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E-raamat: Working with the American Community Survey in R: A Guide to Using the acs Package

  • Formaat: PDF+DRM
  • Sari: SpringerBriefs in Statistics
  • Ilmumisaeg: 06-Oct-2016
  • Kirjastus: Springer International Publishing AG
  • Keel: eng
  • ISBN-13: 9783319457727
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Working with the American Community Survey in R: A Guide to Using the acs PackageSuurem pilt
  • Formaat: PDF+DRM
  • Sari: SpringerBriefs in Statistics
  • Ilmumisaeg: 06-Oct-2016
  • Kirjastus: Springer International Publishing AG
  • Keel: eng
  • ISBN-13: 9783319457727
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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).

PurposeKey FeaturesGetting Started in RWorking with FunctionsExporting DataAdditional Resources
1 The Dawn of the ACS: The Nature of Estimates
1(8)
1.1 Challenges of Estimates in General
2(2)
1.2 Challenges of Multi-Year Estimates in Particular
4(1)
1.3 Additional Issues in Using ACS Data
5(1)
1.4 Putting it All Together: A Brief Example
6(3)
2 Getting Started in R
9(6)
2.1 Introduction
9(1)
2.2 Getting and Installing R
10(1)
2.3 Getting and Installing the acs Package
10(3)
2.3.1 Installing from CRAN
10(2)
2.3.2 Installing from a Zipped Tarball
12(1)
2.4 Getting and Installing a Census API Key
13(2)
2.4.1 Using a Blank Key: An Informal Workaround
14(1)
3 Working with the New Functions
15(24)
3.1 Overview
15(1)
3.2 User-Specific Geographies
16(10)
3.2.1 Basic Building Blocks: The Single Element geo. set
16(1)
3.2.2 But Where's the Data...?
17(1)
3.2.3 Real geo.sets: Complex Groups and Combinations
17(3)
3.2.4 Changing combine and combine term
20(1)
3.2.5 Nested and Flat geo. sets
21(1)
3.2.6 Subsetting geo.sets
22(1)
3.2.7 Two Tools to Reduce Frustration in Selecting Geographies
23(3)
3.3 Getting Data
26(13)
3.3.1 acs fetch (): The Workhorse Function
26(4)
3.3.2 More Descriptive Variable Names: col.names=
30(1)
3.3.3 The acs lookup () Function: Finding the Variables You Want
31(8)
4 Exporting Data
39(2)
5 Additional Resources
41(2)
A A Worked Example Using Blockgroup-Level Data and Nested Combined geo.sets
43(10)
A.1 Making the geo set
43(2)
A.2 Using combine=T to Make a Neighborhood
45(1)
A.3 Even More Complex geo sets
46(1)
A.4 Gathering Neighborhood Data on Transit Mode-Share
47(6)
References 53
Ezra Haber Glenn, AICP, is Lecturer in the Housing, Community, and Economic Development Group of MIT's Department of Urban Studies and Planning, where he teaches on both community development practice and quantitative methods for planning. He has taught urban planning, politics, and GIS mapping as well at Tufts University and the University of Massachusetts at Lowell. Ezra is also the developer and maintainer of the "acs" package in the R Statistical Language, which helps users download and work with data from the American Community Survey in R.
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