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.
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
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.