scf (version 1.0.5)

scf_load: Load SCF Data as Multiply-Imputed Survey Designs

Description

Converts SCF .rds files prepared by scf_download() into scf_mi_survey objects. Each object wraps five implicates per year in replicate-weighted, multiply-imputed survey designs suitable for use with scf_ functions.

Converts SCF .rds files prepared by scf_download() into scf_mi_survey objects. Each object wraps five implicates per year in replicate-weighted, multiply-imputed survey designs suitable for use with scf_ functions.

Usage

scf_load(min_year, max_year = min_year, data_directory = ".")

Value

Invisibly returns a scf_mi_survey (or named list if multiple years). Attributes: mock (logical), year, n_households.

Invisibly returns a scf_mi_survey (or named list if multiple years). Attributes: mock (logical), year, n_households.

Arguments

min_year

Integer. First SCF year to load (1989–2022, divisible by 3).

max_year

Integer. Last SCF year to load. Defaults to min_year.

data_directory

Character. Directory containing .rds files or a full path to a single .rds file. Defaults to the current working directory ".". For examples and tests, use tempdir() to avoid leaving files behind.

Implementation

Provide a year or range and either (1) a directory containing scf<year>.rds files, or (2) a full path to a single .rds file. Files must contain five implicate data frames with columns wgt and wt1b1..wt1bK (typically K=999).

Provide a year or range and either (1) a directory containing scf<year>.rds files, or (2) a full path to a single .rds file. Files must contain five implicate data frames with columns wgt and wt1b1..wt1bK (typically K=999).

See Also

scf_download(), scf_design(), scf_update(), survey::svrepdesign()

Load SCF Data as Multiply-Imputed Survey Designs

scf_download(), scf_design(), scf_update(), survey::svrepdesign()

Examples

# Using with CRAN-compliant mock data:
# Use functions `scf_download()` and `scf_load()`
td <- tempfile("load_")
dir.create(td)

src <- system.file("extdata", "scf2022_mock_raw.rds", package = "scf")
file.copy(src, file.path(td, "scf2022.rds"), overwrite = TRUE)
scf2022 <- scf_load(2022, data_directory = td)

# Do not implement these lines in real analysis: Cleanup for package check
unlink(td, recursive = TRUE, force = TRUE)