Beginning Data Science In R 4
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Beginning Data Science In R 4
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Author : Thomas Mailund
language : en
Publisher: Apress
Release Date : 2022-06-28
Beginning Data Science In R 4 written by Thomas Mailund and has been published by Apress this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-06-28 with Computers categories.
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. 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.
Beginning Data Science In R
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Author : Thomas Mailund
language : en
Publisher: Apress
Release Date : 2017-03-09
Beginning Data Science In R written by Thomas Mailund and has been published by Apress this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-03-09 with Computers categories.
Discover best practices for data analysis and software development in R and start on the path to becoming a fully-fledged data scientist. 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 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. This book is based on a number of lecture notes for classes the author has taught on data science and statistical programming using the R programming language. Modern data analysis requires computational skills and usually a minimum of programming. 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.
Beginning Data Science With R
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Author : Manas A. Pathak
language : en
Publisher: Springer
Release Date : 2014-12-08
Beginning Data Science With R written by Manas A. Pathak and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014-12-08 with Technology & Engineering categories.
“We live in the age of data. In the last few years, the methodology of extracting insights from data or "data science" has emerged as a discipline in its own right. The R programming language has become one-stop solution for all types of data analysis. The growing popularity of R is due its statistical roots and a vast open source package library. The goal of “Beginning Data Science with R” is to introduce the readers to some of the useful data science techniques and their implementation with the R programming language. The book attempts to strike a balance between the how: specific processes and methodologies, and understanding the why: going over the intuition behind how a particular technique works, so that the reader can apply it to the problem at hand. This book will be useful for readers who are not familiar with statistics and the R programming language.
R 4 Data Science Quick Reference
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Author : Thomas Mailund
language : en
Publisher: Apress
Release Date : 2022-11-12
R 4 Data Science Quick Reference written by Thomas Mailund and has been published by Apress this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-11-12 with Computers categories.
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.
Geographical Data Science And Spatial Data Analysis
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Author : Lex Comber
language : en
Publisher: SAGE
Release Date : 2020-12-02
Geographical Data Science And Spatial Data Analysis written by Lex Comber and has been published by SAGE this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-12-02 with Science categories.
We are in an age of big data where all of our everyday interactions and transactions generate data. Much of this data is spatial – it is collected some-where – and identifying analytical insight from trends and patterns in these increasing rich digital footprints presents a number of challenges. Whilst other books describe different flavours of Data Analytics in R and other programming languages, there are none that consider Spatial Data (i.e. the location attached to data), or that consider issues of inference, linking Big Data, Geography, GIS, Mapping and Spatial Analytics. This is a ‘learning by doing’ textbook, building on the previous book by the same authors, An Introduction to R for Spatial Analysis and Mapping. It details the theoretical issues in analyses of Big Spatial Data and developing practical skills in the reader for addressing these with confidence.
Sources Of Knowledge Through Creative And Systematic Research Work
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Author :
language : en
Publisher: Xoffencer International Book Publication House
Release Date : 2025-08-15
Sources Of Knowledge Through Creative And Systematic Research Work written by and has been published by Xoffencer International Book Publication House this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-08-15 with Language Arts & Disciplines categories.
Knowledge is a form of familiarity, awareness understanding, or acquittance. It often involves the possession of information learned through experience, and can be understood as a cognitive success or an epistemic contact with reality, like making a discovery. Knowledge is often understood as a state of an individual person, but it can also refer to a characteristic of a group of people,, as group knowledge, social knowledge, or collective knowledge. Some social sciences understand knowledge as a broad social phenomenon that is similar to culture. Knowledge is closely related to intelligence, but intelligence is more about the ability to acquire, pocess, and apply information, while knowledge concerns information and skills that a person already possess. Knowledge can be produced in many ways. The main source of empirical knowledge is perception, which involves the usage of the senses to learn about the external world. Introspection allows people to learn about their internal mental states and processes. Other sources of knowledge include memory, rational intuition, inference and testimony. According to foundationalism, some of these sources are basic in that they can justify beliefs, without depending on other mental states. Coherentists reject this claim and contend that a sufficient degree of coherence among all the mental states of the believer is necessary for knowledge. According to in finitism, an infinite chain of beliefs is needed.
R For Data Science
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Author : Hadley Wickham
language : en
Publisher: O'Reilly Media
Release Date : 2023-10-03
R For Data Science written by Hadley Wickham and has been published by O'Reilly Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-10-03 with Computers categories.
Learn how to use R to turn data into insight, knowledge, and understanding. Ideal for current and aspiring data scientists, this book introduces you to doing data science with R and RStudio, as well as the tidyverse--a collection of R packages designed to work together to make data science fast, fluent, and fun. Even if you have no programming experience, this updated edition will have you doing data science quickly. You'll learn how to import, transform, and visualize your data and communicate the results. And you'll get a complete, big-picture understanding of the data science cycle and the basic tools you need to manage the details. Each section in this edition includes exercises to help you practice what you've learned along the way. Updated for the latest tidyverse best practices, new chapters dive deeper into visualization and data wrangling, show you how to get data from spreadsheets, databases, and websites, and help you make the most of new programming tools. You'll learn how to: Visualize-create plots for data exploration and communication of results Transform-discover types of variables and the tools you can use to work with them Import-get data into R and in a form convenient for analysis Program-learn R tools for solving data problems with greater clarity and ease Communicate-integrate prose, code, and results with Quarto
Introduction To Data Science
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Author : Rafael A. Irizarry
language : en
Publisher: CRC Press
Release Date : 2024-08-02
Introduction To Data Science written by Rafael A. Irizarry and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-08-02 with Mathematics categories.
Unlike the first edition, the new edition has been split into two books. Thoroughly revised and updated, this is the first book of the second edition of Introduction to Data Science: Data Wrangling and Visualization with R. It introduces skills that can help you tackle real-world data analysis challenges. These include R programming, data wrangling with dplyr, data visualization with ggplot2, file organization with UNIX/Linux shell, version control with Git and GitHub, and reproducible document preparation with Quarto and knitr. The new edition includes additional material on data.table, locales, and accessing data through APIs. The book is divided into four parts: R, Data Visualization, Data Wrangling, and Productivity Tools. Each part has several chapters meant to be presented as one lecture and includes dozens of exercises. The second book will cover topics including probability, statistics and prediction algorithms with R. Throughout the book, we use motivating case studies. In each case study, we try to realistically mimic a data scientist’s experience. For each of the skills covered, we start by asking specific questions and answer these through data analysis. Examples of the case studies included in the book are: US murder rates by state, self-reported student heights, trends in world health and economics, and the impact of vaccines on infectious disease rates. This book is meant to be a textbook for a first course in Data Science. No previous knowledge of R is necessary, although some experience with programming may be helpful. To be a successful data analyst implementing these skills covered in this book requires understanding advanced statistical concepts, such as those covered the second book. If you read and understand all the chapters and complete all the exercises in this book, and understand statistical concepts, you will be well-positioned to perform basic data analysis tasks and you will be prepared to learn the more advanced concepts and skills needed to become an expert.
R For Data Science
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Author : Hadley Wickham
language : en
Publisher: "O'Reilly Media, Inc."
Release Date : 2023-06-08
R For Data Science written by Hadley Wickham and has been published by "O'Reilly Media, Inc." this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-06-08 with Computers categories.
Cover -- Copyright -- Table of Contents -- Preface -- What You Will Learn -- How This Book Is Organized -- What You Won't Learn -- Big Data -- Python, Julia, and Friends -- Nonrectangular Data -- Hypothesis Confirmation -- Prerequisites -- R -- RStudio -- The Tidyverse -- Other Packages -- Running R Code -- Getting Help and Learning More -- Acknowledgments -- Online Version -- Conventions Used in This Book -- Using Code Examples -- O'Reilly Online Learning -- How to Contact Us -- Part I. Explore -- Chapter 1. Data Visualization with ggplot2 -- Introduction -- Prerequisites -- First Steps -- The mpg Data Frame -- Creating a ggplot -- A Graphing Template -- Exercises -- Aesthetic Mappings -- Exercises -- Common Problems -- Facets -- Exercises -- Geometric Objects -- Exercises -- Statistical Transformations -- Exercises -- Position Adjustments -- Exercises -- Coordinate Systems -- Exercises -- The Layered Grammar of Graphics -- Chapter 2. Workflow: Basics -- Coding Basics -- What's in a Name? -- Calling Functions -- Exercises -- Chapter 3. Data Transformation with dplyr -- Introduction -- Prerequisites -- nycflights13 -- dplyr Basics -- Filter Rows with filter() -- Comparisons -- Logical Operators -- Missing Values -- Exercises -- Arrange Rows with arrange() -- Exercises -- Select Columns with select() -- Exercises -- Add New Variables with mutate() -- Useful Creation Functions -- Exercises -- Grouped Summaries with summarize() -- Combining Multiple Operations with the Pipe -- Missing Values -- Counts -- Useful Summary Functions -- Grouping by Multiple Variables -- Ungrouping -- Exercises -- Grouped Mutates (and Filters) -- Exercises -- Chapter 4. Workflow: Scripts -- Running Code -- RStudio Diagnostics -- Exercises -- Chapter 5. Exploratory Data Analysis -- Introduction -- Prerequisites -- Questions -- Variation -- Visualizing Distributions.
R Data Science Quick Reference
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Author : Thomas Mailund
language : en
Publisher: Apress
Release Date : 2019-08-07
R Data Science Quick Reference written by Thomas Mailund and has been published by Apress this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-08-07 with Computers categories.
In this handy, practical book you will cover each concept concisely, with many illustrative examples. You'll be introduced to several R data science packages, with examples of how to use each of them. In this book, you’ll learn about the following APIs and packages that deal specifically with data science applications: readr, dibble, forecasts, lubridate, stringr, tidyr, magnittr, dplyr, purrr, ggplot2, modelr, and more. After using this handy quick reference guide, 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. What You Will Learn 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.