Coordinator and Convener
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About the Group
The name, Osun R Users’ Group – Osun RUG, came into existence when the idea to spread the gospel about R language was conceived by Timothy A. OGUNLEYE. This was intended to promote the language among the people living around his jurisdiction and beyond. This idea was shared with a number of team members, who equally have the same vision and mission.
Osun RUG is a community of both learners and instructors of R language. The primary aim of its establishment is to practically equip those who are keenly interested to learn the grammar of R within and outside our locality. We group ourselves with a view to helping one another in providing solutions to any challenges that are practically related to data science and computational statistics.
Thus,this is a community of R users who have the passion for distributing R software. Our services are free of charge, as we are committed to serving humanity within the purview of our capability. We preach the gospel about R language even beyond our community. We do all these as parts of the services toour immediate community and the entire nation at large.
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About R Programming Language
R is an open-source programming language that facilitates statistical computing and graphical libraries. Being open-source, R enjoys community support of avid developers who work on releasing new packages, updating R and making it a steadfast programming package for Data Science. R programming language is one that allows statistical computing that is used widely by the data miners and statisticians for data analysis. It was developed in 1995 by Ross Ihaka and Robert Gentleman, where the name ‘R’ was derived from the first letters of their names. R is a popular choice in data analytics and data science for statistical computing and graphical techniques. With the help of R, one can perform various statistical operations. We can obtain it for free from the website www.r-project.org. It is driven by command lines. Each command is executed when the user enters them into the prompt. Since R is open-source, most of its routines and procedures have been developed by programmers all over the world. All the packages are available for free at the R project website called CRAN. This contains over 10,000 packages in R. The basic installation comprises a set of tools that various data scientists and statisticians use for multiple tasks. In R, there is a comprehensive environment that facilitates the performance of statistical operations as well as the generation of data analysis in graphical or text format. The commands that a console takes in as input are assessed and subsequently executed. R is incapable of handling auto-formatting characters such as dashes or quotes, hence, you need to be discreet while copy-pasting commands from external sources into your R environment. R was conceived at the Bell Laboratories by John Chambers in 1976. R was developed as an extension as well as an implementation of S programming language. In 1992, the R project was developed by Ross Ihaka and Robert Gentleman of the Department of Statistics, University of Auckland (New Zealand) and, its first version was released in 1995 while a stable (beta) version was released in the year 2000.
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Coordinator and Convener
Timothy, a statistician by training, is a product of the prestigious University of Ilorin, Nigeria, specializing in Quantitative Modelling, Econometrics, Psychometrics and Morphometrics with much interest in Biostatistics, Designs of Experiment, and Applied Statistics. He is well-grounded in Statistics and Mathematics. Timothy is an R language expert with a sound knowledge of SQL and Python programming languages. He was, however, trained by the Department of Computing of Macquarie University, the City of Sydney, Australia, where he has been officially certified as R and Python Programmer in the field of Data Science. In the years back, he started his higher educational pursuits from the Federal Polytechnic, Ede, Nigeria, where he efficiently earned both National Diploma (ND) and Higher National Diploma (HND) certificates in Statistics. In addition, he is also the Secretary-General, International Association for Statistical Computing, African Members Group (IASC-AMG). Undoubtedly, He has at least 15 years of series of rich experiences that cut across both industries and academia. He has published quite a number of academic papers both locally and internationally. He is a monitoring and evaluation (M&E) expert and a tutor per excellence!. Timothy is the coordinator, Osun R Users Group (OSUN RUG), Nigeria and co-founder, Tim-R Programming Consult, Nigeria – both are non-profit, non-governmental organizations. He learns very fast and assimilates quickly. Presently, he is a lecturer at the Department of Statistics, Faculty of Basic and Applied Sciences, College of Science, Engineering and Technology (SET), Osun State University, Osogbo, Nigeria. He could be reached through any of his email addresses: osunrug@rug.org.ng ; thompsondx@gmail.com and timothy.ogunleye@uniosun.edu.ng
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Pros and Cons of R Programming Language
R is one of the most popular languages for statistical modeling and analysis. But like every other programming language, R has its own set of benefits and limitations. In this context, we will discuss the weighing of the pros and cons of R programming against each other. R is a continuously evolving language. This means that many of the cons (disadvantages) will gradually fade away with the future updates of R.
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Some Basic Rules in R
The rules guiding order of precedence during arithmetic operations in R are as follows:
BFEMDAS, meaning that:
⭐B for Bracket;
⭐F for Functions
⭐E for Exponents
⭐M for Multiplication
⭐D for Division
⭐A for Addition
⭐ S for Subtraction
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Advantages of R Programming Language
R success revolves around several advantages it provides for beginners and experts alike. Here are the powerful advantages of R programming:
⭐Excellence for Statistical Computing and Analysis: R is a statistical language created by statisticians. Thus, it excels in statistical computation.
⭐R is the most used programming language for developing statistical tools.
⭐Open-source: R is an open-source programming language. Anyone can work with R without any license or fee. Due to this, R has a huge community that contributes to its environment.
⭐A Large Variety of Libraries: R’s massive community support has resulted in a very large collection of libraries. R is famous for its graphical libraries These libraries support and enhance the R development environment. R has libraries with a huge variety of applications.
⭐Cross-platform Support: R is machine-independent. It supports the cross-platform operation. Thus, it is usable on many different operating systems.
⭐Supports various Data Types: R can perform operations on vectors, arrays, matrices, and various other data objects of varying sizes.
⭐Can do Data Cleansing, Data Wrangling, and Web Scraping: R can collect data from the internet through web scraping and other means. It can also perform data cleansing. Data cleansing is the process of detecting and removing/correcting inaccurate or corrupt records. R is also useful for data wrangling which is the process of converting raw data into the desired format for easier consumption.
⭐Powerful Graphics: R has extensive libraries that can produce production quality graphs and visualizations. These graphics can be of static as well as dynamic nature.
⭐Highly Active Community: The R community is very active. There are users from all around the world to help and support users of R dialect. Many latest ideas and technology appear in the R community.
⭐Parallel and Distributed Computing: Using libraries like ddR or multiDplyr, R can process large data sets using parallel or distributed computing.
⭐Doesn’t need a Compiler: R is an interpreted language. This means that it does not need a compiler to turn the code into an executable program. Instead, R interprets the provided code into lower-level calls and pre-compiled code.
Compatible with other Programming Languages: R is compatible with other languages like C, C++, and FORTRAN. Other languages like .NET, Java, Python can also directly manipulate objects.
⭐Used in Machine learning: R can be useful for machine learning as well. Facebook does a lot of its machine learning research with R. Sentiment analysis and mood prediction are all done using R. The best use of R when it comes to machine learning is in case of exploration or when building one-off models.
⭐Interaction with Databases: R contains several packages that enable it to interact with databases. Some of these packages are Roracle, Open Database Connectivity Protocol), RmySQL, etc.
⭐Comprehensive Environment: R has a very comprehensive development environment. It helps in statistical computing as well as software development. R is an object-oriented programming language. It also has a robust package called Rshiny which can produce full-fledged web apps. R can also be useful for developing software packages.
⭐EYE-CATCHING REPORTS: With packages like Shiny and Markdown, reporting the results of an analysis is extremely easy with R. You can make reports with the data, plots and R scripts embedded in them. You can even make interactive web apps that allow the user to play with the results and the data.
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Disadvantages of R Programming Language
It’s no secret that there’s also a dark side to R programming. Let’s move on to the disadvantages of using R:
⭐Steep Learning Curve: As many have said, R makes easy things hard, and hard things easy. R’s syntax is very different from other languages so are its data types. The learning curve for R is pretty steep for a beginner. Though R is a bit difficult in the beginning, data science enthusiasts still prefer to learn it due to the amazing features of R.
⭐Some Packages may be of Poor Quality: CRAN houses more than 10,000 libraries and packages. Some of them are redundant as well. Due to the large quality, some of the packages may be of poor quality.
⭐Poor Memory Management: R commands don’t concern with memory management. As a result, R can take up all the available space.
⭐Slow Speed: The programs and functions in R are spread across different packages. This makes it slower than alternatives such as MATLAB and Python.
⭐Poor Security: R lacks basic security measures. So making web-apps with it is not always safe.
⭐No Dedicated Support Team: R has no dedicated support team to help a user with their issues and problems. But the community is quite large, so everybody helps each other out.
⭐Flexible Syntax: R is a flexible programming language and there are no strict guidelines to follow. You need to maintain proper coding standards to avoid messy and complicated code.
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Basic Arithmetic Operators in R
⭐ + stands for Plus Sign ⭐ - stands for Minus Sign ⭐ * stands for Multiplication Sign ⭐ / stands for Division Sign ⭐ ^ stands for (Raise to) Power Sign
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Some Logical and Relational Operators in R
< stands for 'less than'
> stands for 'greater than'
<= stands for 'less than or equal to'
>= stands for 'greater than or equal to'
== stands for 'equal to'
!= stands for 'not equal to'
! stands for 'Not'
| stands for 'Or'
& stands for 'And'
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Differences between R and other Statistical Software Packages
⭐ R is much more flexible than most software used by statisticians because it is modern mathematical programming language.
⭐ In R, we can develop new algorithm by writing user's defined functions. Because of its flexibility, it can be extended.
⭐ Codes written for R can be run on many computational platforms with or without a graphical user interface.
⭐ And of course, R is completely free for any use.