Modern Data Visualization With R
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Modern Data Visualization With R
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Author : Robert Kabacoff
language : en
Publisher: Chapman & Hall/CRC The R Series
Release Date : 2024-03-08
Modern Data Visualization With R written by Robert Kabacoff and has been published by Chapman & Hall/CRC The R Series this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-03-08 with categories.
Ways that raw and summary data can be turned into visualizations that convey meaningful insights: basic graphs, bar charts, scatter plots, and line charts, and progresses to tree maps, alluvial plots, radar charts, mosaic plots, grouped dot plots, effects plots, multivariate presentations such as corrgrams, biplots, network diagrams.
R Programming
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Author : Andy Vickler
language : en
Publisher: R Programming
Release Date : 2023-05-06
R Programming written by Andy Vickler and has been published by R Programming this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-05-06 with categories.
This book is like a friend who advises and guides you on how to use the ggplot2 package for making data visualizations. With this book, you will learn how to get started with data visualization in R. You will learn basic concepts of graphics, and you will also learn how to perform statistical analyses. This book is for everyone who faces a difficult task when trying to make data visualizations using R. It is also for people who are interested in learning more about statistics and graphical techniques. The book also teaches readers how to get started with ggplot2, and it also introduces the basics of R so that readers are aware of the basic commands and functions in R, as well as importing libraries. R has transformed the way statistical modeling approaches are applied, and has become a tool which is commonly used by analysts, programmers and visual artists alike. Its ease of use and flexibility make it an invaluable tool for working with large amounts of data. This book provides you with a comprehensive guide to how R can be used for data visualization. You'll learn how to use the statistical modeling and graphics packages in R, as well as gain an understanding of the principles behind both traditional and modern data visualization techniques. Think of R as a programming language that provides access to the power of machine learning and statistical computing. It's software used by statisticians and data scientists, with its capabilities in statistics, data visualization, machine learning, and more.
Modern Data Science With R
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Author : Benjamin S. Baumer
language : en
Publisher: CRC Press
Release Date : 2021-03-31
Modern Data Science With R written by Benjamin S. Baumer and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-03-31 with Business & Economics categories.
From a review of the first edition: "Modern Data Science with R... is rich with examples and is guided by a strong narrative voice. What’s more, it presents an organizing framework that makes a convincing argument that data science is a course distinct from applied statistics" (The American Statistician). Modern Data Science with R is a comprehensive data science textbook for undergraduates that incorporates statistical and computational thinking to solve real-world data problems. Rather than focus exclusively on case studies or programming syntax, this book illustrates how statistical programming in the state-of-the-art R/RStudio computing environment can be leveraged to extract meaningful information from a variety of data in the service of addressing compelling questions. The second edition is updated to reflect the growing influence of the tidyverse set of packages. All code in the book has been revised and styled to be more readable and easier to understand. New functionality from packages like sf, purrr, tidymodels, and tidytext is now integrated into the text. All chapters have been revised, and several have been split, re-organized, or re-imagined to meet the shifting landscape of best practice.
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.
Python And R For The Modern Data Scientist
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Author : Rick J. Scavetta
language : en
Publisher: "O'Reilly Media, Inc."
Release Date : 2021-06-22
Python And R For The Modern Data Scientist written by Rick J. Scavetta 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 2021-06-22 with Computers categories.
Success in data science depends on the flexible and appropriate use of tools. That includes Python and R, two of the foundational programming languages in the field. This book guides data scientists from the Python and R communities along the path to becoming bilingual. By recognizing the strengths of both languages, you'll discover new ways to accomplish data science tasks and expand your skill set. Authors Rick Scavetta and Boyan Angelov explain the parallel structures of these languages and highlight where each one excels, whether it's their linguistic features or the powers of their open source ecosystems. You'll learn how to use Python and R together in real-world settings and broaden your job opportunities as a bilingual data scientist. Learn Python and R from the perspective of your current language Understand the strengths and weaknesses of each language Identify use cases where one language is better suited than the other Understand the modern open source ecosystem available for both, including packages, frameworks, and workflows Learn how to integrate R and Python in a single workflow Follow a case study that demonstrates ways to use these languages together
An Introduction To Data Analysis In R
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Author : Alfonso Zamora Saiz
language : en
Publisher: Springer Nature
Release Date : 2020-07-27
An Introduction To Data Analysis In R written by Alfonso Zamora Saiz and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-07-27 with Computers categories.
This textbook offers an easy-to-follow, practical guide to modern data analysis using the programming language R. The chapters cover topics such as the fundamentals of programming in R, data collection and preprocessing, including web scraping, data visualization, and statistical methods, including multivariate analysis, and feature exercises at the end of each section. The text requires only basic statistics skills, as it strikes a balance between statistical and mathematical understanding and implementation in R, with a special emphasis on reproducible examples and real-world applications. This textbook is primarily intended for undergraduate students of mathematics, statistics, physics, economics, finance and business who are pursuing a career in data analytics. It will be equally valuable for master students of data science and industry professionals who want to conduct data analyses.
R Data Analysis And Visualization
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Author : Tony Fischetti
language : en
Publisher: Packt Publishing Ltd
Release Date : 2016-06-24
R Data Analysis And Visualization written by Tony Fischetti and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-06-24 with Computers categories.
Master the art of building analytical models using R About This Book Load, wrangle, and analyze your data using the world's most powerful statistical programming language Build and customize publication-quality visualizations of powerful and stunning R graphs Develop key skills and techniques with R to create and customize data mining algorithms Use R to optimize your trading strategy and build up your own risk management system Discover how to build machine learning algorithms, prepare data, and dig deep into data prediction techniques with R Who This Book Is For This course is for data scientist or quantitative analyst who are looking at learning R and take advantage of its powerful analytical design framework. It's a seamless journey in becoming a full-stack R developer. What You Will Learn Describe and visualize the behavior of data and relationships between data Gain a thorough understanding of statistical reasoning and sampling Handle missing data gracefully using multiple imputation Create diverse types of bar charts using the default R functions Familiarize yourself with algorithms written in R for spatial data mining, text mining, and so on Understand relationships between market factors and their impact on your portfolio Harness the power of R to build machine learning algorithms with real-world data science applications Learn specialized machine learning techniques for text mining, big data, and more In Detail The R learning path created for you has five connected modules, which are a mini-course in their own right. As you complete each one, you'll have gained key skills and be ready for the material in the next module! This course begins by looking at the Data Analysis with R module. This will help you navigate the R environment. You'll gain a thorough understanding of statistical reasoning and sampling. Finally, you'll be able to put best practices into effect to make your job easier and facilitate reproducibility. The second place to explore is R Graphs, which will help you leverage powerful default R graphics and utilize advanced graphics systems such as lattice and ggplot2, the grammar of graphics. You'll learn how to produce, customize, and publish advanced visualizations using this popular and powerful framework. With the third module, Learning Data Mining with R, you will learn how to manipulate data with R using code snippets and be introduced to mining frequent patterns, association, and correlations while working with R programs. The Mastering R for Quantitative Finance module pragmatically introduces both the quantitative finance concepts and their modeling in R, enabling you to build a tailor-made trading system on your own. By the end of the module, you will be well-versed with various financial techniques using R and will be able to place good bets while making financial decisions. Finally, we'll look at the Machine Learning with R module. With this module, you'll discover all the analytical tools you need to gain insights from complex data and learn how to choose the correct algorithm for your specific needs. You'll also learn to apply machine learning methods to deal with common tasks, including classification, prediction, forecasting, and so on. Style and approach Learn data analysis, data visualization techniques, data mining, and machine learning all using R and also learn to build models in quantitative finance using this powerful language.
Interactive Web Based Data Visualization With R Plotly And Shiny
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Author : Carson Sievert
language : en
Publisher: CRC Press
Release Date : 2020-01-30
Interactive Web Based Data Visualization With R Plotly And Shiny written by Carson Sievert and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-01-30 with Business & Economics categories.
The richly illustrated Interactive Web-Based Data Visualization with R, plotly, and shiny focuses on the process of programming interactive web graphics for multidimensional data analysis. It is written for the data analyst who wants to leverage the capabilities of interactive web graphics without having to learn web programming. Through many R code examples, you will learn how to tap the extensive functionality of these tools to enhance the presentation and exploration of data. By mastering these concepts and tools, you will impress your colleagues with your ability to quickly generate more informative, engaging, and reproducible interactive graphics using free and open source software that you can share over email, export to pdf, and more. Key Features: Convert static ggplot2 graphics to an interactive web-based form Link, animate, and arrange multiple plots in standalone HTML from R Embed, modify, and respond to plotly graphics in a shiny app Learn best practices for visualizing continuous, discrete, and multivariate data Learn numerous ways to visualize geo-spatial data This book makes heavy use of plotly for graphical rendering, but you will also learn about other R packages that support different phases of a data science workflow, such as tidyr, dplyr, and tidyverse. Along the way, you will gain insight into best practices for visualization of high-dimensional data, statistical graphics, and graphical perception. The printed book is complemented by an interactive website where readers can view movies demonstrating the examples and interact with graphics.
Statistical Inference Via Data Science A Moderndive Into R And The Tidyverse
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Author : Chester Ismay
language : en
Publisher: CRC Press
Release Date : 2019-12-23
Statistical Inference Via Data Science A Moderndive Into R And The Tidyverse written by Chester Ismay and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-12-23 with Mathematics categories.
Statistical Inference via Data Science: A ModernDive into R and the Tidyverse provides a pathway for learning about statistical inference using data science tools widely used in industry, academia, and government. It introduces the tidyverse suite of R packages, including the ggplot2 package for data visualization, and the dplyr package for data wrangling. After equipping readers with just enough of these data science tools to perform effective exploratory data analyses, the book covers traditional introductory statistics topics like confidence intervals, hypothesis testing, and multiple regression modeling, while focusing on visualization throughout. Features: ● Assumes minimal prerequisites, notably, no prior calculus nor coding experience ● Motivates theory using real-world data, including all domestic flights leaving New York City in 2013, the Gapminder project, and the data journalism website, FiveThirtyEight.com ● Centers on simulation-based approaches to statistical inference rather than mathematical formulas ● Uses the infer package for "tidy" and transparent statistical inference to construct confidence intervals and conduct hypothesis tests via the bootstrap and permutation methods ● Provides all code and output embedded directly in the text; also available in the online version at moderndive.com This book is intended for individuals who would like to simultaneously start developing their data science toolbox and start learning about the inferential and modeling tools used in much of modern-day research. The book can be used in methods and data science courses and first courses in statistics, at both the undergraduate and graduate levels.
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.