Beginning Data Science With R
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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 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 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.
Beginning Data Science In R Data Analysis Visualization And Modelling
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Author : Balakrishna Ch
language : en
Publisher:
Release Date : 2023-09-02
Beginning Data Science In R Data Analysis Visualization And Modelling written by Balakrishna Ch and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-09-02 with categories.
Welcome to "Beginning Data Science in R: Data Analysis, Visualization, and Modeling." In this book, we embark on an exciting journey into the world of data science using the R programming language. Whether you're a novice seeking to explore the fundamentals or an experienced practitioner looking for a comprehensive reference, this book is designed to be your companion. Data science has become an integral part of decision-making processes across various industries. From understanding customer behavior to predicting market trends and making informed business choices, the power of data analysis, visualization, and modeling cannot be overstated. R, with its extensive ecosystem of packages and tools, has emerged as a preferred choice for data scientists due to its versatility and ability to handle complex analytical tasks. Our aim in this book is to provide you with a solid foundation in data science techniques using R. We will guide you through the entire data science workflow, from data acquisition and cleaning to exploratory data analysis, visualization, and building predictive models. Each chapter is carefully crafted to introduce concepts progressively, with hands-on examples and practical exercises to reinforce your understanding.
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 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.
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
R Programming For Data Analysis
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Author : Peter Simon
language : en
Publisher: Peter Simon
Release Date :
R Programming For Data Analysis written by Peter Simon and has been published by Peter Simon this book supported file pdf, txt, epub, kindle and other format this book has been release on with Computers categories.
Unlock the Power of Data with R Programming for Data Analysis — Your Complete Guide from Beginner to Expert! Are you eager to master data analytics using R programming but unsure where to start? Whether you're a student, aspiring data analyst, or professional looking to upskill, this book is the perfect starting point to learn R for data analysis and become proficient with one of the most powerful tools in the industry. What Makes This Book Your Ultimate Resource for Data Analysis Using R Software? This comprehensive guide covers everything you need to confidently use R software data analysis tools, starting from installation to mastering essential programming concepts. You’ll learn how to harness the full potential of the R language for data science and data analytics with R programming to unlock actionable insights from your data. Inside You’ll Discover: Step-by-step guidance on setting up R and RStudio, the leading R data analysis software, so you can get started quickly. Master core data structures in R language data analysis: vectors, lists, data frames, and factors—building blocks for efficient data analysis using R programming. Practical techniques for cleaning, transforming, and visualizing data—skills every R data analyst needs. How to apply data science with R basics to perform statistical analysis and modeling. Real-world examples that make learning intuitive and effective—like a top R programming course or R programming training session. Who Should Read This? Beginners wanting to learn R programming specifically for data science with R Analysts seeking to upgrade their skills using the best r analysis software Students enrolled in a data science with R course or anyone learning the r programming language course Professionals eager to master r coding language for actionable data analytics R programming Why Choose This Book Over Others? This isn’t just another technical manual. It’s designed to simplify the complexities of R programming for data analysis with clear explanations, practical exercises, and a focus on real-world applications. Whether you want to become a skilled R language for data analytics professional or simply want to understand your data better, this book makes it achievable. Start your journey to becoming a confident data analyst today. Scroll up and order R Programming for Data Analysis now—unlock insights, drive decisions, and transform data into success!
Beginner S Guide For Data Analysis Using R Programming
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Author : Jeeva Jose
language : en
Publisher: KHANNA PUBLISHING HOUSE
Release Date :
Beginner S Guide For Data Analysis Using R Programming written by Jeeva Jose and has been published by KHANNA PUBLISHING HOUSE this book supported file pdf, txt, epub, kindle and other format this book has been release on with Computers categories.
R programming is an efficient tool for statistical analysis of data. Data science has become critical to each field and the popularity of R is skyrocketing. Organization as large and diverse as Google, Facebook, Microsoft, Bank of America, Ford Motor Company, Mozilla, Thomas Cook, The New York Times, The National Weather Service, Twitter, ANZ Bank, Uber, Airbnb etc . have turned to R for reporting, analyzing and visualization of data, this book is for students and professionals of Mathematics, Statistics, Physics, Chemistry, Biology, Social Science and Medicine, Business, Engineering, Software, Information Technology, Sales, Bio Informatics, Pharmacy and any one, where data needs to be analyzed and represented graphically.
Functional Programming In R
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Author : Thomas Mailund
language : en
Publisher: Apress
Release Date : 2017-03-27
Functional Programming 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 2017-03-27 with Computers categories.
Master functions and discover how to write functional programs in R. In this concise book, you'll make your functions pure by avoiding side-effects; you’ll write functions that manipulate other functions, and you’ll construct complex functions using simpler functions as building blocks. In Functional Programming in R, you’ll see how we can 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 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 atleast some experience with programming in R.