R is a programming language and environment commonly used in statistical computing, data analytics and scientific research. It is one of the most popular languages used by statisticians, data analysts, researchers and marketers to retrieve, clean, analyze, visualize and present data.
The core of R is an interpreted computer language which allows branching and looping as well as modular programming using functions. R allows integration with the procedures written in the C, C++, .Net, Python or FORTRAN languages for efficiency.
Due to its expressive syntax and easy-to-use interface, it has grown in popularity in recent years.
R holds a reputation for getting things done with very little code. If you’re a programmer and thinking “Here comes the Hello World code”, you’re in for a surprise.
In just three lines of code, your first R program will generate 10,000 numbers in a random distribution, organize them based on frequency and create a fancy bar chart.
FEATURES OF R
As stated earlier, R is a programming language and software environment for statistical analysis, graphics representation and reporting. The following are the important features of R −
- R is a well-developed, simple and effective programming language which includes conditionals, loops, user defined recursive functions and input and output facilities.
- R has an effective data handling and storage facility,
- R provides a suite of operators for calculations on arrays, lists, vectors and matrices
- R provides a large, coherent and integrated collection of tools for data analysis.
- R provides graphical facilities for data analysis and display either directly at the computer or printing at the papers.
WHAT YOU WILL LEARN FROM THIS PROGRAM
- Basic R syntax
- Foundational R programming concepts such as data types, vectors arithmetic, and indexing
- How to perform operations in R including sorting, data wrangling using dplyr, and making plots
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Introduction
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R architecture
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Data types andstructures
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Common data operations
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Functions in R
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Control functions
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Userdefined functions
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Plyr package
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Ggplot 2 package
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Reshape package
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Statistics Introduction
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