R vs Python
If you are someone who wishes to make a career in Data Science, then the ultimate question you have to face is, which programming language you should learn and why? There have been numerous discussions on public forums with people advocating for R or Python for plenty of reasons.
Though it completely depends on your choice, comparing both the languages on some grounds will surely help you make the right decision.
This blog will highlight the following key areas:
- R vs Python
- Comparison between R and Python on the basis of following:
- Ease of Installation
- Robustness and Flexibility
- Ease of Learning
- Speed of Processing
- Statistical and Analytics Ability
- Suitable Area
- Reasons to Learn R and Python
- Which is Better for Data Analysis and Data Science?
- Is Python better than R?
- Real-life Use Cases of R and Python
- Career Opportunities in R and Python
- Which Language to Go First?
What are R and Python?
R is an open-source programming language developed for statistical analysis and computations.
Like R, Python is also an open-source programming language that was initially developed as a general-purpose programming language, and later branched out to be a language for Statistical Analysis and Machine Learning Modeling.
Let’s understand the difference between these two highly popular Data Science languages:
Ease of Installation
Well to start with, R packages are solely managed by CRAN (The Comprehensive R Archive Network) repository that manages the updated versions, their installations, and related documentation of R Packages. All the packages you install in R are stored in CRAN. Also, any new package to be added in R should be submitted to CRAN. Currently CRAN has over 16000 additional statistical packages. This is why it is easier to install R.
On the other hand, Python has two package management platforms, Conda and PyPI (Python Package Index) that include over 100k Python packages. There have been inconsistencies found in Packages, Libraries, and Versions while installing Python due to two repositories. Due to this reason, it is a little tedious to install Python.
Want to read about Python in detail? Read this riveting blog!
Robustness and Flexibility
R has all the features that Python has in terms of programming ability, statistical computing and modeling, but Python is more flexible and robust. Python is a better option when it comes to integrating it with web applications and production.
However, R is less robust and versatile, which is why it is limited to statistical computing and mathematical modeling.
Ease of Learning
One of the most frequently asked questions is “Which between R and Python is easy to learn?”.
Both R and Python have almost similar features, but when it comes to syntax, R is a little complicated and is better for someone who is already familiar with other programming language. On the other hand, Python has a relatively simpler and readable syntax and hence, for anyone who is about to start-off with a programming language, Python is a good option.
However, when the model building becomes complicated, it requires someone who is proficient in Python.
Speed of Processing
Usually Python is 8 times faster than R till there are up to 1000 iterations. When the number of iterations increases, R typically surpasses Python’s speed. In comparison to Python, R requires more lines of codes to perform a certain task, which make the programs more complex and bulkier.
Statistical and Analytics Ability
R was designed for statistical computation and Modeling purposes and hence it performs better for any level of complex computation. R has better statistical packages and libraries for dashboard than Python. Python being a general programming language somehow lacks the packages and libraries for Data Science. Python is better suited for modeling and machine learning, which is complicated in R.
The focus of R is primarily into statistical analysis and hence it is better suited to academia and research. On the other hand, Python being the programming language for all purposes is suitable for tech industry. However, Python also comes with packages that can create an environment similar to R.
There are plenty of other grounds that differentiate R from Python. The following table will further clarify that!
The above table must have given you a clear idea about the bases R and Python differ on. But the difference alone will not suffice in answering why you should learn any Programming language. Let’s take a look at reasons why should learn either of these in the following section.
Reasons to Learn R and Python
Before deciding upon the programming language you want to learn, you should consider some pre-requisites as they are going to heavily impact your career choices and the skill-set as a whole.
- What is your career objective?
If you wish to become a machine learning expert or a software development professional, Python will be the right choice for you. However, if you want to conduct a research and test hypothesis with the help of statistics, R will be the right choice for you.
- Do you have experience in programming?
If you are someone who has experience in programming languages, then R will be easier for you to begin with as you will be able to correlate the syntax and the logic. However, if you are a beginner, who has no prior experience in programming, Python is a better option.
Also, even if you have knowledge of Object-Oriented Programming language like Java or C++, then you should go for Python, as it is based on OOP concept.
- What is your end goal?
If you wish to develop an application, implement a machine learning model, or wish to simply create visualization for your data, you should learn Python. On the other hand, if your aim is to do time-series analysis, signal processing, data mining, etc., on the dataset, you should learn R.
Which is Better for Data Analysis and Data Science?
Both R and Python have all the functionalities that you would need for Data Analysis or Data Science projects. However, as said before, it depends on the level of complexity and the analytical functionality you are looking for.
If the business problem requires modeling machine learning algorithms, then Python is the right choice. On the other hand, if business problem includes researching a hypothesis and finding a solution by plotting graphs and charts, then R is the right language for you.
If the dataset is large and complex and requires numerical analysis, then R is a better option than Python, as R was built as a statistical and computational programming language. This gives R better functionalities in this matter. However, if your project involves building machine learning models, then python is better.
One fact that one should keep in mind is that Python is more of a general-purpose language that is known for better readability, modularity, and ability for machine learning and deep learning. It has limited packages for in-depth Data Science and Data Analytics. On the other hand, R has always been the language of choice when it came to data scientists and data analysts because of its vast environment.
Learn Data Analysis and Visualization in R and secure a chance of landing a top-notch job in no time!
Is Python better than R?
The answer to this question is: No. Python and R both have different sets of pros and cons and are suitable for different problem statements. But Python is preferred to R by most of data scientists because of the linear learning curve and flexibility to be integrated into other applications.
However, as the complexity of the problem increases, the proficiency level required for solving the issue increases.
Moreover, it is possible to incorporate R into Python and vice versa. This is called Interoperability. For example, data frames can be written in either language, and can be saved and read by the other seamlessly.
Real-life Use Cases of R and Python
Both R and Python are heavily used in real-world and have a plethora of applications. Take a look below:
• Web Development: Python is used in web application development in a much easier way. It can also be used for writing gaming applications, enterprise-level applications, Machine Learning applications, image processing, text processing, etc.
• IoT: Python is suitable for IoT development as it supports big data, real-time analytics, and much more. Python’s flexibility and embedding ability makes this language the best choice for IoT projects.
• Machine Learning: For Artificial Intelligence and Machine Learning, a language is required that is flexible, stable, secure, and has features that support the unique requirements of Machine Learning projects. Python is filled with packages and libraries which are highly useful for this purpose.
• Fin-Tech: Fin-tech industry is one of the most dynamic industries among all. Hence, using a programming language that is easy to learn, implement, integrate, and optimize is of utmost importance. Therefore, people in this industry use Python as their primary programming language.
Some of the ways in which R is used in real-world are:
- Ecommerce: R is among the most prominent tools used in e-commerce. R is used for cross-product suggestions, where related and/or additional products are suggested. R is also used for targeting the advertisement to a specific set of customers or potential customers, and many more such uses.
- Social Media: Social Media are the platforms where people spend most of their time nowadays and hence, it is exploding with data about the choices and preferences of various users. R programming has been exhaustively used for sentiment analysis, target marketing, behavior analysis and much more.
- Healthcare: During Coronavirus pandemic, Healthcare has emerged as a sector that requires a lot of work. R is specifically useful for Genetics, Bioinformatics, Drug Discovery, Epidemiology, pre-clinical trials, drug safety analysis, and a lot more.
- Banking: R is heavily used for credit risk analysis in the banking industry. At times, for this purpose, SAS is also used along with R.
Career Opportunities in R and Python
As per TIOBE Popularity Index for Programming Languages of May 2020, Python stands at position 3 only after C and Java. Python was at position 4 in 2019, which clearly shows the increase in popularity. On the other hand, R stands at position 10, which was at position 21 last, which also shows a huge spike in the popularity of this language.
According to Indeed, the average Python programmer salary in the US in 2020 is $120,365 per year.
It is clearly visible that Python is getting more popularity than R among the industries. And if we look at the history, since 1995, Python has come a long way in terms of gaining prominence among the programmers. Although R has its own niche, looking at the data, anyone can predict that there will be more job opportunities for Python in the coming years.
Want to crack a Python Interview? These Top Python Interview Questions are your ticket to success!
Which Language to Go First?
Looking at the career prospects and growing popularity, Python is the better option to go for first. But again it all depends on the factors discussed in previous sections.
How to learn R and Python?: Best Resources to learn
Well, once you have made the decision about which language to go first, the first question that pops into your head must be “How to learn Python and R?”. So there are plenty of resources to learn among which some of the popular ones are:
E-book/Guides: There are plenty of e-books and guides that are available on various digital platforms that take you through from installation to the complex programming, step by step. Try this guide for starters:
Online Training Courses: Online training courses are quite popular among all the age groups to learn any new skill. Since this option comes with comfort and convenience, more and more aspirants are inclining towards this option. You can either go for live training sessions or self-paced courses, both of them are designed and taught by industry experts. A popular Python Training Course for beginners:
Which programming language did you decide to go with after reading this post? Let us know with your comments!