- The R programming language: what is it?
- History of R
- What is R programming useful?
- R Programming Features
- R programming applications
- Types of R language
- R- Software companies
- Popular R Libraries
- Advantages of R
- R programming salary trends
- Conclusion
R is one of the most commonly used programming languages in records mining. As of March 2022, R ranks eleventh in the TIOBE index, a measure of programming language popularity, having peaked in eighth place in August 2020. In this R tutorial, we can begin by studying what R is.
The R programming language: what is it?
R is a programming language and ecosystem designed for statistical analysis and visualization. It offers extensive statistical and graphical methods, including linear and nonlinear regression, traditional statistical tests, time-series data analysis, categorization, and grouping. Additionally, R is highly flexible and can be easily extended, similar to how Julia enhances capabilities in data analysis and visualization.
History of R
In many aspects, the history of R development sprang from the need for an open alternative to the S programming language in which statistics users often write their code. The released software was open-source, further propelling its growth and development processes and obtaining enhancements from the community. This stimulus eventually led to the formation of the Comprehensive R Archive Network (CRAN) to manage the packages of R, allowing people to interact and work on making lots of extensions. Over the years, R has evolved into one of the packages with the largest number of contributors offering packages and an abundance of resources to make it an all-powerful tool for everyone.
Eager to obtain your R Programming Professional Certification? Check out the R Programming Certification Course now offered at ACTE!
What is R programming useful?
R can be seen as an alternative version of S, with several key distinctions. However, when implemented in R, a large chunk of the code written for S stays the same. Regarding statistical methods research, the S language is usually the choice, although R programming provides an Open-Source avenue for participation. One significant advantage of employing R software is that it makes it easy to create high-quality graphs for publications, with the ability to include equations and mathematical symbols as needed. The user retains full control even while the default settings for small graphic design decisions have been carefully considered.
R Programming Applications
R is one of the most up-to-date and maximum superior equipment available, with a vast spectrum of uses. Its packages expand some distance past an unmarried industry, acting in regions including banking, e-trade, finance, and more. Among the famous uses of R Programming are:
Finance
R is the go-to device for statistics technology in the monetary area. It provides a complicated suite of statistical equipment capable of handling all of the vital monetary operations. R programming has been very useful in finance. Many applications have helped in data analysis and decision-making. Analysts employ R to manage risks and assess and model financial risks in evaluating exposure and volatility. The language is also critical in quantitative analysis, as it enables the development of statistical models to determine asset performance and market trends.
Banking
It is widely utilized in the banking sector for credit risk assessment and other risk evaluation techniques. Financial institutions leverage the statistical capabilities in R to analyze data on individual borrowers, aiding in the creation of accurate credit scoring models. Techniques such as logistic regression and decision tree predictions help banks make informed lending decisions to minimize the risk of default. Additionally, R supports stress testing and scenario analysis, enabling banks to evaluate potential risks under changing economic conditions, a critical aspect of Data Science in finance.
R Programming Features
- Ease and Efficiency: R is a straightforward and powerful programming language, thoroughly developed for analyzing data and performing statistical tasks.
- Data Analysis Tools: R is mainly utilized as a data analysis tool, making it perfect for executing statistical tasks and manipulating data.
- Intuitive and Robust: R is intuitive, easy to grasp, and reliable. It includes support for creating user-defined functions, loops, conditionals, and input/output operations.
- Complete Package of Tools: It comes with a comprehensive package of tools for data analysis, streamlining the analysis workflow and increasing efficiency.
- Operators for Arrays, Lists, and Vectors: R features a collection of operators tailored for various calculations on arrays, lists, and vectors.
- Efficient Data Management and Storage: R provides effective data management and storage solutions, crucial for handling large datasets.
- Open Source and Modular: R is open-source, offering it for free and allowing for extensive customization through packages and user-defined functions.
- Graphical Features: R offers advanced graphical capabilities for creating detailed and flexible visualizations.
- Vectorized Calculations: R enables executing multiple calculations on vectors at once, boosting computational speed.
- Interpreted Nature: R is an interpreted language, executing code line by line, which facilitates rapid feedback during the development phase.
Healthcare
In the healthcare field, R programming is crucial for computing and extracting colossal statistical data in healthcare services, forming an essential base for research and evaluation of statistics. Its use cuts across all fields, such as genetics, where it is applied to analyze genomic data that can identify genetic markers that may be associated with various diseases. In bioinformatics, R is used to visualize and interpret complex biological data and assist in understanding molecular interactions. R is also used in drug discovery, where it can be used to analyze clinical trial data to decide if drugs are effective or safe for administration
Achieve your R Programming Certification by learning from industry-leading experts and advancing your career with ACTE’s R Programming Certification Course.
E-trade
R programming has become an invaluable tool in the e-trade business, capable of processing nearly all types of statistical data, whether structured or unstructured. It can source data from large spreadsheets, SQL, and NoSQL systems. Since most e-trade businesses handle vast amounts of data from these sources, powerful analytical tools are essential for processing and visualizing the information. R offers extensive libraries that facilitate the analysis of customer behavior, market trends, and transaction patterns. With its robust capabilities in predictive models and time series analyses, R enables e-trade companies to make informed decisions based on real-time data, integrating effectively with Artificial Intelligence to enhance their analytics.
The ‘R’ programming is very important for statistical exploration in the world of social media, especially in the realms of sentiment analysis and data mining. An enormous amount of unstructured data is created daily on platforms like Twitter, Facebook, and Instagram; R helps analysts make sense of the information. One can leverage the natural language processing libraries of R to assess the general sentiment of the users, where the posts may be rated as positive, negative, or neutral. Then, brand marketers can utilize such perception to make marketing decisions.
Types of R language
Vectors: A vector is a one-dimensional array containing elements of the same type. It can be a numeric, logical, or character vector.
- Example : Some examples of numeric and character vectors.c(1, 2, 3),c(“a”, “b”, “c”)
- Example: my_list <- list(name="John", age=30, scores=c(90, 85, 88)) is an example that contains mixed data types.
- Example: matrix(1:6, nrow=2, ncol=3) creates a 2×3 matrix.
- Example: Produce a data frame of mixed types: data.frame(name=c(“Alice”, “Bob”), age=c(25, 30), height=c(5.5, 6.0))
- Example: factor(c(“low”, “medium”, “high”)) yields a factor variable of three levels.
- Example: array(1:12, dim=c(2,3,2)) generates a 3D array with 2x3x2 dimensions.
- Example: x <- NULL assigns a null value to the variable x.
- Example: nested_list <- list(list1=c(1, 2), list2=list(a="A", b="B")).
- Google: R is a famous language amongst Google, applied in its project, Google Flu Trends, to forecast flu levels. Additionally, Google employs R Programming to decorate the performance of its marketing and marketing strategies.
- Facebook: If you are a Facebook user, you probably update your reputation and import profile photos. Facebook accomplishes this through R programming.
- Airbnb: Airbnb has advanced an R package deal named `Rbnb`, specially designed for visualizing statistics on large scales.
- The extensibility of R largely depends on an enormous number of packages. Here are some of the most commonly used ones. Package The package is mainly used for data visualizations with the Grammar of Graphics, where a complex graph can be produced using an intuitive syntax and extreme customizability.
- The package presents different data-manipulating functions. It makes tasks like filtering, summarization, and grouping data easier, thus making data cleaning and preparation smoother.
- This is an extension of dplyr. But while dplyr focuses more on cleaning and other forms of data preparation, tidy centres on data tidying. It contains the functionality of reshaping and organizing datasets that form the backbone of how one ought to do effective analysis.
- A complete kit for building Machine Learning K Means Clustering enables fast and easy development of predictive models with tools such as data splitting, pre-processing, feature selection, and model tuning.
- Shiny is a framework that lets any R user build interactive web applications directly from R. It also allows the sharing of easily deployed dashboards and visualizations.
- Free Open Source:It is free and can be used and distributed by anyone without charge. This encourages a collaborative environment where users and contributors can contribute to the development of R.
- Rich Ecosystem: Due to the very high number of packages R has hosted on CRAN, users can usually find tools for almost any type of analysis or visualization, thereby greatly enriching functionality.
- Strong community: Such a responsive R community usually provides immense resources, from forums to online courses, to support novice and advanced users on their path toward learning.
- Statistical Emphasis:R is specifically designed for statistical computations, so its functionality is often very strong and more advanced than other programming languages.
- Superb Graphics Display Ability: Unlike R packages, no statistical language offers graphics display capabilities. The graphics are so excellent that data insights can be communicated easily to others.
- Statistical Analyst
- Data Analyst
- Data Scientist
- Machine Learning Engineer
Lists:A list is a collection of elements with dissimilar types, including vectors, data frames, and other lists.
Matrices:A matrix is a two-dimensional array in which all its elements share the same kind. Rows and columns characterize it.
Data Frames: Data frame A table-like structure with columns of various modes, just like a spreadsheet. Each column can be of a different mode.
Factors:Factors are a special vector mode used for categorical data. They also store the values along with their respective levels.
Arrays: An array is a multi-dimensional generalization of a matrix. It can hold elements of the same type and have more than two dimensions.
Null:The null type represents no or undefined value. It is helpful for indicating missing data or undefined data.
Lists of Lists: Lists can contain other lists, thus enabling complex hierarchical data structures.
Looking to Master Data Science? Discover the Data Science Master ProgramAvailable at ACTE Today!
R- Software Companies
Some popular R libraries
Advantages of R
Getting Ready for an R Programming Job Interview? Browse Our Complete Set of R Programming Interview Questions to Aid Your Preparation!
R Programming Salary Trends
Based onon the O’Reilly Data Scientist Survey findings, the common profits for people professionals in R Programming stand at approximately $115,000. Possessing R capabilities opens up numerous employment opportunities, together with:
Therefore, the potential for a high income and an enticing professional route must function as robust incentives to learn R Programming. As we progress through the following sections of this R Programming Tutorial, you’ll be introduced to the diverse sides of R Programming.
Conclusion
Today, R is a critical tool used by scholars, statistical analysts, statisticians, and marketers for extracting, cleaning, analyzing, visualizing, and presenting data. Focusing on R programming will provide significant advantages for your future as the fields of statistical technology and big data expand. Acquiring R programming skills will not only equip you with essential abilities for a career in statistical technology but may also open doors to a job market expected to experience substantial growth. Additionally, integrating R with tools like Spark SQL can enhance your data analysis capabilities. Therefore, let’s begin our journey into learning R programming.