have sometimes been added to briefly describe other avenues than the main ones,
without going into details.
1.2 Why R?
The R language has experienced such a tremendous development and reached such a
wide array of users during the recent years that a justification of its application to
numerical ecology is not required. Development also means that more and more
domains of numerical ecology are now covered, up to the point where, computationally speaking, some of the most recent methods are actually only available
through R packages.
This book is not intended as a primer in R, however. To find that kind of support,
readers should consult the CRAN web page (http://www.R-project.org). The link to
Manuals provides many free electronic documents, and the link to Books many
references. Readers are expected to have a minimal working knowledge of the basics
of the language, e.g. formatting data and importing them into R, awareness of the
main classes of objects handled in this environment (vectors, matrices, data frames
and factors), as well as the basic syntax necessary to manipulate, create and
otherwise use objects within R. Nevertheless, Chap. 2 starts at an elementary level
as far as multivariate objects are concerned, since these are the main targets of most
analyses addressed throughout the book, while not necessarily being most familiar to
many users.
The book is by far not exhaustive as to the array of functions devoted to any of the
methods. Usually we present one or several variants, but often other functions
serving similar purposes are available in R. Centring the book on a small number
of well-integrated packages and adding some functions of our own when necessary
helps users up the learning curve while keeping the amount of package-level
idiosyncrasies at a reasonable level. Our choices should not suggest that other
existing packages are inferior to the ones used in the book.
1.3 Readership and Structure of the Book
The intended audience of this book is the researchers, practitioners, graduate students and teachers who already have a background in general and multivariate
statistics and wish to apply their knowledge to their data using the R language, as
well as people willing to accompany their learning of the discipline with practical
applications. Although an important part of this book follows the organization and
symbolism of Legendre and Legendre (2012) and many references to that book are
made herein, readers may draw their training from other sources without problem.
Combining an application-oriented book such as this one with a detailed exposé
of the methods used in numerical ecology would have led to an impossibly long and
2
1 Introduction
without going into details.
1.2 Why R?
The R language has experienced such a tremendous development and reached such a
wide array of users during the recent years that a justification of its application to
numerical ecology is not required. Development also means that more and more
domains of numerical ecology are now covered, up to the point where, computationally speaking, some of the most recent methods are actually only available
through R packages.
This book is not intended as a primer in R, however. To find that kind of support,
readers should consult the CRAN web page (http://www.R-project.org). The link to
Manuals provides many free electronic documents, and the link to Books many
references. Readers are expected to have a minimal working knowledge of the basics
of the language, e.g. formatting data and importing them into R, awareness of the
main classes of objects handled in this environment (vectors, matrices, data frames
and factors), as well as the basic syntax necessary to manipulate, create and
otherwise use objects within R. Nevertheless, Chap. 2 starts at an elementary level
as far as multivariate objects are concerned, since these are the main targets of most
analyses addressed throughout the book, while not necessarily being most familiar to
many users.
The book is by far not exhaustive as to the array of functions devoted to any of the
methods. Usually we present one or several variants, but often other functions
serving similar purposes are available in R. Centring the book on a small number
of well-integrated packages and adding some functions of our own when necessary
helps users up the learning curve while keeping the amount of package-level
idiosyncrasies at a reasonable level. Our choices should not suggest that other
existing packages are inferior to the ones used in the book.
1.3 Readership and Structure of the Book
The intended audience of this book is the researchers, practitioners, graduate students and teachers who already have a background in general and multivariate
statistics and wish to apply their knowledge to their data using the R language, as
well as people willing to accompany their learning of the discipline with practical
applications. Although an important part of this book follows the organization and
symbolism of Legendre and Legendre (2012) and many references to that book are
made herein, readers may draw their training from other sources without problem.
Combining an application-oriented book such as this one with a detailed exposé
of the methods used in numerical ecology would have led to an impossibly long and
2
1 Introduction