R (Programming Language)- A Comprehensive Tool for Data Analytics & Statistical Computing

Introduction to R

R is a popular programming language used for statistical computing and graphical presentation. Its most common use is to analyze and visualize data. [1] R is a powerful programming language and software environment primarily used for statistical computing, data analysis, and graphical visualization.

R is a language and environment for statistical computing and graphics. It is a GNU project which is similar to the S language [7] and environment which was developed at Bell Laboratories (formerly AT&T, now Lucent Technologies) by John Chambers and colleagues. R can be considered as a different implementation of S. There are some important differences, but much code written for S runs unaltered under R. [2] As an interpreted language, R has a native command line interface. Moreover, multiple third-party graphical user interfaces are available, such as RStudio—an integrated development environment—and Jupyter—a notebook interface.

The R environment

R is an integrated suite of software facilities for data manipulation, calculation and graphical display. It includes

• an effective data handling and storage facility
• a suite of operators for calculations on arrays, in particular matrices
• a large, coherent, integrated collection of intermediate tools for data analysis
• graphical facilities for data analysis and display either on-screen or on hardcopy
• a well-developed, simple and effective programming language which includes conditionals
• loops, user-defined recursive functions and input and output facilities [3]

Why Use R?

• It is a great resource for data analysis, data visualization, data science and machine learning
• It provides many statistical techniques (such as statistical tests, classification, clustering and data reduction)
• It is easy to draw graphs in R, like pie charts, histograms, box plot, scatter plot, etc++
• It works on different platforms (Windows, Mac, Linux)
• It is open-source and free
• It has a large community support
• It has many packages (libraries of functions) that can be used to solve different problems [4]

Application of R

• Statistical Analysis: R is primarily used for performing complex statistical computations. It offers a vast array of statistical tests, models, and techniques that are essential for data analysis.
• Data Visualization: R is well-known for its data visualization capabilities. Packages like ''ggplot2'' allow users to create a wide range of static and interactive graphs, charts, and plots.
• Data Mining: R is used in data mining to discover patterns and relationships in large datasets. It supports techniques such as clustering, classification, and regression.
• Bioinformatics: R is extensively used in bioinformatics for analyzing and visualizing biological data, such as genomic sequences and protein structures.
• Machine Learning: R provides tools for implementing machine learning algorithms, including decision trees, random forests, and neural networks, to predict outcomes and classify data.
• Finance and Economics: R is used in finance for time series analysis, risk assessment, and portfolio optimization. Economists use R for econometric modeling and forecasting.
• Social Sciences: Researchers in sociology, psychology, and other social sciences use R for survey analysis, psychometrics, and text mining.
• Environmental Science: R is applied in environmental science for analyzing climate data, modeling ecosystems, and assessing environmental impacts.
• Pharmaceutical Industry: In the pharmaceutical industry, R is used for clinical trial data analysis, drug development, and safety monitoring.
• Academic Research: R is a popular tool in academia for conducting research across various disciplines, providing tools for data analysis, visualization, and reproducibility.

Syntax of R

To output text in R, use single or double quotes: [To write R Code, most used Code editor is R-Studio ] Example : INPUT: > print("Hello, World!") OUTPUT: [1] "Hello, World!"

Built-in Functions in R

• print() - Displays an R object on the R console
• min() / max() - Calculates the minimum and maximum of a numeric vector
• sum() - Calculates the sum of a numeric vector
• mean() - Calculates the mean of a numeric vector
• range() - Calculates the minimum and maximum values of a numeric vector
• str() - Displays the structure of an R object
• ncol() - Returns the number of columns of a matrix or a data frame
• length() - Returns the number of items in an R object, such as a vector, a list, and a matrix.
• plot() - Visualize data in graph & chart format to share insight [6]

Examples:

> v <- c(1, 3, 0.2, 1.5, 1.7)

> print(v)

[1] 1.0 3.0 0.2 1.5 1.7

> sum(v)

[1] 7.4

> mean(v)

[1] 1.48

> length(v)

[1] 5

R Studio Code & Output:

For the Following Photo: R Studio is used as a Code Editor. Syntax can be written in R Script File as well as in CONSOLE. Some output come into CONSOLE part. Again graphical output like: Plot, graph, chart result may appears on PLOT part.

R Packages:

The tidyverse is a collection of open source packages for the R programming language. The core tidyverse packages, which provide functionality to model, transform, and visualize data, include: [5]. To use each package, programmer have to install and run code.

Example: > install.packages("ggplot2")

1. ggplot2 - ggplot2 is a system for declaratively creating graphics, based on The Grammar of Graphics
2. dplyr - dplyr provides a grammar of data manipulation, providing a consistent set of verbs that solve the most common data manipulation challenges
3. tidyr - tidyr provides a set of functions that help you get to tidy data
4. readr - readr provides a fast and friendly way to read rectangular data (like csv, tsv, and fwf)
5. purrr - purrr enhances R’s functional programming (FP) toolkit by providing a complete and consistent set of tools for working with functions and vectors
6. tibble - tibble is a modern re-imagining of the data frame, keeping what time has proven to be effective, and throwing out what it has not
7. stringr - stringr provides a cohesive set of functions designed to make working with strings as easy as possible
8. forcats - forcats provides a suite of useful tools that solve common problems with factors

Conclusion:

Generally, one can use Excel for Data Cleaning, Mining and Data Analysis for Business Decision making. Besides excel, there are some important tools for data analytics: like: SQL, Tableau, Power BI, Python and others tools. But R Programming is a comprehension tool for analyze & visualize data. R can solve different tools task by itself. R has it own syntax format like C or Python language. R programming is applicable in Statistical Analytics, Business Research, Social Science, Bioinformatics, Business & Finance and others important area.

References:

[1] R Introduction, W3 Schools: https://www.w3schools.com/R/r_intro.asp

[2] What is R, R-project.org : https://www.r-project.org/about.html

[3] What is R, R-project.org : https://www.r-project.org/about.html

[4] R Introduction, W3 Schools: https://www.w3schools.com/R/r_intro.asp

[5] Tidyverse: https://www.tidyverse.org/packages/

[6] datacamp, Using Functions in R Tutorial: https://www.datacamp.com/tutorial/functions-in-r-a-tutorial

[7] Data Scientest , S Language : https://datascientest.com/en/s-language-everything-you-need-to-know-about-this-language