LibGuide for R

Learn how to use R to turn raw data into insight, knowledge, and understanding. 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, R for Data Science is designed to get you doing data science as quickly as possible. Authors Hadley Wickham and Garrett Grolemund guide you through the steps of importing, wrangling, exploring, and modeling your data and communicating the results. You'll get a complete, big-picture understanding of the data science cycle, along with basic tools you need to manage the details. Each section of the book is paired with exercises to help you practice what you've learned along the way. You'll learn how to: Wrangle--transform your datasets into a form convenient for analysis Program--learn powerful R tools for solving data problems with greater clarity and ease Explore--examine your data, generate hypotheses, and quickly test them Model--provide a low-dimensional summary that captures true "signals" in your dataset Communicate--learn R Markdown for integrating prose, code, and results

Learn the essence of data science and visualization using R in no time at allAbout This Book* Become a pro at making stunning visualizations and dashboards quickly and without hassle* For better decision making in business, apply the R programming language with the help of useful statistical techniques.* From seasoned authors comes a book that offers you a plethora of fast-paced techniques to detect and analyze data patternsWho This Book Is ForIf you are an aspiring data scientist or analyst who has a basic understanding of data science and has basic hands-on experience in R or any other analytics tool, then R Data Science Essentials is the book for you.What You Will Learn* Perform data preprocessing and basic operations on data* Implement visual and non-visual implementation data exploration techniques* Mine patterns from data using affinity and sequential analysis* Use different clustering algorithms and visualize them* Implement logistic and linear regression and find out how to evaluate and improve the performance of an algorithm* Extract patterns through visualization and build a forecasting algorithm* Build a recommendation engine using different collaborative filtering algorithms* Make a stunning visualization and dashboard using ggplot and R shinyIn DetailWith organizations increasingly embedding data science across their enterprise and with management becoming more data-driven it is an urgent requirement for analysts and managers to understand the key concept of data science. The data science concepts discussed in this book will help you make key decisions and solve the complex problems you will inevitably face in this new world.R Data Science Essentials will introduce you to various important concepts in the field of data science using R. We start by reading data from multiple sources, then move on to processing the data, extracting hidden patterns, building predictive and forecasting models, building a recommendation engine, and communicating to the user through stunning visualizations and dashboards.By the end of this book, you will have an understanding of some very important techniques in data science, be able to implement them using R, understand and interpret the outcomes, and know how they helps businesses make a decision.Style and approachThis easy-to-follow guide contains hands-on examples of the concepts of data science using R.

Key Features Book Description What you will learn Generate various plots in R using the basic R plotting techniques Utilize R packages to add context and meaning to your data Design interactive visualizations and integrate them on your website or blog Communicate using visualization techniques, optimal for the underlying data being used as input Create presentations and learn the basics of creating apps in R for your audience Introduce users to basic R functions and data manipulation techniques while creating meaningful visualizations Add elements, text, animation, and colors to your plot to make sense of data Who this book is for If you are a data journalist, academician, student or freelance designer who wants to learn about data visualization, this book is for you. Basic knowledge of R programming is expected.

Translate your data into info-graphics using popular packages in RAbout This Book* Use R's popular packages--such as ggplot2, ggvis, ggforce, and more--to create custom, interactive visualization solutions.* Create, design, and build interactive dashboards using Shiny* A highly practical guide to help you get to grips with the basics of data visualization techniques, and how you can implement them using RWho This Book Is ForIf you are looking to create custom data visualization solutions using the R programming language and are stuck somewhere in the process, this book will come to your rescue. Prior exposure to packages such as ggplot2 would be useful but not necessary. However, some R programming knowledge is required.What You Will Learn* Get to know various data visualization libraries available in R to represent data* Generate elegant codes to craft graphics using ggplot2, ggvis and plotly* Add elements, text, animation, and colors to your plot to make sense of data* Deepen your knowledge by adding bar-charts, scatterplots, and time series plots using ggplot2* Build interactive dashboards using Shiny.* Color specific map regions based on the values of a variable in your data frame* Create high-quality journal-publishable scatterplots* Create and design various three-dimensional and multivariate plotsIn DetailR 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.Style and approachThis book is packed with practical recipes, designed to provide you with all the guidance needed to get to grips with data visualization with R. You will learn to leverage the power of R and ggplot2 to create highly customizable data visualizations of varying complexities. The readers will then learn how to create, design, and build interactive dashboards using Shiny.

Summary: This book is ideal for those who are already exposed to R, but have not yet used it extensively for data analytics and are seeking to get up and running quickly for analytics tasks. This book will help people who aspire to enhance their skills in any of the following ways: perform advanced analyses and create informative and professional charts become proficient in acquiring data from many sources apply supervised and unsupervised data mining techniques use R's features to present analyses professionally.

This book seeks to teach undergraduate and graduate students in social sciences how to use R to manage, visualize, and analyze data in order to answer substantive questions and replicate published findings. This book distinguishes itself from other introductory R or statistics books in three ways. First, targeting an audience rarely exposed to statistical programming, it adopts a minimalist approach and covers only the most important functions and skills in R that one will need for conducting reproducible research projects. Second, it emphasizes meeting the practical needs of students using R in research projects. Specifically, it teaches students how to import, inspect, and manage data; understand the logic of statistical inference; visualize data and findings via histograms, boxplots, scatterplots, and diagnostic plots; and analyze data using one-sample t-test, difference-of-means test, covariance, correlation, ordinary least squares (OLS) regression, and model assumption diagnostics. Third, it teaches students how to replicate the findings in published journal articles and diagnose model assumption violations. The principle behind this book is to teach students to learn as little R as possible but to do as much reproducible, substance-driven data analysis at the beginner or intermediate level as possible. The minimalist approach dramatically reduces the learning cost but still proves adequate information for meeting the practical research needs of senior undergraduate and beginning graduate students. Having completed this book, students can use R and statistical analysis to answer questions regarding some substantively interesting continuous outcome variable in a cross-sectional design.

If you are a data analyst who has a firm grip on some advanced data analysis techniques and wants to learn how to leverage the features of R, this is the book for you. You should have some basic knowledge of the R language and should know about some data science topics.

Mine valuable insights from your data using popular tools and techniques in RAbout This Book* Understand the basics of data mining and why R is a perfect tool for it.* Manipulate your data using popular R packages such as ggplot2, dplyr, and so on to gather valuable business insights from it.* Apply effective data mining models to perform regression and classification tasks.Who This Book Is ForIf you are a budding data scientist, or a data analyst with a basic knowledge of R, and want to get into the intricacies of data mining in a practical manner, this is the book for you. No previous experience of data mining is required.What You Will Learn* Master relevant packages such as dplyr, ggplot2 and so on for data mining* Learn how to effectively organize a data mining project through the CRISP-DM methodology* Implement data cleaning and validation tasks to get your data ready for data mining activities* Execute Exploratory Data Analysis both the numerical and the graphical way* Develop simple and multiple regression models along with logistic regression* Apply basic ensemble learning techniques to join together results from different data mining models* Perform text mining analysis from unstructured pdf files and textual data* Produce reports to effectively communicate objectives, methods, and insights of your analysesIn DetailR is widely used to leverage data mining techniques across many different industries, including finance, medicine, scientific research, and more. This book will empower you to produce and present impressive analyses from data, by selecting and implementing the appropriate data mining techniques in R.It will let you gain these powerful skills while immersing in a one of a kind data mining crime case, where you will be requested to help resolving a real fraud case affecting a commercial company, by the mean of both basic and advanced data mining techniques.While moving along the plot of the story you will effectively learn and practice on real data the various R packages commonly employed for this kind of tasks. You will also get the chance of apply some of the most popular and effective data mining models and algos, from the basic multiple linear regression to the most advanced Support Vector Machines. Unlike other data mining learning instruments, this book will effectively expose you the theory behind these models, their relevant assumptions and when they can be applied to the data you are facing. By the end of the book you will hold a new and powerful toolbox of instruments, exactly knowing when and how to employ each of them to solve your data mining problems and get the most out of your data.Finally, to let you maximize the exposure to the concepts described and the learning process, the book comes packed with a reproducible bundle of commented R scripts and a practical set of data mining models cheat sheets.Style and approachThis book takes a practical, step-by-step approach to explain the concepts of data mining. Practical use-cases involving real-world datasets are used throughout the book to clearly explain theoretical concepts.

Data analysis is crucial to accurately predict the performance of an application. The book begins by getting you started with R, including basic programming and data import, data visualization, pivoting, merging, aggregating, and joins. Once you are comfortable with the basics, you can read ahead and learn all about data visualization and ...

Think about your data intelligently and ask the right questions Key Features Master data cleaning techniques necessary to perform real-world data science and machine learning tasks Spot common problems with dirty data and develop flexible solutions from first principles Test and refine your newly acquired skills through detailed exercises at the end of each chapter Book Description Data cleaning is the all-important first step to successful data science, data analysis, and machine learning. If you work with any kind of data, this book is your go-to resource, arming you with the insights and heuristics experienced data scientists had to learn the hard way. In a light-hearted and engaging exploration of different tools, techniques, and datasets real and fictitious, Python veteran David Mertz teaches you the ins and outs of data preparation and the essential questions you should be asking of every piece of data you work with. Using a mixture of Python, R, and common command-line tools, Cleaning Data for Effective Data Science follows the data cleaning pipeline from start to end, focusing on helping you understand the principles underlying each step of the process. You'll look at data ingestion of a vast range of tabular, hierarchical, and other data formats, impute missing values, detect unreliable data and statistical anomalies, and generate synthetic features. The long-form exercises at the end of each chapter let you get hands-on with the skills you've acquired along the way, also providing a valuable resource for academic courses. What you will learn Ingest and work with common data formats like JSON, CSV, SQL and NoSQL databases, PDF, and binary serialized data structures Understand how and why we use tools such as pandas, SciPy, scikit-learn, Tidyverse, and Bash Apply useful rules and heuristics for assessing data quality and detecting bias, like Benford's law and the 68-95-99.7 rule Identify and handle unreliable data and outliers, examining z-score and other statistical properties Impute sensible values into missing data and use sampling to fix imbalances Use dimensionality reduction, quantization, one-hot encoding, and other feature engineering techniques to draw out patterns in your data Work carefully with time series data, performing de-trending and interpolation Who this book is for This book is designed to benefit software developers, data scientists, aspiring data scientists, teachers, and students who work with data. If you want to improve your rigor in data hygiene or are looking for a refresher, this book is for you. Basic familiarity with statistics, general concepts in machine learning, knowledge of a programming language (Python or R), and some exposure to data science are helpful.

Hands-On Exploratory Data Analysis with R puts the complete process of exploratory data analysis into a practical demonstration in one nutshell. You will understand the concepts of data analysis right from data ingestion, data cleaning, data manipulation to applying statistical techniques and visualizing hidden patterns.