Data Wrangling With R

Author: Bradley C. Boehmke, Ph.D.
Editor: Springer
ISBN: 3319455990
Size: 17,96 MB
Format: PDF, ePub
Read: 323
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This guide for practicing statisticians, data scientists, and R users and programmers will teach the essentials of preprocessing: data leveraging the R programming language to easily and quickly turn noisy data into usable pieces of information. Data wrangling, which is also commonly referred to as data munging, transformation, manipulation, janitor work, etc., can be a painstakingly laborious process. Roughly 80% of data analysis is spent on cleaning and preparing data; however, being a prerequisite to the rest of the data analysis workflow (visualization, analysis, reporting), it is essential that one become fluent and efficient in data wrangling techniques. This book will guide the user through the data wrangling process via a step-by-step tutorial approach and provide a solid foundation for working with data in R. The author's goal is to teach the user how to easily wrangle data in order to spend more time on understanding the content of the data. By the end of the book, the user will have learned: How to work with different types of data such as numerics, characters, regular expressions, factors, and dates The difference between different data structures and how to create, add additional components to, and subset each data structure How to acquire and parse data from locations previously inaccessible How to develop functions and use loop control structures to reduce code redundancy How to use pipe operators to simplify code and make it more readable How to reshape the layout of data and manipulate, summarize, and join data sets

Expert Data Wrangling With R

Author: Garrett Grolemund
Editor:
ISBN:
Size: 14,87 MB
Format: PDF, ePub
Read: 624
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"Analysts often spend 50-80% of their time preparing and transforming data sets before they begin more formal analysis work. This video tutorial shows you how to streamline your code-and your thinking-by introducing a set of principles and R packages that make this work much faster and easier. Garrett Grolemund, Data Scientist and Master Instructor at RStudio, demonstrates how R and its packages help you tackle three main issues. Data Manipulation. Data sets contain more information than they display. By transforming your data, you can reveal a wealth of descriptive statistics, group level observations, and hidden variables. R's dplyr package provides optimized functions to help you transform data, as well as a pipe syntax that makes R code more concise and intuitive. Data Tidying. Data sets come in many formats, but R prefers just one. R runs quickly and intuitively when your data is stored in the tidy format, a layout that allows vectorized programming. R's tidyr package reshapes the layout of your data sets, making them tidy while preserving the relationships they contain. Data Visualization. The structure of data visualizations parallels the structure of data sets. Once your data is tidy, visualizations become straightforward: each observation in your dataset becomes a mark on a graph, each variable becomes a visual property of the marks. The result is a grammar of graphics that lets you create thousands of graphs. R's ggvis package implements the grammar, providing a system of data visualization for R."--Resource description page.

Data Wrangling With R

Author: ROMEO. VAIDYA CABRERA (PRASAD.)
Editor:
ISBN: 9781838559793
Size: 10,86 MB
Format: PDF, ePub
Read: 486
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Hands On Data Science With R

Author: Vitor Bianchi Lanzetta
Editor: Packt Publishing Ltd
ISBN: 1789135834
Size: 19,50 MB
Format: PDF, Docs
Read: 579
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A hands-on guide for professionals to perform various data science tasks in R Key Features Explore the popular R packages for data science Use R for efficient data mining, text analytics and feature engineering Become a thorough data science professional with the help of hands-on examples and use-cases in R Book Description R is the most widely used programming language, and when used in association with data science, this powerful combination will solve the complexities involved with unstructured datasets in the real world. This book covers the entire data science ecosystem for aspiring data scientists, right from zero to a level where you are confident enough to get hands-on with real-world data science problems. The book starts with an introduction to data science and introduces readers to popular R libraries for executing data science routine tasks. This book covers all the important processes in data science such as data gathering, cleaning data, and then uncovering patterns from it. You will explore algorithms such as machine learning algorithms, predictive analytical models, and finally deep learning algorithms. You will learn to run the most powerful visualization packages available in R so as to ensure that you can easily derive insights from your data. Towards the end, you will also learn how to integrate R with Spark and Hadoop and perform large-scale data analytics without much complexity. What you will learn Understand the R programming language and its ecosystem of packages for data science Obtain and clean your data before processing Master essential exploratory techniques for summarizing data Examine various machine learning prediction, models Explore the H2O analytics platform in R for deep learning Apply data mining techniques to available datasets Work with interactive visualization packages in R Integrate R with Spark and Hadoop for large-scale data analytics Who this book is for If you are a budding data scientist keen to learn about the popular pandas library, or a Python developer looking to step into the world of data analysis, this book is the ideal resource you need to get started. Some programming experience in Python will be helpful to get the most out of this course

Introduction To Data Science

Author: Rafael A. Irizarry
Editor: CRC Press
ISBN: 1000708039
Size: 12,56 MB
Format: PDF, Docs
Read: 669
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Introduction to Data Science: Data Analysis and Prediction Algorithms with R introduces concepts and skills that can help you tackle real-world data analysis challenges. It covers concepts from probability, statistical inference, linear regression, and machine learning. It also helps you develop skills such as R programming, data wrangling, data visualization, predictive algorithm building, file organization with UNIX/Linux shell, version control with Git and GitHub, and reproducible document preparation. This book is a textbook for a first course in data science. No previous knowledge of R is necessary, although some experience with programming may be helpful. The book is divided into six parts: R, data visualization, statistics with R, data wrangling, machine learning, and productivity tools. Each part has several chapters meant to be presented as one lecture. The author uses motivating case studies that realistically mimic a data scientist’s experience. He starts by asking specific questions and answers these through data analysis so concepts are learned as a means to answering the questions. Examples of the case studies included are: US murder rates by state, self-reported student heights, trends in world health and economics, the impact of vaccines on infectious disease rates, the financial crisis of 2007-2008, election forecasting, building a baseball team, image processing of hand-written digits, and movie recommendation systems. The statistical concepts used to answer the case study questions are only briefly introduced, so complementing with a probability and statistics textbook is highly recommended for in-depth understanding of these concepts. If you read and understand the chapters and complete the exercises, you will be prepared to learn the more advanced concepts and skills needed to become an expert.

Data Wrangling With Python

Author: Jacqueline Kazil
Editor: "O'Reilly Media, Inc."
ISBN: 1491956801
Size: 13,56 MB
Format: PDF, ePub, Mobi
Read: 929
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How do you take your data analysis skills beyond Excel to the next level? By learning just enough Python to get stuff done. This hands-on guide shows non-programmers like you how to process information that’s initially too messy or difficult to access. You don't need to know a thing about the Python programming language to get started. Through various step-by-step exercises, you’ll learn how to acquire, clean, analyze, and present data efficiently. You’ll also discover how to automate your data process, schedule file- editing and clean-up tasks, process larger datasets, and create compelling stories with data you obtain. Quickly learn basic Python syntax, data types, and language concepts Work with both machine-readable and human-consumable data Scrape websites and APIs to find a bounty of useful information Clean and format data to eliminate duplicates and errors in your datasets Learn when to standardize data and when to test and script data cleanup Explore and analyze your datasets with new Python libraries and techniques Use Python solutions to automate your entire data-wrangling process

Harness The Power Of Tidyverse For Data Preprocessing And Visualisation In R

Author: Minerva Singh
Editor:
ISBN:
Size: 12,77 MB
Format: PDF, Kindle
Read: 965
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Data wrangling and data visualization with the Tidyverse R data science package About This Video Minimal mathematical jargon. The course focuses on teaching you basic time series concepts and hands-on applications for the most important concepts in R People with no prior exposure to time series can use this course to get started A thorough grounding in how to use both statistical and machine learning techniques on time series data In Detail This is your roadmap to becoming highly proficient in data preprocessing, data wrangling, and data visualization using two of the most in-demand R data science packages What this course will do for you: It will take you from a basic level to a level where you'll perform some of the most common data wrangling tasks in R-with two of the most well-known R data science packages: Tidyverse and dplyr. It will equip you to use some of the most important R data wrangling and visualization packages such as dplyr and ggplot2. It will Introduce you, in a practical way, to some of the most important data visualization concepts so that you can apply them to practical data analysis and interpretation. You will also be able to decide which wrangling and visualization techniques are best suited to answering your research questions and most applicable to your data, so that you can interpret the results. The course will mostly focus on helping you implement different techniques on real-life data After each video, you will have learned a new concept or technique and will be able to apply it to your own projects immediately!

Modern Data Science With R

Author: Benjamin S. Baumer
Editor: CRC Press
ISBN: 1498724493
Size: 13,24 MB
Format: PDF, ePub
Read: 303
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Modern Data Science with R is a comprehensive data science textbook for undergraduates that incorporates statistical and computational thinking to solve real-world problems with data. 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 statistical questions. Contemporary data science requires a tight integration of knowledge from statistics, computer science, mathematics, and a domain of application. This book will help readers with some background in statistics and modest prior experience with coding develop and practice the appropriate skills to tackle complex data science projects. The book features a number of exercises and has a flexible organization conducive to teaching a variety of semester courses.

Mastering Scientific Computing With R

Author: Paul Gerrard
Editor: Packt Publishing Ltd
ISBN: 1783555262
Size: 15,29 MB
Format: PDF, Docs
Read: 214
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If you want to learn how to quantitatively answer scientific questions for practical purposes using the powerful R language and the open source R tool ecosystem, this book is ideal for you. It is ideally suited for scientists who understand scientific concepts, know a little R, and want to be able to start applying R to be able to answer empirical scientific questions. Some R exposure is helpful, but not compulsory.

R For Data Science

Author: Hadley Wickham
Editor: "O'Reilly Media, Inc."
ISBN: 1491910364
Size: 18,19 MB
Format: PDF, ePub, Docs
Read: 398
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"This book introduces you to R, RStudio, and the tidyverse, a collection of R packages designed to work together to make data science fast, fluent, and fun. Suitable for readers with no previous programming experience"--