Comparative Approaches to Using R and Python for Statistical Data Analysis

216 Pages·2016·5.154 MB·English

by  Rui Sarmento, Vera Costa

Comparative Approaches
to Using R and Python for
Statistical Data Analysis
Rui Sarmento
University of Porto, Portugal
Vera Costa
University of Porto, Portugal
A volume in the Advances in
Systems Analysis, Software
Engineering, and High
Performance Computing
(ASASEHPC) Book Series
Published in the United States of America by
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Library of Congress Cataloging-in-Publication Data
Names: Sarmento, Rui, 1979- | Costa, Vera, 1983-
Title: Comparative approaches to using R and Python for statistical data
analysis / by Rui Sarmento and Vera Costa.
Description: Hershey PA : Information Science Reference, [2017] | Includes
bibliographical references and index.
Identifiers: LCCN 2016050989| ISBN 9781683180166 (hardcover) | ISBN
9781522519898 (ebook)
Subjects: LCSH: Mathematical statistics--Data processing. | R (Computer
program language) | Python (Computer program language)
Classification: LCC QA276.45.R3 S27 2017 | DDC 519.50285/5133--dc23 LC record available at
https://lccn.loc.gov/2016050989

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3461)
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Advances in Systems
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(ASASEHPC) Book Series
ISSN:2327-3453
EISSN:2327-3461
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To our parents and family…
Table of Contents
Preface.................................................................................................................viii
; ;
Introduction...........................................................................................................x
; ;
Chapter 1
;
Statistics..................................................................................................................1
; ;
Chapter 2
;
Introduction.to.Programming.R.and.Python.Languages.......................................32
; ;
Chapter 3
;
Dataset..................................................................................................................78
; ;
Chapter 4
;
Descriptive.Analysis.............................................................................................83
; ;
Chapter 5
;
Statistical.Inference.............................................................................................114
; ;
Chapter 6
;
Introduction.to.Linear.Regression......................................................................140
; ;
Chapter 7
;
Factor.Analysis...................................................................................................148
; ;
Chapter 8
;
Clusters...............................................................................................................179
; ;
Chapter 9
;
Discussion.and.Conclusion.................................................................................191
; ;
About the Authors.............................................................................................195
; ;
Index...................................................................................................................196
; ;
viii
Preface
We may at once admit that any inference from the particular to the general
must be attended with some degree of uncertainty, but this is not the same
as to admit that such inference cannot be absolutely rigorous, for the nature
and degree of the uncertainty may itself be capable of rigorous expression.
– Sir Ronald Fisher
The importance of Statistics in our world is increasing greatly in recent decades.
Due do the need to provide inference from data samples; statistics is one of
the greatest achievements of humanity. Its use has spread to a large range of
research areas, not only limited to research done by mathematicians or pure
statistics professionals. Nowadays, it is standard procedure to include some
statistical analysis when the scientific study involves data. There is a high
influence and demand for statistical analysis in today’s Medicine, Biology,
Psychology, Physics and many other areas.
The demand for statistical analysis of data has proliferated so much; it has
survived inclusively to attacks from the mathematical challenged.
If the statistics are boring, then you’ve got the wrong numbers. – Edward
R. Tufte
Thus, with the advent of computers and advanced computer software, the
intuitiveness of analysis software has evolved greatly in recent years and they
have opened to a wider audience of users. It is common to see another kind
of statistical researchers in modern academies. Those with no advanced stud-
ies in the mathematical areas are the new statisticians and use and produce
statistical studies with scarce or no help from others.
Above all else show the data. – Edward R. Tufte
ix
The need to expose the studies in a clear fashion for a non-specialized
audience has brought the development of, not only intuitive software but
software directed to the visualization of data and data analysis. For example,
the psychologist with no mathematical foundations can now choose from
several languages and software to add value to their studies by performing
throughout analysis of their data and present it in an understandable fashion.
This book presents a comparison of two of the available languages to
execute data analysis and statistical analysis, R language and also the Python
language. It is directed to anyone, experienced or not, that might need to
analyze his/her data in an understandable way. For those more experienced,
the authors of this book approach the theoretical fundamentals of statistics,
and for a larger range of audience, explain the programming fundamentals,
both with R and Python languages.
The statistical tasks range from Descriptive Analytics. The authors describe
the need for basic statistical metrics and present the main procedures with
both languages. Then, Inferential Statistics are presented in this book. High
importance is given to the most needed statistical tests to perform a coherent
data analysis. Following Inferential Statistics, the authors also provide ex-
amples, with both languages, in a throughout explanation of Factor Analysis.
The authors emphasize the importance of variable study and not only the
objects study. Nonetheless, the authors present a chapter also dedicated to
the clustering analysis of studied objects. Finally, an introductory study of
regression models and linear regression is also tabled in this book.
The authors do not deny that the structure of the book might pose some
comparison questions since the book deals with two different programming
languages. The authors end the book with a discussion that provides some
clarification on this subject but, above all, also provides some insights for
further consideration.
Finally, the authors would like to thank all the colleagues that provided
suggestions and reviewed the manuscript in all its development phases, and
all the friends and family members for their support.