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Master functions and discover how to write functional programs in
R. In this book, updated for R 4, you'll learn to make your
functions pure by avoiding side effects, write functions that
manipulate other functions, and construct complex functions using
simpler functions as building blocks. In Functional Programming in
R 4, you’ll see how to replace loops, which can have
side-effects, with recursive functions that can more easily avoid
them. In addition, the book covers why you shouldn't use recursion
when loops are more efficient and how you can get the best of both
worlds. Functional programming is a style of programming, like
object-oriented programming, but one that focuses on data
transformations and calculations rather than objects and state.
Where in object-oriented programming you model your programs by
describing which states an object can be in and how methods will
reveal or modify that state, in functional programming you model
programs by describing how functions translate input data to output
data. Functions themselves are considered to be data you can
manipulate and much of the strength of functional programming comes
from manipulating functions; that is, building more complex
functions by combining simpler functions. What You'll Learn Write
functions in R 4, including infix operators and replacement
functions Create higher order functions Pass functions to
other functions and start using functions as data you can
manipulate Use Filer, Map and Reduce functions to express the
intent behind code clearly and safely Build new functions from
existing functions without necessarily writing any new functions,
using point-free programming Create functions that carry data along
with them Who This Book Is For Those with at least some experience
with programming in R.
Learn approaches of computational thinking and the art of designing
algorithms. Most of the algorithms you will see in this book are
used in almost all software that runs on your computer. Learning
how to program can be very rewarding. It is a special feeling to
seeing a computer translate your thoughts into actions and see it
solve your problems for you. To get to that point, however, you
must learn to think about computations in a new way-you must learn
computational thinking. This book begins by discussing models of
the world and how to formalize problems. This leads onto a
definition of computational thinking and putting computational
thinking in a broader context. The practical coding in the book is
carried out in Python; you'll get an introduction to Python
programming, including how to set up your development environment.
What You Will Learn Think in a computational way Acquire general
techniques for problem solving See general and concrete algorithmic
techniques Program solutions that are both computationally
efficient and maintainable Who This Book Is For Those new to
programming and computer science who are interested in learning how
to program algorithms and working with other computational aspects
of programming.
Gain a better understanding of pointers, from the basics of how
pointers function at the machine level, to using them for a variety
of common and advanced scenarios. This short contemporary guide
book on pointers in C programming provides a resource for
professionals and advanced students needing in-depth hands-on
coverage of pointer basics and advanced features. It includes the
latest versions of the C language, C20, C17, and C14. You'll see
how pointers are used to provide vital C features, such as strings,
arrays, higher-order functions and polymorphic data structures.
Along the way, you'll cover how pointers can optimize a program to
run faster or use less memory than it would otherwise. There are
plenty of code examples in the book to emulate and adapt to meet
your specific needs. What You Will Learn Work effectively with
pointers in your C programming Learn how to effectively manage
dynamic memory Program with strings and arrays Create recursive
data structures Implement function pointers Who This Book Is For
Intermediate to advanced level professional programmers, software
developers, and advanced students or researchers. Prior experience
with C programming is expected.
Implement practical data structures and algorithms for text search
and discover how it is used inside other larger applications. This
unique in-depth guide explains string algorithms using the C
programming language. String Algorithms in C teaches you the
following algorithms and how to use them: classical exact search
algorithms; tries and compact tries; suffix trees and arrays;
approximative pattern searches; and more. In this book, author
Thomas Mailund provides a library with all the algorithms and
applicable source code that you can use in your own programs. There
are implementations of all the algorithms presented in this book so
there are plenty of examples. You'll understand that string
algorithms are used in various applications such as image
processing, computer vision, text analytics processing from data
science to web applications, information retrieval from databases,
network security, and much more. What You Will Learn Use classical
exact search algorithms including naive search, borders/border
search, Knuth-Morris-Pratt, and Boyer-Moor with or without Horspool
Search in trees, use tries and compact tries, and work with the
Aho-Carasick algorithm Process suffix trees including the use and
development of McCreight's algorithm Work with suffix arrays
including binary searches; sorting naive constructions; suffix tree
construction; skew algorithms; and the Borrows-Wheeler transform
(BWT) Deal with enhanced suffix arrays including longest common
prefix (LCP) Carry out approximative pattern searches among suffix
trees and approximative BWT searches Who This Book Is For Those
with at least some prior programming experience with C or Assembly
and have at least prior experience with programming algorithms.
Get an introduction to functional data structures using R and write
more effective code and gain performance for your programs. This
book teaches you workarounds because data in functional languages
is not mutable: for example you'll learn how to change
variable-value bindings by modifying environments, which can be
exploited to emulate pointers and implement traditional data
structures. You'll also see how, by abandoning traditional data
structures, you can manipulate structures by building new versions
rather than modifying them. You'll discover how these so-called
functional data structures are different from the traditional data
structures you might know, but are worth understanding to do
serious algorithmic programming in a functional language such as R.
By the end of Functional Data Structures in R, you'll understand
the choices to make in order to most effectively work with data
structures when you cannot modify the data itself. These techniques
are especially applicable for algorithmic development important in
big data, finance, and other data science applications. What You'll
Learn Carry out algorithmic programming in R Use abstract data
structures Work with both immutable and persistent data Emulate
pointers and implement traditional data structures in R Build new
versions of traditional data structures that are known Who This
Book Is For Experienced or advanced programmers with at least a
comfort level with R. Some experience with data structures
recommended.
Learn how to write object-oriented programs in R and how to
construct classes and class hierarchies in the three
object-oriented systems available in R. This book gives an
introduction to object-oriented programming in the R programming
language and shows you how to use and apply R in an object-oriented
manner. You will then be able to use this powerful programming
style in your own statistical programming projects to write
flexible and extendable software. After reading Advanced
Object-Oriented Programming in R, you'll come away with a practical
project that you can reuse in your own analytics coding endeavors.
You'll then be able to visualize your data as objects that have
state and then manipulate those objects with polymorphic or generic
methods. Your projects will benefit from the high degree of
flexibility provided by polymorphism, where the choice of concrete
method to execute depends on the type of data being manipulated.
What You'll Learn Define and use classes and generic functions
using R Work with the R class hierarchies Benefit from
implementation reuse Handle operator overloading Apply the S4 and
R6 classes Who This Book Is For Experienced programmers and for
those with at least some prior experience with R programming
language.
In this handy, quick reference book you'll be introduced to several
R data science packages, with examples of how to use each of them.
All concepts will be covered concisely, with many illustrative
examples using the following APIs: readr, dibble, forecasts,
lubridate, stringr, tidyr, magnittr, dplyr, purrr, ggplot2, modelr,
and more. With R 4 Data Science Quick Reference, you'll have the
code, APIs, and insights to write data science-based applications
in the R programming language. You'll also be able to carry out
data analysis. All source code used in the book is freely available
on GitHub.. What You'll Learn Implement applicable R 4 programming
language specification features Import data with readr Work with
categories using forcats, time and dates with lubridate, and
strings with stringr Format data using tidyr and then transform
that data using magrittr and dplyr Write functions with R for data
science, data mining, and analytics-based applications Visualize
data with ggplot2 and fit data to models using modelr Who This Book
Is For Programmers new to R's data science, data mining, and
analytics packages. Some prior coding experience with R in general
is recommended.
Discover how to write manuscripts in Markdown and translate them
with Pandoc into different output formats. You'll use Markdown to
annotate text formatting information with a strong focus on
semantic information: you can annotate your text with information
about where chapters and sections start, but not how chapter and
heading captions should be formatted. As a result, you'll decouple
the structure of a text from how it is visualized and make it
easier for you to produce different kinds of output. The same text
can easily be formatted as HTML, PDF, or Word documents, with
various visual styles, by tools that understand the markup
annotations. Finally, you'll learn to use Pandoc, a tool for
translating between different markup languages, such as LaTeX,
HTML, and Markdown. This book will not describe all the
functionality that Pandoc provides, but will teach you how to
translate Markdown documents, how to customize your documents using
templates, and how to extend Pandoc's functionality using filters.
If that is something you are interested in, Introducing Markdown
and Pandoc will get you started. With this set of skills you'll be
able to write more efficiently without worrying about needless
formatting and other distractions. What You Will Learn Why and how
to use Markdown and Pandoc Write Markdown Use extensions available
in Pandoc and Markdown Write math and code blocks Use templates and
produce documents Who This Book Is For Programmers and problem
solvers looking for technical documentation solutions.
Gain an accelerated introduction to domain-specific languages in R,
including coverage of regular expressions. This compact, in-depth
book shows you how DSLs are programming languages specialized for a
particular purpose, as opposed to general purpose programming
languages. Along the way, you'll learn to specify tasks you want to
do in a precise way and achieve programming goals within a
domain-specific context. Domain-Specific Languages in R includes
examples of DSLs including large data sets or matrix
multiplication; pattern matching DSLs for application in computer
vision; and DSLs for continuous time Markov chains and their
applications in data science. After reading and using this book,
you'll understand how to write DSLs in R and have skills you can
extrapolate to other programming languages. What You'll Learn
Program with domain-specific languages using R Discover the
components of DSLs Carry out large matrix expressions and
multiplications Implement metaprogramming with DSLs Parse and
manipulate expressions Who This Book Is For Those with prior
programming experience. R knowledge is helpful but not required.
Learn how to manipulate functions and expressions to modify how the
R language interprets itself. This book is an introduction to
metaprogramming in the R language, so you will write programs to
manipulate other programs. Metaprogramming in R shows you how to
treat code as data that you can generate, analyze, or modify. R is
a very high-level language where all operations are functions and
all functions are data that can be manipulated. This book shows you
how to leverage R's natural flexibility in how function calls and
expressions are evaluated, to create small domain-specific
languages to extend R within the R language itself. What You'll
Learn Find out about the anatomy of a function in R Look inside a
function call Work with R expressions and environments Manipulate
expressions in R Use substitutions Who This Book Is For Those with
at least some experience with R and certainly for those with
experience in other programming languages.
Discover best practices for data analysis and software development
in R and start on the path to becoming a fully-fledged data
scientist. Updated for the R 4.0 release, this book teaches you
techniques for both data manipulation and visualization and shows
you the best way for developing new software packages for R.
Beginning Data Science in R 4, Second Edition details how data
science is a combination of statistics, computational science, and
machine learning. You'll see how to efficiently structure and mine
data to extract useful patterns and build mathematical models. This
requires computational methods and programming, and R is an ideal
programming language for this. Modern data analysis requires
computational skills and usually a minimum of programming. After
reading and using this book, you'll have what you need to get
started with R programming with data science applications. Source
code will be available to support your next projects as well.
Source code is available at github.com/Apress/beg-data-science-r4.
What You Will Learn Perform data science and analytics using
statistics and the R programming language Visualize and explore
data, including working with large data sets found in big data
Build an R package Test and check your code Practice version
control Profile and optimize your code Who This Book Is For Those
with some data science or analytics background, but not necessarily
experience with the R programming language.
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