Practicing R for statistical computing

By:

Aslam, Muhammad

Contributor(s):

Imdad Ullah, Muhammad

Material type: Text

TextPublication details: Springer Singapore 2023Description: xvii, 292 pISBN:

9789819928880

Subject(s):

Statistical computing

DDC classification:

005.133 ASL

Summary: This book is designed to provide a comprehensive introduction to R programming for data analysis, manipulation and presentation. It covers fundamental data structures such as vectors, matrices, arrays and lists, along with techniques for exploratory data analysis, data transformation and manipulation. The book explains basic statistical concepts and demonstrates their implementation using R, including descriptive statistics, graphical representation of data, probability, popular probability distributions and hypothesis testing. It also explores linear and non-linear modeling, model selection and diagnostic tools in R. The book also covers flow control and conditional calculations by using ‘‘if’’ conditions and loops and discusses useful functions and resources for further learning. It provides an extensive list of functions grouped according to statistics classification, which can be helpful for both statisticians and R programmers. The use of different graphic devices, high-level and low-level graphical functions and adjustment of parameters are also explained. Throughout the book, R commands, functions and objects are printed in a different font for easy identification. Common errors, warnings and mistakes in R are also discussed and classified with explanations on how to prevent them. (https://link.springer.com/book/10.1007/978-981-99-2886-6)

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Holdings
Item type	Current library	Collection	Call number	Copy number	Status	Date due	Barcode
Book	Indian Institute of Management LRC General Stacks	IT & Decisions Sciences	005.133 ASL (Browse shelf(Opens below))	1	Available		006613

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Previous	005.13 SEB Concepts of programming languages	005.133 ACH Data analytics using R	005.133 ACH Reimagining data visualization using python	005.133 ASL Practicing R for statistical computing	005.133 BEN Applied text analysis with Python: enabling language-aware data products with machine learning	005.133 BRO Business case analysis with R: simulation tutorials to support complex business decisions	005.133 BRU Quantitative portfolio management: with applications in Python	Next

Table of content:
Front Matter
Pages i-xvii
Download chapter PDF
R Language: Introduction
Muhammad Aslam, Muhammad Imdad Ullah
Pages 1-6
Obtaining and Installing R Language
Muhammad Aslam, Muhammad Imdad Ullah
Pages 7-17
Using R as a Calculator
Muhammad Aslam, Muhammad Imdad Ullah
Pages 19-25
Data Mode and Data Structure
Muhammad Aslam, Muhammad Imdad Ullah
Pages 27-43
Working with Data
Muhammad Aslam, Muhammad Imdad Ullah
Pages 45-82
Descriptive Statistics
Muhammad Aslam, Muhammad Imdad Ullah
Pages 83-100
Probability and Probability Distributions
Muhammad Aslam, Muhammad Imdad Ullah
Pages 101-148
Confidence Intervals and Comparison Tests
Muhammad Aslam, Muhammad Imdad Ullah
Pages 149-172
Correlation and Regression Analysis
Muhammad Aslam, Muhammad Imdad Ullah
Pages 173-187
Graphing in R
Muhammad Aslam, Muhammad Imdad Ullah
Pages 189-223
Control Flow: Selection and Iteration
Muhammad Aslam, Muhammad Imdad Ullah
Pages 225-241
Functions and R Resources
Muhammad Aslam, Muhammad Imdad Ullah
Pages 243-250
Common Errors and Mistakes
Muhammad Aslam, Muhammad Imdad Ullah
Pages 251-261
Functions for Better Programming
Muhammad Aslam, Muhammad Imdad Ullah
Pages 263-272
Some Useful Functions
Muhammad Aslam, Muhammad Imdad Ullah
Pages 273-287
Important Packages
Muhammad Aslam, Muhammad Imdad Ullah
Pages 289-292

[https://link.springer.com/book/10.1007/978-981-99-2886-6)

This book is designed to provide a comprehensive introduction to R programming for data analysis, manipulation and presentation. It covers fundamental data structures such as vectors, matrices, arrays and lists, along with techniques for exploratory data analysis, data transformation and manipulation. The book explains basic statistical concepts and demonstrates their implementation using R, including descriptive statistics, graphical representation of data, probability, popular probability distributions and hypothesis testing. It also explores linear and non-linear modeling, model selection and diagnostic tools in R.

The book also covers flow control and conditional calculations by using ‘‘if’’ conditions and loops and discusses useful functions and resources for further learning. It provides an extensive list of functions grouped according to statistics classification, which can be helpful for both statisticians and R programmers. The use of different graphic devices, high-level and low-level graphical functions and adjustment of parameters are also explained. Throughout the book, R commands, functions and objects are printed in a different font for easy identification. Common errors, warnings and mistakes in R are also discussed and classified with explanations on how to prevent them.
(https://link.springer.com/book/10.1007/978-981-99-2886-6)

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