Did You Know?
- By 2023, the Big Data Analytics market is anticipated to reach USD 40.6 billion, which shows a growth rate of 29.7%
- According to Randstad, there is a salary hike in the analytics industry which is 50% higher for data scientists than the IT industry.
- Glassdoor states that the salary of a data scientist in India at a mid-career is around INR 516,000 and that at the senior-level may reach up to INR 1,023,000.
- Almost all the tech giants such as JPMorgan Chase, Amazon, Microsoft, Accenture, Apple, Facebook, Google, Twitter, PayPal, and many more hire data scientists.
- A survey by Burtch Works Survey, out of all data scientists who were surveyed, 40% prefer R, 34% prefer SAS, and 26% Python.
These facts make Data Science with R courses trending among professionals in order to make a career in the domain which is in demand across the globe.
Specifically designed for data science, R is considered the second most popular language in Data Science. Google Trends also showcases the popularity of R.
You might get confused about which language to master, R or Python, for Data Science. This war is going on for ages in the field of Data Science. While both the languages have their own use cases; pros and cons; there are some specific advantages of both of them.
In this article, we will read why R is the preferred language for Data Science.
What is R?
R is a programming language specifically designed for statistical computing and graphics. R is an integrated suite of software facilities that are used for data manipulation, graphical display, and graphical display. It includes:
- Excellent storage facility and data handling features.
- A huge, logical, integrated collection of intermediate tools utilized for data analysis.
- Graphical facilities enable data analysis and display on hard copy or on-screen.
- A collection of operators to be used for calculating arrays, in particular matrices.
- A full-fledged, simple, and constructive programming language which includes loops, conditionals, loops, user-defined recursive functions, and input and output functions.
Features of R
The R environment is a well-planned, coherent system, unlike other data-analysis software. It allows you to add new functionality or edit the existing one, by defining new functions. R language is so written that makes it easy for you to follow the algorithmic choices.
Apart from being a statistical system, you can use R for almost anything you want to program. You can extend R very easily through packages. There are around 8 built-in packages that are supplied with the R distribution and many others are available via the CRAN family of websites which cover a wide range of modern statistics.
The documentation format of R is LaTeX-like, which enables you to supply comprehensive documentation in hardcopy as well as online in different formats.
The most important feature of R is that it is an interpreted language. That means you don’t need a compiler to run your code.
It is a vector-based language that allows you to perform multiple calculations.
Python Vs R
|Usage||R is primarily used in academics and research and is an excellent tool for data analysis. Also, it is used in enterprises as well.||Python is a tool used for statistical analysis and data analysis|
|Used by||Engineers, statisticians, and data scientists. Also used by professionals in finance, media, pharmaceuticals, marketing, and academia.||It is a production-ready language used by data scientists.|
|Accessibility||R is easier to learn, even if you have no coding experience.
Very few lines are required to write for creating statistical models.
|Because of easy syntaxes, coding and debugging is easy. If you are from a software engineering background, Python is easy for you.|
|Flexibility||R is a flexible language that is easy to use for complex functions in R.
The models are easily available and usable.
|Python is flexible when it comes to creating new functions. It can be used for creating scripting websites and other applications.|
|Ease of Learning||R is best when you are a beginner. It is best suited for non-programmers.||Python is relatively simple and readable. It is good for professionals with a coding background.|
|Popular Libraries||dplyr, tidyr, zoo, ggplot2, caret are some of the popular libraries in R||Pandas, SciPy, NumPy, Scikit-learn are some of the popular libraries in Python.|
Applications of R in Data Science
The most popular application of R that you can find is in academics. R is used for statistical analysis and also experimenting with data science.
The process of cleaning complex data sets so as to enable easy consumption is referred to as data wrangling. R consists of tools for database manipulation and wrangling. The libraries meant for data wrangling are dplyr, readr, data.table packages and more.
Data visualization, analysis, and representation are also facilitated by R with packages such as ggplot2 and ggedit.
In machine learning, when you need tools to train and evaluate an algorithm, R makes your work easy and approachable. The packages for machine learning are MICE, CARET, and more. R is an open-source programming language that is not restricted to operating systems, which makes it highly available.
You would also love to read about: Code Obfuscation Techniques.
After going through the article, you might have got the answer to “Is R easier to learn than Python?”
Of course, the answer is Yes!
As it is suitable for professionals who don’t have any experience in coding.
R is the most preferred language for Data Scientists as it has excellent tools for data analysis and manipulation.
When you look for skills required for data scientists, R is on the top. To make a career in Data Science and learn R, you should get certified by taking up an online training course. There are some accredited online training providers that give you the choice of learning at your own pace. You can choose the mode of learning as well which can be online training, instructor-led and blended learning. Industry experts are there to help you out with the queries and doubts you have. Get yourself registered now!