Examples#

Basic Usage#

Getting Population Estimates#

using CensusACS

# Get total population for all states using 5-year estimates
df = get_acs(
    variables = ["B01003_001E"],
    geography = "state"
)

# Get population and median household income
df = get_acs(
    variables = ["B01003_001E", "B19013_001E"],
    geography = "state"
)

Working with Margins of Error#

# Get MOE for total population
df_moe = get_acs_moe(
    variables = ["B01003_001M"],
    geography = "state"
)

Geographic Levels#

State Level#

# Get state-level data
df = get_acs(
    variables = ["B01003_001E"],
    geography = "state"
)

County Level#

# Get all counties in California
df = get_acs(
    variables = ["B01003_001E"],
    geography = "county",
    state = "CA"
)

Census Tract Level#

# Get all tracts in a specific county
df = get_acs(
    variables = ["B01003_001E"],
    geography = "tract",
    state = "CA",
    county = "001"  # Alameda County
)

Survey Types#

1-Year Estimates#

# Get 1-year estimates (65,000+ population areas only)
df = get_acs(
    variables = ["B01003_001E"],
    geography = "state",
    survey = "acs1"
)

3-Year Estimates#

# Get 3-year estimates from 2013 (20,000+ population areas)
df = get_acs(
    variables = ["B01003_001E"],
    geography = "state",
    year = 2013,
    survey = "acs3"
)

5-Year Estimates#

# Get 5-year estimates (all areas)
df = get_acs(
    variables = ["B01003_001E"],
    geography = "state",
    survey = "acs5"
)

Error Handling#

The package includes robust error handling for common scenarios:

# Invalid year for 3-year estimates
try
    df = get_acs(
        variables = ["B01003_001E"],
        geography = "state",
        year = 2020,
        survey = "acs3"
    )
catch e
    println("Error: ", e)
end

# Invalid geography level
try
    df = get_acs(
        variables = ["B01003_001E"],
        geography = "invalid"
    )
catch e
    println("Error: ", e)
end