The programming language ‘R’ is the modern version of the S language developed by statistician John Chambers while working at Bell Labs. R was developed by the statistician duo of Robert Gentleman and Ross Ihaka from the University of Auckland. R Programming language is currently ranked #16th in the April 2019 TIOBE index, a measure of search engine queries and developer demand.
R has been around since 1995 and has today become the go-to programming language for data scientists around the globe. It includes several data packages, shell graph functions which makes it an attractive language for data scientists. Top tech companies including Google and Microsoft are using R for large data analysis.
R is popularly used for data analytics and for producing statistical models. The language combined with lexical scoping delivers great results. R programming language has several GNU packages that makes it a powerful package for machine learning.
Here are 5 reasons why R is preferred by data science professionals:
1. Data Analysis
Exploratory data analysis is an important term used in data analysis using R. This includes implementation of various techniques like extraction of important variables, test underlying assumptions, and maximising insights into the dataset. R is mainly used when the data analysis tasks require standalone computing or analysis on individual servers. The language is pretty handy for data analysis due to its high number of packages and readily usable tests.
2. Data Wrangling
Data Wrangling is related to cleaning messy and complex data sets, enabling easier consumption and detailed analysis. It is an important process in data science. R offers a number of library tools for data manipulation and wrangling including dplyr, data.tab, and readr package.
3. Data Visualisation
The visual representation of data in a graphical form is a key in any data science analysis. Data visualisation allow for a comprehensive analysis of data even when the available data is unorganised or in a tabular format. R language has many tools that can help in data visualisation, analysis, and representation.
4. Machine Learning
Programmers need to train the algorithm and bring in automation and learning capabilities to ensure accurate predictions. R offers a number of tools for developers to train and evaluate an algorithm. The language creates a foundation for machine learning.
5. Availability
R is an open source programming language, which makes it highly cost-effective for a wide range of projects. Development happens at a rapid pace while using R. The community support in R offers mailing lists, user contributed documentation, and active stack overflow members.