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Über dieses Buch

This book serves as a hands-on guide to the "acs" R package for demographers, planners, and other researchers who work with American Community Survey (ACS) data. It gathers the most common problems associated with using ACS data and implements functions as a package in the R statistical programming language. The package defines a new "acs" class object (containing estimates, standard errors, and metadata for tables from the ACS) with methods to deal appropriately with common tasks (e.g., creating and combining subgroups or geographies, automatic fetching of data via the Census API, mathematical operations on estimates, tests of significance, plots of confidence intervals).

Inhaltsverzeichnis

Frontmatter

Chapter 1. The Dawn of the ACS: The Nature of Estimates

Abstract
Every 10 years, the U.S. Census Bureau undertakes a complete count of the country’s population, or at least attempts to do so; that’s what a census is. The information they gather is very limited: this is known as the Census “short form,” which consists of only six questions on sex, age, race, and household composition. This paper has nothing to do with that.
Ezra Haber Glenn

Chapter 2. Getting Started in R

Abstract
In recent years, the R statistical package has emerged as the leading open-source alternative to applications such as SPSS, Stata, and SAS. In some fields—notably biological modeling and econometrics—R is becoming more widely used than commercial competitors, due in large part to the open source development model which allows researchers to collaboratively design custom-built packages for niche applications. Unfortunately, one area of application that has not been as widely explored—despite the potential for fruitful development—is the use of R for demographic analysis. Prior to the current work, there were a few R packages to bridge the gap between GIS and statistical analysis [4]—and one contribution to help with the downloading and management of spatial data and associated datasets from the 2000 Census [3]—but no R packages existed to manage ACS data or address the types of issues raised above.
Ezra Haber Glenn

Chapter 3. Working with the New Functions

Abstract
We’ve tried to make this User Guide as detailed as possible, to help you learn about the many advanced features of the new package. As a result, it may look like there is a lot to learn, but in fact the basics are pretty simple: to get ACS data for your own user-defined geographies, all you need to do is:
Ezra Haber Glenn

Chapter 4. Exporting Data

Abstract
In the future, versions of the acs package will include improved export functions to allow users to save acs data in a variety of formats. For now, however, users wishing to export data for use in spreadsheets or other program can make use of the existing export functions, such as write.csv, along with the package’s estimate, standard.error, and confint functions. Thus, to save the estimates, standard errors, and a 90 % confidence interval as three different .csv spreadsheets:
Ezra Haber Glenn

Chapter 5. Additional Resources

Abstract
The acs package is hosted on the cran repository, where updates will appear from time to time. For additional guidance and examples, users are advised to review the complete documentation at (http://​cran.​r-project.​org/​web/​packages/​acs/​index.​html), which can also be accessed in an R session via the help function.
Ezra Haber Glenn

Backmatter

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