Analyzing Census Data with Data Commons

Learning Objectives

By the end of this vignette, you will be able to:

• Understand what Google Data Commons is and why it offers unique value for data integration
• Navigate the knowledge graph structure using relationships between geographic entities
• Find and use DCIDs (Data Commons IDs) and statistical variables
• Retrieve demographic data across different geographic levels using the datacommons R package
• Integrate data from multiple sources through the unified Data Commons API
• Handle multiple data sources (facets) and select appropriate ones for your analysis
• Build progressively complex demographic analyses that combine disparate datasets

Why Start with Census Data?

Census data provides an ideal introduction to Data Commons for several reasons:

1. Familiar territory: Many analysts have worked with census data, making it easier to focus on learning the Data Commons approach rather than the data itself.

2. Rich relationships: Census data showcases the power of the knowledge graph through natural hierarchies (country → state → county → city), demonstrating how Data Commons connects entities.

3. Integration opportunities: Census demographics become even more valuable when combined with health, environmental, and economic data—showing the true power of Data Commons.

4. Real-world relevance: The examples we’ll explore address actual policy questions that require integrated data to answer properly.

Why Data Commons?

The R ecosystem has excellent packages for specific data sources. For example, the tidycensus package provides fantastic functionality for working with U.S. Census data, with deep dataset-specific features and conveniences.

So why use Data Commons? The real value is in data integration.

Data Commons is part of Google’s philanthropic initiatives, designed to democratize access to public data by combining datasets from organizations like the UN, World Bank, and U.S. Census into a unified knowledge graph.

Imagine you’re a policy analyst studying the social determinants of health. You need to analyze relationships between:

• Census demographics (population, age, income)
• CDC health statistics (disease prevalence, obesity rates)
  
• EPA environmental data (air quality indices)
• Bureau of Labor Statistics (unemployment rates)

With traditional approaches, you’d need to:

1. Learn multiple different APIs
2. Deal with different geographic coding systems
   
3. Reconcile different time periods and update cycles
4. Match entities across datasets (is “Los Angeles County” the same in all datasets?)

Data Commons solves this by providing a unified knowledge graph that links all these datasets together. One API, one set of geographic identifiers, one consistent way to access everything. The datacommons R package is your gateway to this integrated data ecosystem, enabling reproducible analysis pipelines that seamlessly combine diverse data sources.

Understanding the Knowledge Graph

Data Commons organizes information as a graph, similar to how web pages link to each other. Here’s the key terminology:

DCIDs (Data Commons IDs)

Every entity in Data Commons has a unique identifier called a DCID. Think of it like a social security number for data:

• country/USA = United States
• geoId/06 = California (using FIPS code 06—Federal Information Processing Standards codes are essentially ZIP codes for governments, providing standard numeric identifiers for states, counties, and other geographic areas)
  
• Count_Person = the statistical variable for population count

Relationships

Entities are connected by relationships, following the Schema.org standard—a collaborative effort to create structured data vocabularies that help machines understand web content. For Data Commons, this means consistent, machine-readable relationships between places and data:

• California –(containedInPlace)–> United States
• California –(typeOf)–> State
• Los Angeles County –(containedInPlace)–> California

This structure lets us traverse the graph to find related information. Want all counties in California? Follow the containedInPlace relationships backward.

Statistical Variables

These are the things we can measure:

• Count_Person = total population
• Median_Age_Person = median age
• UnemploymentRate_Person = unemployment rate
• Mean_Temperature = average temperature

The power comes from being able to query any variable for any place using the same consistent approach through the datacommons R package.

Prerequisites

# Core packages
library(datacommons)
library(dplyr)
library(tidyr)
library(stringr)
library(ggplot2)
library(scales)
library(knitr)

# Set a consistent theme for plots
theme_set(theme_minimal())

Setting Up API Access

You’ll need a free API key from https://apikeys.datacommons.org/

# Set your API key
dc_set_api_key("YOUR_API_KEY_HERE")

# Or manually set DATACOMMONS_API_KEY in your .Renviron file

Don’t forget to restart your R session after setting the key to automatically load it.