The urbnindicators Codebook

library(urbnindicators)
library(dplyr)
library(reactable)

Every dataframe returned by compile_acs_data() includes a codebook as an attribute. The codebook documents every variable in the dataset–what it represents, how it was calculated, and whether it is a raw ACS estimate or a derived measure.

Accessing the codebook

The codebook is stored as an attribute of the dataframe and can be retrieved with attr():

df = compile_acs_data(year = 2024, geography = "us")
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codebook = attr(df, "codebook")

Understanding the columns

The codebook has three columns:

• Variable: The variable name as it appears in the dataframe (e.g., snap_received_percent).

• Type: The kind of variable. Common types include:

  • count – a raw ACS estimate directly from the API
  • percent – a derived ratio, typically a count divided by a universe variable
  • metadata – a non-computed variable such as a median or geographic identifier

• Definition: A formula describing how the variable was calculated. For raw ACS variables, this is the original Census Bureau variable code (e.g., B22003_002). For derived variables, this is an expression like snap_received / snap_universe that shows the numerator and denominator. More complex definitions may reference multiple raw variables joined with + in the numerator or denominator.

These definition strings are also used internally to calculate margins of error for derived variables, so their accuracy is critical.

Browse the codebook

Use the search box below to filter by variable name, type, or definition text. Note that this codebook reflects all variables from the tables returned by list_tables(), but if you were to specify different tables in your compile_acs_data() call, your codebook would comprise different variable listings.