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R features


Why we use R:


}  Availability
R programming language is open source. This makes it highly cost effective for a project of any size. Since it is open source, developments in R happen at a rapid scale and the community of developers is huge
}  Data wrangling
Data wrangling is the process of cleaning messy and complex data sets to enable convenient consumption and further analysis. This is a very important and time taking process in data science. R has an extensive library of tools for database manipulation and wrangling. For  Example  dplyr,stingr.
}  Data visualization
Data visualization is the visual representation of data in graphical form. This allows analyzing data from angles which are not clear in unorganized or tabulated data. R has many tools that can help in data visualization, analysis, and representation.
}  Specificity
R is a language designed especially for statistical analysis and data reconfiguration. All the R libraries focus on making one thing certain - to make data analysis easier, more approachable and detailed. Any new statistical method is first enabled through R libraries. This makes R a perfect choice for data analysis and projection
}  Machine learning
In data science, a programmer needs to train the algorithm and bring in automation and learning capabilities to make predictions possible. R provides ample tools to developers to train and evaluate an algorithm and predict future events. Some popular packages for this are  third party.
}  Academia
R is a very popular language in academia. Many researchers and scholars use R for    experimenting with data science. Members of the R community are very active and supporting and they have a great knowledge of statistics as well as programming


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