Exploring and Visualizing US Census Data with R

Exploring and Visualizing US Census Data with R
Author: Eric Pimpler
Publisher:
Total Pages: 132
Release: 2019-10-25
Genre:
ISBN: 9781702556354

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In this book you will learn how to use R with the tidycensus and tidyverse packages to explore and visualize US Census data.tidycensus is an R package that allows users to interface with the US Census Bureau's decennial Census and five-year American Community APIs and return tidyverse-ready data frames, optionally with simple feature geometry included. tidycensus is designed to help R users get Census data that is pre-prepared for exploration within the tidyverse, and optionally spatially with the sf package.If your work involves the use of data from the US Census Bureau and would like to use R to explore, manipulate, and visualize these datasets, the tidycensus and tidyverse packages are great tools for accomplishing these tasks. Beyond this, the sf package now allows R users to work with spatial data in an integrated way with tidyverse tools, and updates to the tigris package provide access to Census boundary data as sf objects.This book will also allow the student to learn, in detail, the fundamentals of the R language and additionally master some of the most efficient libraries for data visualization in chart, graph, and map formats. The student will learn the language and applications through examples and practice. No prior programming skills are required.

Analyzing US Census Data

Analyzing US Census Data
Author: Kyle Walker
Publisher: CRC Press
Total Pages: 378
Release: 2023-02-09
Genre: Business & Economics
ISBN: 1351360302

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Census data are widely used by practitioners to understand demographic change, allocate resources, address inequalities, and make sound business decisions. Until recently, projects using US Census data have required proficiency with multiple web interfaces and software platforms to prepare, map, and present data products. This book introduces readers to tools in the R programming language for accessing and analyzing Census data, helping analysts manage these types of projects in a single computing environment. Chapters in this book cover the following key topics: • Rapidly acquiring data from the decennial US Census and American Community Survey using R, then analyzing these datasets using tidyverse tools; • Visualizing US Census data with a wide range of methods including charts in ggplot2 as well as both static and interactive maps; • Using R as a geographic information system (GIS) to manage, analyze, and model spatial demographic data from the US Census; • Working with and modeling individual-level microdata from the American Community Survey’s PUMS datasets; • Applying these tools and workflows to the analysis of historical Census data, other US government datasets, and international Census data from countries like Canada, Brazil, Kenya, and Mexico. Kyle Walker is an associate professor of geography at Texas Christian University, director of TCU’s Center for Urban Studies, and a spatial data science consultant. His research focuses on demographic trends in the United States, demographic data visualization, and software tools for open spatial data science. He is the lead author of a number of R packages including tigris, tidycensus, and mapboxapi.

Exploring the U.S. Census

Exploring the U.S. Census
Author: Frank Donnelly
Publisher: SAGE Publications
Total Pages: 562
Release: 2019-10-07
Genre: Social Science
ISBN: 1544355440

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The United States census provides researchers, students, and the public with some of the richest and broadest information available about the American people. Exploring the U.S. Census by Frank Donnelly gives social science students and researchers alike the tools to understand, extract, process, and analyze data from the decennial census, the American Community Survey, and other data collected by the U.S. Census Bureau. More than just a data collection exercise performed every ten years, the census is a series of datasets updated on an ongoing basis. With all that data comes opportunities and challenges: opportunities to teach students the value of census data for studying communities and answering research questions, and the challenges of navigating and comprehending such a massive data source and transforming it into usable information that students and researchers can analyze with basic skills and software. Just as important as showing what the census can tell social researchers is showing how to ask good questions of census data. Exploring the U.S. Census provides a thorough background on the data collection methods, structures, and potential pitfalls of the census for unfamiliar researchers, collecting information previously available only in widely disparate sources into one handy guide. Hands-on, applied exercises at the end of the chapters help readers dive into the data. The first chapter of the book places the census into context, discussing the history and the role of the census in society as well as in the larger universe of government, open, and big data. The book then moves onto the essentials of the data structure including the variety of sources and searching mechanisms, geography from nation down to zip code, and the fundamental subject categories (social, economic, and geographic) that are used for summarizing data in all of the various datasets. The next section delves into the individual datasets, discussing the purpose and structure of each, with separate chapters devoted to the decennial census, ACS, Population Estimates Program, and business datasets. A final chapter for this section pulls everything together, with a focus on writing and presenting your research on the data. The final section covers advanced topics and applications including mapping, geographic information systems, creating new variables and measures from census data, historical census data, and microdata. Along the way, the author shows how best to analyze census data with open-source software and tools, such as QGIS geographic information system, LibreOffice® Calc, and the DB Browser for SQLite®. Readers can freely evaluate the data on their own computers, in keeping with the free and open data provided by the Census Bureau. By placing the census in the context of the open data movement, this text makes the history and practice of the census relevant so readers can understand what a crucial resource the United States census is for research and knowledge.

Statistical Inference via Data Science: A ModernDive into R and the Tidyverse

Statistical Inference via Data Science: A ModernDive into R and the Tidyverse
Author: Chester Ismay
Publisher: CRC Press
Total Pages: 461
Release: 2019-12-23
Genre: Mathematics
ISBN: 1000763463

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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.

An Introduction to ATM Networks

An Introduction to ATM Networks
Author: Harry G. Perros
Publisher: Wiley
Total Pages: 272
Release: 2001-11-28
Genre: Technology & Engineering
ISBN: 9780471498278

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Asynchronous Transfer Mode (ATM) has revolutionized telecommunications, and has become an integral part of the networking infrastructure. This introductory well-structured text on ATM networks describes their development, architecture, congestion control, deployment, and signalling in an intuitive, accessible way. It covers extensive background information and includes exercises that support the explanations throughout the book. The networking expert Harry G. Perros explains ATM networks, including such hot topics as: * ATM adaptation layer 2 * Quality of Service * Congestion control * Tag switching and MPLS (Multi-Protocol Label Switching) * ADSL-based access networks * Signalling * PNNI (Private Network Node Interface) An Introduction to ATM Networks is a textbook for graduate students and undergraduates in electrical engineering and computer science as well as a reference work for networking engineers. An Online solutions Manual is now available.

Hands-On Data Visualization

Hands-On Data Visualization
Author: Jack Dougherty
Publisher: O'Reilly Media
Total Pages: 275
Release: 2021-04-30
Genre:
ISBN: 9781492086000

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Tell your story and show it with data, using free and easy-to-learn tools on the web. This introductory book teaches you how to design interactive charts and customized maps for your website, beginning with simple drag-and-drop tools such as Google Sheets, Datawrapper, and Tableau Public. You'll also gradually learn how to edit open source code templates like Chart.js, Highcharts, and Leaflet on GitHub. Hands-On Data Visualization for All takes you step-by-step through tutorials, real-world examples, and online resources. This hands-on resource is ideal for students, nonprofit organizations, small business owners, local governments, journalists, academics, and anyone who wants to take data out of spreadsheets and turn it into lively interactive stories. No coding experience is required. Build interactive charts and maps and embed them in your website Understand the principles for designing effective charts and maps Learn key data visualization concepts to help you choose the right tools Convert and transform tabular and spatial data to tell your data story Edit and host Chart.js, Highcharts, and Leaflet map code templates on GitHub Learn how to detect bias in charts and maps produced by others

Visualizing Data

Visualizing Data
Author: Ben Fry
Publisher: "O'Reilly Media, Inc."
Total Pages: 384
Release: 2008
Genre: Computers
ISBN: 0596519303

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Provides information on the methods of visualizing data on the Web, along with example projects and code.

Modern Data Science with R

Modern Data Science with R
Author: Benjamin S. Baumer
Publisher: CRC Press
Total Pages: 830
Release: 2021-03-31
Genre: Business & Economics
ISBN: 0429575394

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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.

Using R for Introductory Statistics

Using R for Introductory Statistics
Author: John Verzani
Publisher: CRC Press
Total Pages: 522
Release: 2018-10-03
Genre: Computers
ISBN: 1315360306

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The second edition of a bestselling textbook, Using R for Introductory Statistics guides students through the basics of R, helping them overcome the sometimes steep learning curve. The author does this by breaking the material down into small, task-oriented steps. The second edition maintains the features that made the first edition so popular, while updating data, examples, and changes to R in line with the current version. See What’s New in the Second Edition: Increased emphasis on more idiomatic R provides a grounding in the functionality of base R. Discussions of the use of RStudio helps new R users avoid as many pitfalls as possible. Use of knitr package makes code easier to read and therefore easier to reason about. Additional information on computer-intensive approaches motivates the traditional approach. Updated examples and data make the information current and topical. The book has an accompanying package, UsingR, available from CRAN, R’s repository of user-contributed packages. The package contains the data sets mentioned in the text (data(package="UsingR")), answers to selected problems (answers()), a few demonstrations (demo()), the errata (errata()), and sample code from the text. The topics of this text line up closely with traditional teaching progression; however, the book also highlights computer-intensive approaches to motivate the more traditional approach. The authors emphasize realistic data and examples and rely on visualization techniques to gather insight. They introduce statistics and R seamlessly, giving students the tools they need to use R and the information they need to navigate the sometimes complex world of statistical computing.

Data Mashups in R

Data Mashups in R
Author: Jeremy Leipzig
Publisher: "O'Reilly Media, Inc."
Total Pages: 40
Release: 2011-03-04
Genre: Computers
ISBN: 9781449307257

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How do you use R to import, manage, visualize, and analyze real-world data? With this short, hands-on tutorial, you learn how to collect online data, massage it into a reasonable form, and work with it using R facilities to interact with web servers, parse HTML and XML, and more. Rather than use canned sample data, you'll plot and analyze current home foreclosure auctions in Philadelphia. This practical mashup exercise shows you how to access spatial data in several formats locally and over the Web to produce a map of home foreclosures. It's an excellent way to explore how the R environment works with R packages and performs statistical analysis. Parse messy data from public foreclosure auction postings Plot the data using R's PBSmapping package Import US Census data to add context to foreclosure data Use R's lattice and latticeExtra packages for data visualization Create multidimensional correlation graphs with the pairs() scatterplot matrix package