Convert to PIP format and add class

Convert welfare, weight and (optionally) imputed id vectors to PIP format from a data.frame

Usage

as_pip(
  dt,
  welfare_var,
  weight_var,
  imputation_id_var = NULL,
  pip_type = NULL,
  groupdata_threshold = getOption("pipster.gd_threshold"),
  verbose = getOption("pipster.verbose")
)

Arguments

dt

data.frame with welfare data

welfare_var

character: variable name of welfare vector in dt

weight_var

character: variable name of weight vector in dt

imputation_id_var

character: variable name of imputation ID vector in dt

pip_type

character: One of "md", "id", "gd_*". Generally this comes from the output of identify_pip_type()

groupdata_threshold

numeric: threshold to discriminate between micro data an group data. Default is 200 observations

verbose

logical: Whether to display important messages about your data or query

Value

data.frame

Examples

# Example 1: Basic usage with md data.
as_pip(dt = pip_md,
       welfare_var = "welfare",
       weight_var = "weight") |>
class()
#> ! vectors not sorted
#> → PIP type identified: "md"
#> [1] "pipmd"      "data.table" "data.frame"

# Example 2: Including imputation_id_var
as_pip(dt = pip_id,
       welfare_var = "welfare",
       weight_var = "weight",
       imputation_id_var = "imputation_id") |>
class()
#> ! vectors not sorted
#> → PIP type identified: "id"
#> [1] "pipid"      "pipmd"      "data.table" "data.frame"

# Example 3: Basic usage with gd data and explicit pip_type
as_pip(dt = pip_gd,
       welfare_var = "L",
       weight_var = "P",
       pip_type = "gd_1") |>
class()
#> Warning: replacing previous import ‘collapse::fdroplevels’ by ‘data.table::fdroplevels’ when loading ‘wbpip’
#> ℹ columns "welfare" and "P" have been rescaled to range (0,1]
#> [1] "pipgd"      "data.table" "data.frame"