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Analyzing US Census Data in R

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更新日 2023/03

Learn to rapidly visualize and explore demographic data from the United States Census Bureau using tidyverse tools.

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RExploratory Data Analysis4時間17 ビデオ59 演習5,050 XP5,349達成証明書

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コース説明

Analysts across industries rely on data from the United States Census Bureau in their work. In this course, students will learn how to work with Census tabular and spatial data in the R environment. The course focuses on the tidycensus package for acquiring data from the decennial US Census and American Community survey in a tidyverse-friendly format, and the tigris package for accessing Census geographic data within R. By the end of this course, students will be able to rapidly visualize and explore demographic data from the Census Bureau using ggplot2 and other tidyverse tools, and make maps of Census demographic data with only a few lines of R code.

前提条件

Introduction to the Tidyverse Visualizing Geospatial Data in R

1

Census data in R with tidycensus

In this chapter, students will learn the basics of working with Census data in R with tidycensus. They will acquire data using tidycensus functions, search for data, and make a basic plot.

Census data in R: an overview

Obtain and set your Census API key

Getting Census data with tidycensus

Basic tidycensus functionality

Understanding tidycensus options

Tidy and wide data in tidycensus

Searching for data with tidycensus

Loading variables in tidycensus

Exploring variables with tidyverse tools

Visualizing Census data with ggplot2

Comparing geographies with ggplot2 visualizations

Customizing ggplot2 visualizations of ACS data

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2

Wrangling US Census Data

In this chapter, students learn how to use tidyverse tools to wrangle data from the US Census and American Community Survey. They also learn about handling margins of error in the ACS.

Tables and summary variables in tidycensus

Download and view a table of data from the ACS

Get a summary variable and calculate percentages

Census data wrangling with tidy tools

Finding the largest group by county

Recoding variables and calculating group sums

Comparing ACS estimates for multiple years

Working with margins of error in tidycensus

Inspecting margins of error

Using margin of error functions in tidycensus

Calculating group-wise margins of error

Visualizing margins of error from the ACS

Quick visual exploration of ACS margins of error

Customizing a ggplot2 margin of error plot

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3

US Census geographic data in R

In this chapter, students will learn how to work with US Census Bureau geographic data in R using the tigris R package.

Understanding census geography and tigris basics

Getting Census boundary files with tigris

Getting geographic features with tigris

Understanding the structure of tigris objects

Customizing tigris options

TIGER/Line and cartographic boundary files

Getting data as simple features objects

Setting a cache directory

Working with historic shapefiles

Combining and joining census geographic datasets

Combining datasets of the same tigris type

Getting data for multiple states

Joining data from an external data frame

Plotting data with tigris and ggplot2

Plotting simple features with geom_sf()

Customizing geom_sf() plots

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4

Mapping US Census Data

In this chapter, you will learn how to obtain feature geometry with the tidycensus package, and use ggplot2 and mapview to make customized static and interactive maps of US Census data.

Simple feature geometry and tidycensus

Getting simple feature geometry

Joining data from tigris and tidycensus

Shifting Alaska and Hawaii geometry

Mapping demographic data with ggplot2

Making a choropleth map

Modifying map colors

Customizing the map output

Advanced demographic mapping

Graduated symbol maps

Faceted maps with ggplot2

Interactive visualization with mapview

Cartographic workflows with tigris and tidycensus

Generating random dots with sf

Obtaining data for cartography with tigris

Making a dot-density map with ggplot2

Next steps for working with demographic data in R

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Analyzing US Census Data in R

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