If you are a senior data scientist or pro in predictive analytics, you would probably be using both R & Python, and maybe other tools like SAS, SQL etc. But, what if you are a beginner or just thinking about to start a career in data science, machine learning, and business analytics? Which one should you learn – R or Python? It has always been a topic of great debate among data scientists, researchers and analytics professionals. In this article, we will discuss R vs Python – usability, popularity index, advantages & limitations, job opportunities, and salaries. In this article, you will get to know r vs python for data science, r vs python for machine learning, r vs python for data analysis etc.
R is a statistical and visualization language that is deep and huge and mathematical. R was developed in 1992 and was the preferred programming language of most data scientists for years. R makes it possible to find a library for whatever analysis you want to perform. The rich variety of libraries makes R the first choice for statistical analysis, especially for specialized analytical work. Additionally, one of the standout features of using R is you can create beautiful data visualization reports and communicate the findings.
Check out the Data Science Certification Course using R by Edureka
Python is based on C, it is a software development language that is deep and huge, and intuitive. It is easier to learn than many other languages, and you don’t need to be totally fluent in order to make use of it for genomics or other biological data analysis. It can do some statistics and is a great scripting language to help you link your workflow or pipeline components together.
Python was released in 1989 with a philosophy that emphasizes code readability and efficiency. It is an object-oriented programming language, which means it groups data and codes into objects that can interact with and modify one another. Java, C++, and Scala are other examples.
Python is a tool to deploy and implement machine learning at a large scale. It can pretty much do the same tasks as R: data wrangling, engineering, feature selection web scrapping, app, and so on. But, Python codes are easier to maintain and more robust than R. It provides cutting-edge API for machine learning or Artificial Intelligence.
Most of the data science jobs can be done with five Python libraries: Numpy, Pandas, Scipy, Scikit-learn, and Seaborn. Additionally, Python makes reproducibility and accessibility easier than R. If you need to use the results of your analysis in an application or website, Python is the best choice.
Check out the Best Python Online Courses and Python Certification Training for Data Science from Edureka
According to Chris Groskopf, Quartz’s former Data Editor, Python is better for data manipulation and repeated tasks, while R is good for ad-hoc analysis and exploring datasets.
He further added that from pulling the data, to running automated analyses over and over, to producing visualizations like maps and charts from the results, Python was the better choice when he was working on elections coverage.
“If I had done the analysis in R, then I would have had to switch to a different tool to create the website and automate the process, but Python also works well for those things,” he says.
In contrast, R is good for statistics-heavy projects and one-time dives into a dataset. Take text analysis, where you want to deconstruct paragraphs into words or phrases and then identify patterns.
“I often don’t know where I’ll end up when I start a process like that, and R makes it easy to try a lot of different ideas quickly,” Groskopf says. “In Python, I would inevitably end up writing a bunch of generic code to solve this pretty narrow problem.”
R has a steep learning curve, and people without programming experience may find it overwhelming. Python is generally considered easier to pick up.
Python is a great go-to tool for programmers and developers.
Another advantage of Python is that it is a more general programming language: For those interested in doing more than statistics, this comes in handy for building a website or making sense of command-line tools. Python is a pure player in Machine Learning. But, Python is not entirely mature (yet) for econometrics and communication.
Python is the best tool for Machine Learning integration and deployment, but not for business analytics.
R is meant for academicians, scholars, and scientists. R is designed to answer statistical problems, machine learning, and data science. R is the right tool for data science because of its powerful communication libraries. Besides, R is equipped with many packages to perform time series analysis, panel data and data mining.
When it comes to usage in data science, some data scientists prefer R to Python because of its visualization libraries and interactive style.
R comes with great abilities in data visualization, both static and interactive. Interactive visualization built with R packages like Plotly, Highcharter, Dygraphs, and Ggiraph take the interaction between the users and the data to a new level.
Since R was built as a statistical language, it suits much better to do statistical learning. It represents the way statisticians think pretty well, so anyone with a formal statistics background can use R easily.
But, if you are looking for higher performance or structured code Python is the go-to language. It is because Python has some of the best libraries such as SciKit-Learn, IPython, numpy, scipy, matplotlib, etc.
NumPy is the foundational library for scientific computing in Python, and it introduces objects for multi-dimensional arrays and matrices, as well as routines that allow developers to perform advanced mathematical and statistical functions on those arrays with fewer codes. Matplotlib is the standard Python library for creating 2D plots and graphs.
Python is also a better choice for machine learning with its flexibility for production use, especially when the data analysis tasks need to be integrated with web applications. For rapid prototyping and working with datasets to build machine learning models, R inches ahead. Python has caught up some with advances in Matplotlib but R still seems to be much better at data visualization (ggplot2, htmlwidgets, Leaflet).
Additionally, Python is also great if you want to do a lot of software engineering. It integrates much better than R in the larger scheme of things in an engineering environment. However, to write really efficient code, you might have to employ a lower-level language such as C++ or Java, but providing a Python wrapper to that code is a good option to allow for better integration with other components.
Related: So You Think You Can Become A Data Scientist?
Till 2015-2016, R has been more popular. But, in the last 2 – 3 years, Python gained tremendous popularity. Burtch Works did a comprehensive survey of data scientists and analytics professionals to determine which tool they prefer to use – SAS, R, or Python. KDnuggets also did another survey to figure out the top platforms among data scientists and analytics professionals. Have a look at the results below.
The seasoned pros use R (and SAS) more. In contrast, entry-level data scientists prefer using Python which is no surprise as Python is easier to pick up. Predictive Analytics Professionals prefer using SAS. While for the Data Scientists, Python is a clear winner. Additionally, the usage and popularity also vary from industry to industry and by education level. Have a look at the graphs below.
The figure below shows the number of data science jobs by programming language. SQL is the most in-demand language, followed by Python and Java. R is the fifth most popular language. However, if we focus on the long-term trend between Python (in orange) and R (in blue), we can see that Python is becoming increasingly more popular than R.
In terms of salaries, the average annual salaries were $99,000 (R) and $100,000 (Python).
Below are the findings from the Analytics India Annual Salary Study that aims to understand a wide range of current and emerging compensation trends in Analytics & Data science organizations across India.
Knowledge of multiple tools will obviously allow you to earn more. Have a look at the chart below (data from 2016 – 2017).
R Programming for Absolute Beginners
Data Science and Machine Learning with R
R Programming A-Z for Data Science with Real Exercises
R Programming for Statistics and Data Science
Text Mining, Scrapping, and Sentiment Analysis with R
Mastering Data Visualization with R (using R Base Graphics, Lattice Package, and ggplot/GGPlot2)
Data Science with Python for Students and Beginners
Mastering Machine Learning with Python from Scratch
Introduction to Data Science in Python
Python for Data Science and Machine Learning Bootcamp
Applied Machine Learning in Python
Machine Learning with Python by IBM
Machine Learning A-Z™: Hands-On Python & R In Data Science
Data Analysis with Pandas and Python
Data Science with Python and Pandas, Numpy, Matplotlib
Data Visualization with Python and Matplotlib
Capstone: Retrieving, Processing, and Visualizing Data with Python
If you are new to data science and have a background in statistics, I recommend learning Python first. Python is a general-purpose programming language that is easy to learn and has a wide range of libraries for data science. You can use Python to build models from scratch, and then use the machine learning libraries to deploy and reproduce your models.
If you already know the algorithms or want to focus on statistical methods, you can start with either Python or R. However, if you want to do more than statistics, such as writing reports and creating dashboards, Python is a better choice. R is a statistical programming language that is better suited for data analysis and visualization.
Ultimately, the best language for you will depend on your specific needs and goals. If you are not sure which language to choose, I recommend starting with Python. It is a versatile language that can be used for a wide range of data science tasks.
The choice between R and Python really depends on your level of knowledge and objective. But, going ahead you need to learn both.
Day-to-day users and data scientists are getting best of both worlds, as R users can run a rPython package within R to run Python code from R, and Python users who are using RPy2 library can run R code from within the Python environment.
Top Platforms and Resources to Learn Data Science and Machine Learning Tools
How to Get Data Science, Machine Learning & AI Jobs in 2018
Data Engineer vs Data Scientist – Background, Responsibilities, Skills, Job Prospects, and Salaries
References: 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12.
Featured Image Source: Working Nation
As a high school student, the decision to pursue a career in engineering can be…
In today's rapidly evolving technological landscape, fields like Artificial Intelligence (AI), Machine Learning (ML), and…
Studying abroad during college is a transformative experience that offers students the opportunity to immerse…
Hey there, high school scholars! If you've ever marveled at how technology seems to understand…
What is the GRE, and why is it important? The GRE (Graduate Record Examination) is…
Preparing for the Medical College Admission Test (MCAT) can be a daunting task, but with…