R is a state-of-the-art programming languague for statistical computing, data analysis, and machine learning.

You can download additional packages from The Comprehensive R Archive Network ( CRAN).The R project is currently being developed and supported by the R Development Core Team. A standard installation of the R programming languagues comes with 25 of these packages. The features of the R programming languages are organized into packages. Here is a simple interaction with the R command line: > a b a * b Here is an example of lexical scoping in R: x character represents the command line prompt. This means the lexical structure of the program determines the scope of a variable, not the most recently assigned variable. Lexical scoping is another name for static scoping. In other words, the name R describes both the R programming language and the R software development environment used to run R codes. R is both a programming language and a software development environment. R allows integration with Python, C, C++. Most R packages are written in the R programming language, but heavy computational chucks are written in C, C++, and Fortran.

The R progamming languague is written in C, Fortran, and R itself. The R environment is designed to perforrm complex statistical analysis and display results using many visual graphics.

It's an alternative implementation of the S programming language, which was widely used in the 1980s for statistical computing. R is a free and open-source scripting language developed by Ross Ihaka and Robert Gentleman in 1993. R, Python, and Julia are popular programming languagues in data analytics and data science. Powerful programming languagues are necessary to perform such tasks. Mathematical or statistical analysis cannot use them either. Business Intelligence applications can't perform such transformations. However, most of the time, data comes in unstructured format and needs preprocessing and transformation. Data science applies mathematical analysis, statistical techniques, and machine learning algorithms to extract insight from data.īusiness Intelligence (BI) applications like Power BI and Tableau help with analyzing and visualizing data. Data analytics studies how we collect, process, and interpret data.

Transactional data from invoices, payment orders, payment methods, discountsīusinesses and organizations can gain a competitive advantage by analyzing large amount of data ( 'big data') to reveal patterns and gain insights.

Machine data from medical devices, satellites, web logs.

Social data from Facebook posts, tweets, google trends.

The primary sources of these data are the following: The amount of data generated daily is expected to reach 463 exabytes by 2025. At the beginning of 2020, the amount of data in the world was estimated at 44 zettabytes.

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