Data miners and statisticians frequently use the R programming language for statistical computing when analysing data. The name "R" was derived from the first letters of Ross Ihaka and Robert Gentleman's words, and it was created in 1995.
R is a popular choice for statistical computation and graphical tools in data analytics and data science.
Why Do We Choose R for Data Science?
The field of data science has become the most well-liked in the twenty-first century. It's because it's urgent to analyse the data and draw conclusions from it. Raw data is transformed into furnished data products by industries. The raw data must be processed using several crucial tools to achieve this. One programming language that offers a robust environment for information analysis, processing, transformation, and visualisation is R. For many statisticians who want to create statistical models to address challenging problems, and it is their first choice.
There are countless packages in R that are useful for all types of disciplines, including astronomy, biology, etc. R was initially used for academic purposes, but it is also used in industry.
Why is R important?
R offers several crucial packages for data wrangling, including dplyr, readxl, Google Sheets, datapasta, jsonlite, tidyquant, and tidyr, among others.
R offers a lot of assistance with statistical modelling. Since data science relies heavily on statistics, R is the perfect tool for performing different statistical operations.
Because it offers aesthetically pleasing visualisation tools like ggplot2, scatterplot3D, lattice, highchart, etc., R is a desirable tool for many data science applications. R is widely used in data science applications for ETL (Extract, Transform, Load). (Refer to a data science course for more information on ETL process). It offers an interface for many databases, including spreadsheets and SQL. The ability of R to interact with NoSQL databases and analyze unstructured data is another crucial feature.
Data Science Companies that Use R
For social network analytics, Facebook heavily relies on R. It uses R to establish connections between users and learn more about their behaviour.
For its various daily data operations, Airbnb supports R. The dplyr package is used by R to slice and dice the data. The graphics package ggplot2 is also used to visualise the data. Additionally, it uses the pwr package for various experiments and statistical analyses.
To access its charting components, Uber uses the R package shiny. Shiny is an interactive web application created with R for embedding interactive visual graphics.
R is a popular option for carrying out many analytical tasks at Google. R is used by the Google Flu Trends project to examine trends and patterns in searches for the flu. Google's prediction API also uses R to analyse historical data and predict the future.
One of the biggest banks in Australia is called ANZ. In order to predict loan defaults based on customer transactions and credit scores, it uses R for credit risk analytics.
A significant pharmaceutical company, Novartis, uses R to analyse clinical data for FDA submissions.
One of the most prominent investors in R is IBM. It just joined the R collaboration. R has been used on IBM Watson, an open-source computing platform. IBM also uses R to create a variety of analytical solutions. Additionally, IBM supports R missions and contributes significantly to the community's expansion.
In this article, we discussed R's definition and its role in data science. Additionally, we covered several R features and provided information on a number of industries that use R for data science. Ultimately, we can say that R is the best programming language for data analysis in data science.
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