Titlebook: R Programming; Statistical Data Ana Kingsley Okoye,Samira Hosseini Book 2024 The Editor(s) (if applicable) and The Author(s), under exclusi

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Working with Data in R: Objects, Vectors, Factors, Packages and Libraries, and Data Visualizationhe users can plot or visualize data (graphs) in R. R has many built-in functions and packages that allows the user to perform different types of data analysis, and is also . due to fact that it allows the users to define their own additional functions or methods.

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alysis using R-OOP language and RStudio integrated developme.This book is written for statisticians, data analysts, programmers, researchers, professionals, and general consumers on how to perform different types of statistical data analysis for research purposes using R object-oriented programming

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Introduction to R Programming and RStudio Integrated Development Environment (IDE)isplays, modeling, and calculations particularly for research purposes. It covers the basic concept of R programming, and how the readers can install and run their first project using R. R is built on the well-developed, simple, and effective programming language called “S and S-Plus” that supports

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Test of Normality and Reliability of Data in Ried methods for research purposes and investigation are explained and illustrated in detail in this chapter. The . . are performed to assess if a data sample is well modeled by a normal distribution, while the . of the datasets or research instruments is done to determine the extent (consistency of

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Choosing Between Parametric and Non-parametric Tests in Statistical Data Analysisdes a guide for the readers on how to choose which of the test is most suitable for their specific research, including a description of the differences, advantages, and disadvantages of using the two types of tests. Before using the different statistical methods in R (see Chap. .), the users need to

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Understanding Dependent and Independent Variables in Research Experiments and Hypothesis Testingt conditions to the use of the two types of variables (dependent and independent) in scientific research and hypothesis testing. The differences between the two variables and examples of each use case scenario are provided in this chapter. The relationship between the . (IV) and . (DV) variables is