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Analyzing US Census Data in R

Working with margins of error in tidycensus

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Este exercício faz parte do curso

Analyzing US Census Data in R

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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.

Exercise 1: Census data in R: an overview Exercise 2: Obtain and set your Census API key Exercise 3: Getting Census data with tidycensus Exercise 4: Basic tidycensus functionality Exercise 5: Understanding tidycensus options Exercise 6: Tidy and wide data in tidycensus Exercise 7: Searching for data with tidycensus Exercise 8: Loading variables in tidycensus Exercise 9: Exploring variables with tidyverse tools Exercise 10: Visualizing Census data with ggplot2 Exercise 11: Comparing geographies with ggplot2 visualizations Exercise 12: Customizing ggplot2 visualizations of ACS 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.

Exercise 1: Tables and summary variables in tidycensus Exercise 2: Download and view a table of data from the ACS Exercise 3: Get a summary variable and calculate percentages Exercise 4: Census data wrangling with tidy tools Exercise 5: Finding the largest group by county Exercise 6: Recoding variables and calculating group sums Exercise 7: Comparing ACS estimates for multiple years Exercise 8: Working with margins of error in tidycensus

Exercício atual

Exercise 9: Inspecting margins of error Exercise 10: Using margin of error functions in tidycensus Exercise 11: Calculating group-wise margins of error Exercise 12: Visualizing margins of error from the ACS Exercise 13: Quick visual exploration of ACS margins of error Exercise 14: Customizing a ggplot2 margin of error plot

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

Exercise 1: Understanding census geography and tigris basics Exercise 2: Getting Census boundary files with tigris Exercise 3: Getting geographic features with tigris Exercise 4: Understanding the structure of tigris objects Exercise 5: Customizing tigris options Exercise 6: TIGER/Line and cartographic boundary files Exercise 7: Getting data as simple features objects Exercise 8: Setting a cache directory Exercise 9: Working with historic shapefiles Exercise 10: Combining and joining census geographic datasets Exercise 11: Combining datasets of the same tigris type Exercise 12: Getting data for multiple states Exercise 13: Joining data from an external data frame Exercise 14: Plotting data with tigris and ggplot2 Exercise 15: Plotting simple features with geom_sf()Exercise 16: Customizing geom_sf() plots

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.

Exercise 1: Simple feature geometry and tidycensus Exercise 2: Getting simple feature geometry Exercise 3: Joining data from tigris and tidycensus Exercise 4: Shifting Alaska and Hawaii geometry Exercise 5: Mapping demographic data with ggplot2 Exercise 6: Making a choropleth map Exercise 7: Modifying map colors Exercise 8: Customizing the map output Exercise 9: Advanced demographic mapping Exercise 10: Graduated symbol maps Exercise 11: Faceted maps with ggplot2 Exercise 12: Interactive visualization with mapview Exercise 13: Cartographic workflows with tigris and tidycensus Exercise 14: Generating random dots with sf Exercise 15: Obtaining data for cartography with tigris Exercise 16: Making a dot-density map with ggplot2 Exercise 17: Next steps for working with demographic data in R