What is R?
R is a programming language used for statistical computing and graphics. R is used in Data Science as it provides a wide variety of statistical (linear and nonlinear modeling, classical statistical tests, time-series analysis, classification, clustering, …) and graphical techniques, and is highly extensible. The biggest advantage is that R is open-source, like Python. This means anyone interested can contribute and also learn R.
Let us explore a few advantages and disadvantages of the R programming language and the reason to use R for data science.
Advantages of R Programming Language
- R makes it very easy to manipulate data.
- R is open-source
- R has advanced statistical tools; aids in statistical computation and inference
- R has several packages to help in Data Visualisation and analysis
- R has easy syntax, which makes it easier for beginners to pick up the language
- R is a well-developed, simple, and effective programming language that includes conditionals, loops, user-defined recursive functions, and input and output facilities.
- R supports multiple data types and multiple data structures, which makes it easier to store data.
- R programming language use can be extended for Machine Learning, Data Science projects with the help of advanced packages.
Disadvantages of R Programming Language
There are several disadvantages too associated with the R programming language, R is a bit difficult when compared with Python. Since R was designed for statisticians, it is more used for computational purposes, data analysis like finding the probability, and goodness of the model, evaluating the model through different metrics, and various statistical tests like chi-square, p test, and hypothesis testing Often beginners in data science find it difficult to continue learning R due to a lack of good explanations, resources like good videos, blogs, articles, research papers. On the contrary learning, Python becomes much easier due to the wide availability of resources.
- Limited resources to learn and understand R; as compared to Python
- Later Stages like Production and Building Models become challenging in R
- R is more inclined towards data computation, and statistical inferences making it more specialized in nature.
- Community support in R is less as compared to Python.
Difference Between R Programming and Python
- To know more about the exact difference between R and Python refer to this blog.
- You can also understand the difference by watching this video.
Other Resources
- R Documentation:https://www.r-project.org/about.html
R Course for Beginners
If you are interested in learning R, you can learn more about R here, also you can enroll in the basic fundamental R course. This course covers R packages, functions, and methods in R, you will learn various interesting visualizations using the ggplot2 visualization package in R.