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Data science using R

R is a programming language which is free and open source. It offers various statistical and graphical techniques. It is compatible across all the patforms.It has an extensive library of packages for machine learning. It can be easily integrated with popular software’s like tableau, SQL server etc.

Data science is an interdisciplinary field that uses scientific methods, processes, algorithms and systems to extract knowledge and insights from many structural and unstructured data In order to do so, it requires several important tools to use the raw data. R is one of the programming languages that provide an intensive environment for you to analyze, process, transform and visualize information.

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

  1. R provides various important packages for data wrangling like dplyr, purrr, readxl, google sheets, data pasta, jsonlite, tidy quant, tidyr etc.
  2. 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.
  3. R is an attractive tool for various data science applications because it provides aesthetic visualization tools like ggplot2, scatterplot3D, lattice, highcharter etc.
  4. R is heavily used in data science applications for ETL (Extract, Transform, Load). It provides an interface for many databases like SQL and even spreadsheets.
  5. Another important ability of R is to interface with NoSQL databases and analyze unstructured data. This is very useful in Data Science applications where a pool of data has to be analyzed.
  6. With R, data scientists can apply machine learning algorithm’s to gain insights about future events. There are various packages like rpart, CARET, random Forest, and nnet.

Features of R includes:-

  1. Open source : Free to develop your own libraries etc.
  2. Oops : it also contains several oops features in the language.
  3. Analytical support : we can perform analytical operations.
  4. Supports extensions : we can develop our own libraries and packages
  5. Facilities interaction with databases : it consists of several add-on packages that connect R with databases like the RODBC package.
  1. Extensive community support : it has active community support
  2. Simple and easy to understand: it has easy to understand syntax which allows us to remember and understand R properly.

List of R Packages | Complete Guide to the Top 16 R Packages

Packages of R for Data science includes :-

  1. Ggplot2 :- Most famous library for visualization.
  2. Tibyr :- It allows you to clean and organize your data.
  3. Dplyr :- It allows you to organize, manage and wrangle data.

Conclusion:-

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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