This PDF covers Introduction and preliminaries, Simple manipulations : numbers and vectors, Objects, Ordered and unordered factors, Arrays and matrices, Lists and data frame, Reading data from files, Probability distribution, Grouping, Statistical models in R, Graphical procedures, Packages, OS Facilities, Invoking R, The command-line editor, function and variable index.
Author(s): W.N.Venables and D.M.Smith
103Pages
This book covers the history and fundamentals of R, guiding readers through installation and usage of R, R Studio, and R Shiny. Explore data manipulation, interfaces, and visualization, while mastering statistical modeling and machine learning. Real-world case studies offer practical insights into hypothesis generation, data exploration, and model building. This guide is your essential companion for mastering statistical computing and data science using R.
Author(s): Sathyabama Institute of Science and Technolog
151Pages
This PDF covers the following topics related to R Programming : Introduction, Basic Arithmetic and Objects , Data, Functions, Graphics, The apply Family of Functions, Arrays and Tables, Strings, Classes and Methods, Debugging, Arithmetic Subtleties, List of the Most Useful Functions in R.
Author(s): Robin Evans
82Pages
R is the open-source statistical language that seems poised to take over the world of statistics and data science. R is really more than a statistical package - it is a language or an environment designed to produce statistical analysis and production of high quality graphics. This page covers the following topics related to R Programming : Introduction, RStudio, Basic R, Data I/O, Subsetting Data in R, Data Summarization, Data Classes, Data Cleaning, Manipulating Data in R, Data Visualization, Statistical Analysis in R, Functions, Simulations, RMarkdown, Shiny, Solutions to Exercises.
Author(s): Dr Robert Parker
NAPages
