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Build the wide longitudinal file from per-wave surveys

Source: R/panel-design.R
panel_merge.Rd

Joins a named list of per-wave data.frames on a stable unit key into the wide, one-row-per-unit file that the longitudinal recipe consumes. Each wave's non-key columns are suffixed with the wave name, and per-wave presence indicators .wf_in_<wave> (and, if responded is given, response indicators .wf_resp_<wave>) are added so a later step_attrition() can model the response pattern.

Usage

panel_merge(
  waves,
  by,
  responded = NULL,
  require = c("any", "all"),
  suffix = "_"
)

Arguments

waves

a named list of per-wave data.frames; the names become the wave labels and column suffixes.

by

one or more column names (a character vector) forming the unit key, present in every wave – e.g. "ID" for a household or c("ID", "person_no") for a person.

responded

optional string: the name of a response indicator present in every wave, used to build .wf_resp_<wave>.

require

one of "any" (union of units, default) or "all" (intersection).

suffix

string inserted before the wave label when renaming columns (default "_"), e.g. age in wave T1 becomes age_T1.

Value

a wide data.frame, one row per unit, ready for panel_design() / weighting_spec().

Details

require = "any" (the default) keeps every unit seen in at least one wave – which the attrition model needs, because it has to be fitted over responders and non-responders. require = "all" keeps only the intersection (CEPAL's s(2) = s1 cap s2 cap ...); prefer to reach it with step_attrition() after estimating, not by dropping units before the model can see them.

See also

panel_design()

Examples

# reshape two waves of the long panel into one wide, one-row-per-unit file
wide <- panel_merge(
  list(T1 = subset(panel_ine, wave == 1), T2 = subset(panel_ine, wave == 2)),
  by = c("household_id", "person_no"), require = "any")
names(wide)                       # per-wave columns are suffixed _T1 / _T2
#>  [1] "household_id"       "person_no"          "person_id_T1"      
#>  [4] "stratum_T1"         "psu_T1"             "region_T1"         
#>  [7] "sex_T1"             "age_T1"             "wave_T1"           
#> [10] "rotation_group_T1"  "month_in_sample_T1" "pw_T1"             
#> [13] "disposition_T1"     "lf_status_T1"       "employed_T1"       
#> [16] "unemployed_T1"      "income_T1"          ".wf_in_T1"         
#> [19] "person_id_T2"       "stratum_T2"         "psu_T2"            
#> [22] "region_T2"          "sex_T2"             "age_T2"            
#> [25] "wave_T2"            "rotation_group_T2"  "month_in_sample_T2"
#> [28] "pw_T2"              "disposition_T2"     "lf_status_T2"      
#> [31] "employed_T2"        "unemployed_T2"      "income_T2"         
#> [34] ".wf_in_T2"