R vs. Python: What’s the best programming tool for machine learning and data science applications?

Harikrishna Kundariya, Contributor to Linux.com

Data science is one of the most promising career choices today. It is also evident as data is a new power. 

Businesses across the globe receive tons of data from their customers, different metrics, and other sources. Analyzing this data to make data-driven decisions is crucial to beating your competitors. 

Data science and data analysis are vital, and if you want to become a skilled data scientist, you need to have a mastery of at least one programming language. 

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

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

This blog simplifies the confusion. We will discuss both languages to help you choose the correct tool for your machine learning and data science career and intended application. 

Before discussing which language is necessary for data scientists, let’s briefly get to know both languages. 

What is Python?

Python is one of the most popular and preferred programming languages, allowing superior productivity and higher code readability. 

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

In addition to that, Python also has some amazing packages available such as PyPi. Also, it has community libraries where users can contribute with suggestions and inputs. 

Python is considered one of data scientists’ most dominating programming languages 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 combined with lexical scoping semantics from the Scheme programming language. 

The main aim of developing R was to offer a language to developers that help in data analysis, statistics, and data science. Earlier, the use of R was limited 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 vast community where users contribute a lot. You can find supporting documents, mailing lists, and a highly active Stack Overflow group. 

R also has packages such as CRAN. It allows developers to access the latest data science techniques and functionalities without writing code. 

Comparison of R vs. Python 

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

Usage in Data Science and Data Analysis 

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

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

Mostly, developers use Python for machine learning and data analysis in superior production environments. For example, if you want to build a face recognition feature in your mobile application, you can use Python. 

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

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

The two primary distinctions are that R is a dedicated data science programming language, and Python is a multi-purpose programming language. 

Data Collection 

Regarding data formats, Python supports almost all data formats, such as JSON-sourced data, comma-separated values, and others. In addition to that, it also allows developers to import SQL tables into the Python code. 

On the other hand, R is dedicatedly designed for data scientists and analysts as it allows importing data from Microsoft Excel, Google Sheets, CSV, and text files. Furthermore, you can also convert SPSS files into R data frames. 

Here, Python is more versatile and flexible in pulling data from the internet. 

Data Exploration 

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

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

You can use standard machine learning, data mining, and analyzing techniques. Also, you can apply various data statistics tests and build probability distributions. 

In summary, R is more flexible for data exploration compared to Python. 

Data Modeling 

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

• Numpy for numerical and statistical data modeling analysis 
• SciPy for analytical and scientific computing and calculations 
• Scikit-Learn for machine learning algorithms 

On the other hand, when using R, you might need to rely on external packages for data modeling. R has Tidyverse, a set of data analysis packages to import, visualize, model, and report on data. 

Data Visualization

Python loses when it comes to data visualization as it is not its core competency. 

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

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

Also, GGPLOT2 allows developers to create complex scatter plots with clear regression lines. 

Conclusion 

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

However, one thing to remember here is that Python is a versatile, flexible multi-purpose language with an easy-to-read syntax that is developer-friendly. 

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 functionalities and features. If you are a data scientist with a statistical background, you can easily learn R and use it for data analysis. 

R is an amazing choice for statistical learning and data analysis, whereas Python is best-suited for machine learning and large-scale applications.  

Hire Python developers to build scalable applications when you want data analysis within a web application environment. 

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