R for data science concentrates on the statistical and graphical applications of the language. You can perform statistical analyses and create data visualizations using R when you learn the language for data science. Data cleansing, import, and analysis are also made simple by R’s statistical functions.
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It might have an Integrated Development Environment installed (IDE). An IDE’s main function, according to computer software provider GitHub, is to facilitate writing and working with software packages. An IDE for R called RStudioExternal link:open in new has a syntax-highlighting editor that aids in code execution and makes graphics more accessible. As you start to learn R for data science, this might be useful.
Features of R – Data Science
Among R’s key attributes for applications in data science are:
- R offers a lot of assistance with statistical modeling.
- R offers aesthetically pleasing visualization tools, making it a good tool for a variety of data science applications.
- In data science applications for ETL, R is widely used (Extract, Transform, Load). It offers an interface for many databases, including spreadsheets and SQL.
- R also offers a number of crucial packages for handling data.
- R allows data scientists to use machine learning algorithms to learn about the future.
- R’s ability to interact with NoSQL databases and analyze unstructured data is a key feature.
