeling and reporting data.

Visual display of information

Python loses when it comes to data visualization because it is not its primary skill.

However, you can create basic charts and graphs using the Matplotlib library in Python.

On the other hand, R is specifically designed for data visualization and allows you to create graphs, charts and charts for statistical analysis.

In addition, GGPLOT2 allows developers to create complex point clouds with clear regression lines.

conclusion

Python and R are both widely used for data science and machine learning.

However, one thing to remember here is that Python is a versatile and flexible general-purpose language with an easy-to-read syntax that is suitable for developers.

If you are a developer, choosing Python is a good idea with its low learning curve.

On the other hand, R is a complex language to learn with its advanced features and functionality. If you are a data scientist with a statistical background, you can easily learn R and use it for data analysis.

R is a great choice for statistical learning and data analysis, while Python is best suited for machine learning and wide-ranging applications.

Use Python developers to build scalable applications when you want to analyze data in a web application environment.