R provides a conducive environment for statistical computing and design by providing a large range of statistics-related libraries. Furthermore, because it is useful for data importing and cleaning, many quantitative analysts utilise the R programming language as a programming tool.
We can use R to do machine learning tasks like classification and regression. R includes a number of tools and functionalities for creating artificial neural networks for this purpose. The top data scientists in the world use R.
R is a simple language to learn. Its primary function is data analysis and processing, and it is meant to make these activities simple. Writing code in R is rather simple; in many circumstances, it's almost a question of translating the arithmetic in your head into code with a similar logical structure.
Both R and Python are widely used open-source programming languages. New libraries or tools are added to their respective catalogues on a regular basis. R is mostly used for statistical analysis, whereas Python offers a more comprehensive data science approach.
You can work as a data analyst, statistician, or data scientist if you have a strong grasp of the R language. These are the highest-paying employment roles on the market right now.
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