Видавець: 16
Atmajitsinh Gohil, Atmajitsingh Gohil
Vitor Bianchi Lanzetta
R is an open source language for data analysis and graphics that allows users to load various packages for effective and better data interpretation. Its popularity has soared in recent years because of its powerful capabilities when it comes to turning different kinds of data into intuitive visualization solutions.This book is an update to our earlier R data visualization cookbook with 100 percent fresh content and covering all the cutting edge R data visualization tools. This book is packed with practical recipes, designed to provide you with all the guidance needed to get to grips with data visualization using R. It starts off with the basics of ggplot2, ggvis, and plotly visualization packages, along with an introduction to creating maps and customizing them, before progressively taking you through various ggplot2 extensions, such as ggforce, ggrepel, and gganimate. Using real-world datasets, you will analyze and visualize your data as histograms, bar graphs, and scatterplots, and customize your plots with various themes and coloring options. The book also covers advanced visualization aspects such as creating interactive dashboards using ShinyBy the end of the book, you will be equipped with key techniques to create impressive data visualizations with professional efficiency and precision.
R Deep Learning Cookbook. Solve complex neural net problems with TensorFlow, H2O and MXNet
PKS Prakash, Achyutuni Sri Krishna Rao
Deep Learning is the next big thing. It is a part of machine learning. It's favorable results in applications with huge and complex data is remarkable. Simultaneously, R programming language is very popular amongst the data miners and statisticians. This book will help you to get through the problems that you face during the execution of different tasks and Understand hacks in deep learning, neural networks, and advanced machine learning techniques. It will also take you through complex deep learning algorithms and various deep learning packages and libraries in R. It will be starting with different packages in Deep Learning to neural networks and structures. You will also encounter the applications in text mining and processing along with a comparison between CPU and GPU performance.By the end of the book, you will have a logical understanding of Deep learning and different deep learning packages to have the most appropriate solutions for your problems.
Mark Hodnett, Joshua F. Wiley
Deep learning is a powerful subset of machine learning that is very successful in domains such as computer vision and natural language processing (NLP). This second edition of R Deep Learning Essentials will open the gates for you to enter the world of neural networks by building powerful deep learning models using the R ecosystem.This book will introduce you to the basic principles of deep learning and teach you to build a neural network model from scratch. As you make your way through the book, you will explore deep learning libraries, such as Keras, MXNet, and TensorFlow, and create interesting deep learning models for a variety of tasks and problems, including structured data, computer vision, text data, anomaly detection, and recommendation systems. You’ll cover advanced topics, such as generative adversarial networks (GANs), transfer learning, and large-scale deep learning in the cloud. In the concluding chapters, you will learn about the theoretical concepts of deep learning projects, such as model optimization, overfitting, and data augmentation, together with other advanced topics.By the end of this book, you will be fully prepared and able to implement deep learning concepts in your research work or projects.
R Deep Learning Projects. Master the techniques to design and develop neural network models in R
Yuxi (Hayden) Liu, Pablo Maldonado
R is a popular programming language used by statisticians and mathematicians for statistical analysis, and is popularly used for deep learning. Deep Learning, as we all know, is one of the trending topics today, and is finding practical applications in a lot of domains.This book demonstrates end-to-end implementations of five real-world projects on popular topics in deep learning such as handwritten digit recognition, traffic light detection, fraud detection, text generation, and sentiment analysis. You'll learn how to train effective neural networks in R—including convolutional neural networks, recurrent neural networks, and LSTMs—and apply them in practical scenarios. The book also highlights how neural networks can be trained using GPU capabilities. You will use popular R libraries and packages—such as MXNetR, H2O, deepnet, and more—to implement the projects. By the end of this book, you will have a better understanding of deep learning concepts and techniques and how to use them in a practical setting.
Yu-Wei, Chiu (David Chiu)
This cookbook offers a range of data analysis samples in simple and straightforward R code, providing step-by-step resources and time-saving methods to help you solve data problems efficiently.The first section deals with how to create R functions to avoid the unnecessary duplication of code. You will learn how to prepare, process, and perform sophisticated ETL for heterogeneous data sources with R packages. An example of data manipulation is provided, illustrating how to use the “dplyr” and “data.table” packages to efficiently process larger data structures. We also focus on “ggplot2” and show you how to create advanced figures for data exploration.In addition, you will learn how to build an interactive report using the “ggvis” package. Later chapters offer insight into time series analysis on financial data, while there is detailed information on the hot topic of machine learning, including data classification, regression, clustering, association rule mining, and dimension reduction.By the end of this book, you will understand how to resolve issues and will be able to comfortably offer solutions to problems encountered while performing data analysis.
David Lillis, David A Lillis
Jaynal Abedin, Jaynal Abedin, Hrishi Mittal
Aloysius Shao Qin Lim, Tjhi W Chandra,...
R i pakiet shiny. Kurs video. Interaktywne aplikacje w analizie danych
Alicja Wolny-Dominiak
Obierz kurs na... analizę danych W dzisiejszej praktyce biznesowej duże znaczenie mają dane i ich analiza. W analizie zastosowanie znajduje wiele modeli statystycznych, implementowanych w różnych programach komputerowych. Na przykład Excel ma specjalny dodatek, nazwany po prostu Analiza Danych. Bardzo popularne narzędzie stanowi program R, którego zaletą jest łatwe tworzenie dashboardów obliczeniowych automatyzujących operacje na danych i rysowanie wykresów z użyciem pakietu shiny. Pakiet ten jest oprogramowaniem typu open source, przeznaczonym także dla osób, które nie znają języków HTML, CSS i JavaScript. Ba, by tworzyć eleganckie i wydajne aplikacje internetowe w języku R, nie trzeba nawet być programistą. Pakiet shiny pozwala na automatyzację obliczeń, wizualizację danych i szacowanie modeli statystycznych stworzonych w R. Wbudowane w niego funkcje ułatwiają pracę z danymi – ich eksplorowanie i prezentowanie. Oprogramowanie to służy również do tworzenia dynamicznych dashboardów i paneli sterowania, które łączą różne wykresy, tabele, filtry i opcje wyboru, aby przedstawiać dane w czytelny i interaktywny sposób. Za jego pośrednictwem można przygotowywać także raporty – do tego celu służą odpowiednie aplikacje. Proponowany przez nas kurs wyjaśnia zasady działania pakietu shiny i uczy, jak z niego korzystać. Nabyte umiejętności mogą być dalej bezpośrednio przydatne podczas pracy z danymi i w trakcie analizy procesów biznesowych. Co Cię czeka podczas naszego profesjonalnego szkolenia W ramach kursu: Dowiesz się, jak zacząć pracę z pakietem shiny w RStudio Poznasz budowę prostej aplikacji rysującej histogram z szablonu w RStudio Przyjrzysz się budowie podstawowego interfejsu użytkownika ui – domyślne ui w pakiecie shiny Zobaczysz, jak wygląda rozszerzony interfejs użytkownika z wykorzystaniem innych pakietów przeznaczonych do korzystania z shiny Opanujesz zasady tworzenia serwera obliczeniowego i renderowania obliczeń w interfejsie użytkownika ui Dowiesz się, jakie jest zastosowanie reaktywnych możliwości w pakiecie Zrobisz update interfejsu użytkownika w trakcie pracy Zapoznasz się z wybranymi pakietami, które można zastosować w aplikacji webowej do renderowania danych i wykresów Stworzysz mapę w aplikacji webowej R i pakiet shiny. Kurs video. Interaktywne aplikacje w analizie danych kończy się na poziomie średnio zaawansowanym. Szkolenie pozwoli użytkownikowi zrozumieć sposób tworzenia aplikacji webowej w pakiecie shiny. Dalszy rozwój umiejętności jest uzależniony od wiedzy z zakresu programowania w języku R i w innych językach, głównie w JavaScripcie. Do czego i komu przydaje się język R Tematyka kursu ma zastosowanie przede wszystkim w wypadku osób pracujących na stanowiskach, na których używa się różnorodnych danych. W trakcie ich obróbki często powtarzają się te same schematy obliczeniowe – niezależnie od tego, jak zmienne są dane, na których się pracuje. By ułatwić sobie zadanie, zamiast w kółko powtarzać te same obliczenia, można zbudować w shiny aplikację webową, która będzie miała zakodowane schematy obliczeniowe w serwerze – jedyną zmienną będą wówczas dane wejściowe. Automatyzacja obliczeń bywa skomplikowana, ale jakże upraszcza życie!
Dipanjan Sarkar, Raghav Bali
Data science and machine learning are some of the top buzzwords in the technical world today. From retail stores to Fortune 500 companies, everyone is working hard to making machine learning give them data-driven insights to grow their business. With powerful data manipulation features, machine learning packages, and an active developer community, R empowers users to build sophisticated machine learning systems to solve real-world data problems.This book takes you on a data-driven journey that starts with the very basics of R and machine learning and gradually builds upon the concepts to work on projects that tackle real-world problems.You’ll begin by getting an understanding of the core concepts and definitions required to appreciate machine learning algorithms and concepts. Building upon the basics, you will then work on three different projects to apply the concepts of machine learning, following current trends and cover major algorithms as well as popular R packages in detail. These projects have been neatly divided into six different chapters covering the worlds of e-commerce, finance, and social-media, which are at the very core of this data-driven revolution. Each of the projects will help you to understand, explore, visualize, and derive insights depending upon the domain and algorithms.Through this book, you will learn to apply the concepts of machine learning to deal with data-related problems and solve them using the powerful yet simple language, R.
Dr. Sunil Kumar Chinnamgari
R is one of the most popular languages when it comes to performing computational statistics (statistical computing) easily and exploring the mathematical side of machine learning. With this book, you will leverage the R ecosystem to build efficient machine learning applications that carry out intelligent tasks within your organization.This book will help you test your knowledge and skills, guiding you on how to build easily through to complex machine learning projects. You will first learn how to build powerful machine learning models with ensembles to predict employee attrition. Next, you’ll implement a joke recommendation engine and learn how to perform sentiment analysis on Amazon reviews. You’ll also explore different clustering techniques to segment customers using wholesale data. In addition to this, the book will get you acquainted with credit card fraud detection using autoencoders, and reinforcement learning to make predictions and win on a casino slot machine.By the end of the book, you will be equipped to confidently perform complex tasks to build research and commercial projects for automated operations.
R: Mining spatial, text, web, and social media data. Create and customize data mining algorithms
Nathan H. Danneman, Richard Heimann, Pradeepta Mishra,...
Data mining is the first step to understanding data and making sense of heaps of data. Properly mined data forms the basis of all data analysis and computing performed on it. This learning path will take you from the very basics of data mining to advanced data mining techniques, and will end up with a specialized branch of data mining—social media mining.You will learn how to manipulate data with R using code snippets and how to mine frequent patterns, association, and correlation while working with R programs. You will discover how to write code for various predication models, stream data, and time-series data. You will also be introduced to solutions written in R based on R Hadoop projects. Now that you are comfortable with data mining with R, you will move on to implementing your knowledge with the help of end-to-end data mining projects. You will learn how to apply different mining concepts to various statistical and data applications in a wide range of fields. At this stage, you will be able to complete complex data mining cases and handle any issues you might encounter during projects.After this, you will gain hands-on experience of generating insights from social media data. You will get detailed instructions on how to obtain, process, and analyze a variety of socially-generated data while providing a theoretical background to accurately interpret your findings. You will be shown R code and examples of data that can be used as a springboard as you get the chance to undertake your own analyses of business, social, or political data.This Learning Path combines some of the best that Packt has to offer in one complete, curated package. It includes content from the following Packt products:? Learning Data Mining with R by Bater Makhabel ? R Data Mining Blueprints by Pradeepta Mishra? Social Media Mining with R by Nathan Danneman and Richard Heimann
R: Predictive Analysis. Master the art of predictive modeling
Eric Mayor, Tony Fischetti, Rui Miguel Forte
Predictive analytics is a field that uses data to build models that predict a future outcome of interest. It can be applied to a range of business strategies and has been a key player in search advertising and recommendation engines.The power and domain-specificity of R allows the user to express complex analytics easily, quickly, and succinctly. R offers a free and open source environment that is perfect for both learning and deploying predictive modeling solutions in the real world. This Learning Path will provide you with all the steps you need to master the art of predictive modeling with R. We start with an introduction to data analysis with R, and then gradually you’ll get your feet wet with predictive modeling. You will get to grips with the fundamentals of applied statistics and build on this knowledge to perform sophisticated and powerful analytics. You will be able to solve the difficulties relating to performing data analysis in practice and find solutions to working with “messy data”, large data, communicating results, and facilitating reproducibility. You will then perform key predictive analytics tasks using R, such as train and test predictive models for classification and regression tasks, score new data sets and so on. By the end of this Learning Path, you will have explored and tested the most popular modeling techniques in use on real-world data sets and mastered a diverse range of techniques in predictive analytics.This Learning Path combines some of the best that Packt has to offer in one complete, curated package. It includes content from the following Packt products:• Data Analysis with R, Tony Fischetti• Learning Predictive Analytics with R, Eric Mayor• Mastering Predictive Analytics with R, Rui Miguel Forte
R Programming By Example. Practical, hands-on projects to help you get started with R
Omar Trejo Navarro
R is a high-level statistical language and is widely used among statisticians and data miners to develop analytical applications. Often, data analysis people with great analytical skills lack solid programming knowledge and are unfamiliar with the correct ways to use R. Based on the version 3.4, this book will help you develop strong fundamentals when working with R by taking you through a series of full representative examples, giving you a holistic view of R.We begin with the basic installation and configuration of the R environment. As you progress through the exercises, you'll become thoroughly acquainted with R's features and its packages. With this book, you will learn about the basic concepts of R programming, work efficiently with graphs, create publication-ready and interactive 3D graphs, and gain a better understanding of the data at hand. The detailed step-by-step instructions will enable you to get a clean set of data, produce good visualizations, and create reports for the results. It also teaches you various methods to perform code profiling and performance enhancement with good programming practices, delegation, and parallelization.By the end of this book, you will know how to efficiently work with data, create quality visualizations and reports, and develop code that is modular, expressive, and maintainable.
R Programming Fundamentals. Deal with data using various modeling techniques
Kaelen Medeiros
R Programming Fundamentals, focused on R and the R ecosystem, introduces you to the tools for working with data. You’ll start by understanding how to set up R and RStudio, followed by exploring R packages, functions, data structures, control flow, and loops.Once you have grasped the basics, you’ll move on to studying data visualization and graphics. You’ll learn how to build statistical and advanced plots using the powerful ggplot2 library. In addition to this, you’ll discover data management concepts such as factoring, pivoting, aggregating, merging, and dealing with missing values.By the end of this book, you’ll have completed an entire data science project of your own for your portfolio or blog.
R: Recipes for Analysis, Visualization and Machine Learning. Click here to enter text
Shanthi Viswanathan, Atmajitsinh Gohil, Viswa Viswanathan, Yu-Wei,...
The R language is a powerful, open source, functional programming language. At its core, R is a statistical programming language that provides impressive tools to analyze data and create high-level graphics. This Learning Path is chock-full of recipes. Literally! It aims to excite you with awesome projects focused on analysis, visualization, and machine learning. We’ll start off with data analysis – this will show you ways to use R to generate professional analysis reports. We’ll then move on to visualizing our data – this provides you with all the guidance needed to get comfortable with data visualization with R. Finally, we’ll move into the world of machine learning – this introduces you to data classification, regression, clustering, association rule mining, and dimension reduction.This Learning Path combines some of the best that Packt has to offer in one complete, curated package. It includes content from the following Packt products:• R Data Analysis Cookbook by Viswa Viswanathan and Shanthi Viswanathan• R Data Visualization Cookbook by Atmajitsinh Gohil• Machine Learning with R Cookbook by Yu-Wei, Chiu (David Chiu)
R Statistics Cookbook. Over 100 recipes for performing complex statistical operations with R 3.5
Francisco Juretig
R is a popular programming language for developing statistical software. This book will be a useful guide to solving common and not-so-common challenges in statistics. With this book, you'll be equipped to confidently perform essential statistical procedures across your organization with the help of cutting-edge statistical tools.You'll start by implementing data modeling, data analysis, and machine learning to solve real-world problems. You'll then understand how to work with nonparametric methods, mixed effects models, and hidden Markov models. This book contains recipes that will guide you in performing univariate and multivariate hypothesis tests, several regression techniques, and using robust techniques to minimize the impact of outliers in data.You'll also learn how to use the caret package for performing machine learning in R. Furthermore, this book will help you understand how to interpret charts and plots to get insights for better decision making.By the end of this book, you will be able to apply your skills to statistical computations using R 3.5. You will also become well-versed with a wide array of statistical techniques in R that are extensively used in the data science industry.
R: Unleash Machine Learning Techniques. Smarter data analytics
Brett Lantz, Cory Lesmeister, Dipanjan Sarkar, Raghav...
R is the established language of data analysts and statisticians around the world. And you shouldn’t be afraid to use it…This Learning Path will take you through the fundamentals of R and demonstrate how to use the language to solve a diverse range of challenges through machine learning. Accessible yet comprehensive, it provides you with everything you need to become more a more fluent data professional, and more confident with R. In the first module you’ll get to grips with the fundamentals of R. This means you’ll be taking a look at some of the details of how the language works, before seeing how to put your knowledge into practice to build some simple machine learning projects that could prove useful for a range of real world problems.For the following two modules we’ll begin to investigate machine learning algorithms in more detail. To build upon the basics, you’ll get to work on three different projects that will test your skills. Covering some of the most important algorithms and featuring some of the most popular R packages, they’re all focused on solving real problems in different areas, ranging from finance to social media.This Learning Path has been curated from three Packt products:• R Machine Learning By Example By Raghav Bali, Dipanjan Sarkar• Machine Learning with R - Second Edition By Brett Lantz• Mastering Machine Learning with R By Cory Lesmeister
Arkadiusz Kołodziej
Autor prezentuje złożone zagadnienia analizy danych empirycznych za pomocą języka R w sposób wyjątkowo przejrzysty i systematyczny. Jego praca stanowi odpowiedź na rosnące zapotrzebowanie na literaturę, która nie tylko wprowadza w podstawy programowania, ale również pokazuje praktyczne zastosowania tego narzędzia w badaniach społecznych. Książka została podzielona na trzy rozdziały, które logicznie prowadzą czytelnika od podstaw języka R, przez przetwarzanie i wizualizację danych, aż po zaawansowane techniki analizy danych. Taki układ sprawia, że materiał jest zrozumiały nawet dla tych osób, które nie miały wcześniej do czynienia z programowaniem czy analizą danych. dr hab. Anna Turczak, prof. US Program R stał się swoistym lingua franca, obok Pythona, w zakresie szeroko rozumianej analizy danych. Wiele instytucji na świecie korzysta bowiem z programu R nie tylko do celów stricte naukowych, ale także biznesowych. [...] Książka jest niewątpliwie wartościową pozycją dla studentów nauk społecznych i informatycznych oraz wszystkich osób zgłębiających metody statystyczne i statystycznej analizy danych. Może być przydatna również dla naukowców, którzy wykorzystują w badaniach metody statystyczne, metody analizy danych, zaawansowane metody sztucznej inteligencji czy uczenia maszynowego. dr hab. Marcin Pełka, prof. UEW
R Web Scraping Quick Start Guide. Techniques and tools to crawl and scrape data from websites
Olgun Aydin
Web scraping is a technique to extract data from websites. It simulates the behavior of a website user to turn the website itself into a web service to retrieve or introduce new data. This book gives you all you need to get started with scraping web pages using R programming.You will learn about the rules of RegEx and Xpath, key components for scraping website data. We will show you web scraping techniques, methodologies, and frameworks. With this book's guidance, you will become comfortable with the tools to write and test RegEx and XPath rules. We will focus on examples of dynamic websites for scraping data and how to implement the techniques learned. You will learn how to collect URLs and then create XPath rules for your first web scraping script using rvest library. From the data you collect, you will be able to calculate the statistics and create R plots to visualize them. Finally, you will discover how to use Selenium drivers with R for more sophisticated scraping. You will create AWS instances and use R to connect a PostgreSQL database hosted on AWS. By the end of the book, you will be sufficiently confident to create end-to-end web scraping systems using R.