R also provides support for operations on arrays, matrices, and vectors.R is famous for its graphical libraries that allow the users to delineate aesthetic graphs and make them intractable for the users. R also allows its users to develop their own web applications using R Shiny, which is used for visualizations in web pages.
Why We Choose R for Data Science?
Data Science has emerged as the most popular field of the 21st century. It is because there is a pressing need to analyze and construct insights from the data. Industries transform raw data into furnished data products. In order to do so, it requires several important tools to churn the raw data. R is one of the programming languages that provide an intensive environment for you to analyze, process, transform and visualize information.
Why is R important in Data science
R provides various important packages for data wrangling like dplyr, purrr, readxl, google sheets, data pasta, jsonlite, tidy quant, tidyr etc.
R provides extensive support for statistical modelling. Since Data Science is statistics heavy, R is an ideal tool for implementing various statistical operations on it.
Tibyr :- It allows you to clean and organize your data.
Dplyr :- It allows you to organize, manage and wrangle data.
R is a very good tool for analysis in data science. It is easy to learn and is open source. It is having good community support. It has a lot of packages and libraries for data science. It can be easily integrated with popular databases. It has a statistical computing and graphics environment.
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