Preperation of LISS data

Goal

Set up

#load packages
library(dplyr)
library(purrr)
library(tidyr)
library(tidyselect)
library(haven)
library(magrittr)

Import the merged datafiles

load(file = file.path("data",
          "data-processed",
          "liss_merged",
       "liss_merged_raw.Rdata"))

Filter (participated in LISS module)

We can use the month of datacollection to see whether someone has participated in a specific year/module combination if someone has an an NA they did not participate in it. To surmise, we want people who participated in the CDN module and also in the politics and values waves.

# Let's create a filter of people who participated in at least one of these.

#first create a leisure and integration NA variable.
#first create a subset of the data, so we can use rowwise to create a selection id.
subset1 <- liss %>%
  rowwise() %>%
  #create filter variable
  mutate(na_lei_int = sum(is.na(
    c(
      cs08a_m,
      cs09b_m,
      cs10c_m,
      cs11d_m,
      cs12e_m,
      cs13f_m,
      cs14g_m,
      cs15h_m,
      cs16i_m,
      cs17j_m,
      cs18k_m
    )
  ))) %>%
  #use only the observations that have participated in at least one wave of the leisure and integration data. We can of course change this later on.
  filter(na_lei_int < 11) %>%
  ungroup()

#we go from 25306 cases to 14473 cases.

#second create a pol val NA variable.
#filter out respondents who did not participate in any of the 11 waves.
liss_subset <- subset1 %>%
  rowwise() %>%
  mutate(na_pol_val = sum(is.na(
    c(
      cv08a_m,
      cv09b_m,
      cv10c_m,
      cv11d_m,
      cv12e_m,
      cv13f_m,
      cv14g_m,
      cv17i_m,
      cv18j_m1,
      cv18j_m2,
      cv18j_m3,
      cv19k_m1,
      cv19k_m2,
      cv19k_m3
    )
  ))) %>%
  filter(na_pol_val < 14) %>% #now it is 14, due the weird coding in 18/19.
  ungroup()

#we go from 14473 cases to 13273 cases. We will work with this liss_subset dataframe. 

Variable selection

Goal: create subset of all the data that I want for every wave. Then we can make a new file where we only have the selected variables that we need for our analyses.

Politics and Values

#liss politics variables that I need
liss_pol <- liss_subset %>%
  select(
    nomem_encr,
    cv08a012,
    cv08a047,
    cv08a048,
    cv08a049,
    cv08a050,
    cv08a051,
    cv08a052,
    cv08a058,
    cv08a101,
    cv08a102,
    cv08a103,
    cv08a104,
    cv08a105,
    cv08a001,
    cv08a044,
    cv08a014,
    cv08a106,
    cv08a107,
    cv08a108,
    cv08a109,
    cv08a110,
    cv08a111,
    cv08a112,
    cv08a113,
    cv08a114,
    cv08a115,
    cv08a116,
    cv08a117,
    cv08a118,
    cv08a119,
    cv08a120,
    cv08a121,
    cv08a122,
    cv08a123,
    cv09b012,
    cv09b047,
    cv09b048,
    cv09b049,
    cv09b050,
    cv09b051,
    cv09b052,
    cv09b058,
    cv09b101,
    cv09b102,
    cv09b103,
    cv09b104,
    cv09b105,
    cv09b001,
    cv09b044,
    cv09b014,
    cv09b106,
    cv09b107,
    cv09b108,
    cv09b109,
    cv09b110,
    cv09b111,
    cv09b112,
    cv09b113,
    cv09b114,
    cv09b115,
    cv09b116,
    cv09b117,
    cv09b118,
    cv09b119,
    cv09b120,
    cv09b121,
    cv09b122,
    cv09b123,
    cv10c012,
    cv10c047,
    cv10c048,
    cv10c049,
    cv10c050,
    cv10c051,
    cv10c052,
    cv10c058,
    cv10c101,
    cv10c102,
    cv10c103,
    cv10c104,
    cv10c105,
    cv10c001,
    cv10c044,
    cv10c014,
    cv10c106,
    cv10c107,
    cv10c108,
    cv10c109,
    cv10c110,
    cv10c111,
    cv10c112,
    cv10c113,
    cv10c114,
    cv10c115,
    cv10c116,
    cv10c117,
    cv10c118,
    cv10c119,
    cv10c120,
    cv10c121,
    cv10c122,
    cv10c123,
    cv11d012,
    cv11d047,
    cv11d048,
    cv11d049,
    cv11d050,
    cv11d051,
    cv11d052,
    cv11d171,
    cv11d101,
    cv11d102,
    cv11d103,
    cv11d104,
    cv11d105,
    cv11d001,
    cv11d044,
    cv11d014,
    cv11d106,
    cv11d107,
    cv11d108,
    cv11d109,
    cv11d110,
    cv11d111,
    cv11d112,
    cv11d113,
    cv11d114,
    cv11d115,
    cv11d116,
    cv11d117,
    cv11d118,
    cv11d119,
    cv11d120,
    cv11d121,
    cv11d122,
    cv11d123,
    cv12e012,
    cv12e047,
    cv12e048,
    cv12e049,
    cv12e050,
    cv12e051,
    cv12e052,
    cv12e171,
    cv12e101,
    cv12e102,
    cv12e103,
    cv12e104,
    cv12e105,
    cv12e001,
    cv12e044,
    cv12e014,
    cv12e106,
    cv12e107,
    cv12e108,
    cv12e109,
    cv12e110,
    cv12e111,
    cv12e112,
    cv12e113,
    cv12e114,
    cv12e115,
    cv12e116,
    cv12e117,
    cv12e118,
    cv12e119,
    cv12e120,
    cv12e121,
    cv12e122,
    cv12e123,
    cv13f012,
    cv13f047,
    cv13f048,
    cv13f049,
    cv13f050,
    cv13f051,
    cv13f052,
    cv13f209,
    cv13f101,
    cv13f102,
    cv13f103,
    cv13f104,
    cv13f105,
    cv13f001,
    cv13f044,
    cv13f014,
    cv13f106,
    cv13f107,
    cv13f108,
    cv13f109,
    cv13f110,
    cv13f111,
    cv13f112,
    cv13f113,
    cv13f114,
    cv13f115,
    cv13f116,
    cv13f117,
    cv13f118,
    cv13f119,
    cv13f120,
    cv13f121,
    cv13f122,
    cv13f123,
    cv14g012,
    cv14g047,
    cv14g048,
    cv14g049,
    cv14g050,
    cv14g051,
    cv14g052,
    cv14g209,
    cv14g101,
    cv14g102,
    cv14g103,
    cv14g104,
    cv14g105,
    cv14g001,
    cv14g044,
    cv14g014,
    cv14g106,
    cv14g107,
    cv14g108,
    cv14g109,
    cv14g110,
    cv14g111,
    cv14g112,
    cv14g113,
    cv14g114,
    cv14g115,
    cv14g116,
    cv14g117,
    cv14g118,
    cv14g119,
    cv14g120,
    cv14g121,
    cv14g122,
    cv14g123,
    cv16h012,
    cv16h047,
    cv16h048,
    cv16h049,
    cv16h050,
    cv16h051,
    cv16h052,
    cv16h209,
    cv16h101,
    cv16h102,
    cv16h103,
    cv16h104,
    cv16h105,
    cv16h001,
    cv16h044,
    cv16h014,
    cv16h106,
    cv16h107,
    cv16h108,
    cv16h109,
    cv16h110,
    cv16h111,
    cv16h112,
    cv16h113,
    cv16h114,
    cv16h115,
    cv16h116,
    cv16h117,
    cv16h118,
    cv16h119,
    cv16h120,
    cv16h121,
    cv16h122,
    cv16h123,
    cv17i012,
    cv17i047,
    cv17i048,
    cv17i049,
    cv17i050,
    cv17i051,
    cv17i052,
    cv17i244,
    cv17i101,
    cv17i102,
    cv17i103,
    cv17i104,
    cv17i105,
    cv17i001,
    cv17i044,
    cv17i014,
    cv17i106,
    cv17i107,
    cv17i108,
    cv17i109,
    cv17i110,
    cv17i111,
    cv17i112,
    cv17i113,
    cv17i114,
    cv17i115,
    cv17i116,
    cv17i117,
    cv17i118,
    cv17i119,
    cv17i120,
    cv17i121,
    cv17i122,
    cv17i123,
    cv18j012,
    cv18j047,
    cv18j048,
    cv18j049,
    cv18j050,
    cv18j051,
    cv18j052,
    cv18j243,
    cv18j101,
    cv18j102,
    cv18j103,
    cv18j104,
    cv18j105,
    cv18j001,
    cv18j044,
    cv18j014,
    cv18j106,
    cv18j107,
    cv18j108,
    cv18j109,
    cv18j110,
    cv18j111,
    cv18j112,
    cv18j113,
    cv18j114,
    cv18j115,
    cv18j116,
    cv18j117,
    cv18j118,
    cv18j119,
    cv18j120,
    cv18j121,
    cv18j122,
    cv18j123,
    cv19k012,
    cv19k047,
    cv19k048,
    cv19k049,
    cv19k050,
    cv19k051,
    cv19k052,
    cv19k243,
    cv19k101,
    cv19k102,
    cv19k103,
    cv19k104,
    cv19k105,
    cv19k001,
    cv19k044,
    cv19k014,
    cv19k106,
    cv19k107,
    cv19k108,
    cv19k109,
    cv19k110,
    cv19k111,
    cv19k112,
    cv19k113,
    cv19k114,
    cv19k115,
    cv19k116,
    cv19k117,
    cv19k118,
    cv19k119,
    cv19k120,
    cv19k121,
    cv19k122,
    cv19k123
  )


#change names.
names(liss_pol) <-
  c(
    "nomem_encr",
    "pol_int.1" ,
    "parl_donot_care.1" ,
    "pol_part_int.1"    ,
    "no_influece.1" ,
    "cap_act_pol.1" ,
    "clear_picture.1"   ,
    "pol_compl.1"   ,
    "vote_int.1"    ,
    "lr_scale.1"    ,
    "euthanasia.1"  ,
    "income_diff.1" ,
    "immigrants.1",
    "eu_integration.1"  ,
    "gov_sat.1",
    "satdem.1",
    "poltrust.1",
    "postmatfirst.1",
    "postmatsecond.1",
    "postmatthird.1",
    "workingmother.1",
    "childjobmother.1",
    "fulltimemother.1",
    "equalcontribution.1",
    "fatherworks.1",
    "fatherhousehold.1",
    "fatherschildcare.1",
    "differentculture.1",
    "foreignerculture.1",
    "asylum.1",
    "foreignsocsec.1",
    "toomanyforeigners.1",
    "foreignnotaccepted.1",
    "foreigneconomy.1",
    "neighborhood.1",
    "pol_int.2" ,
    "parl_donot_care.2" ,
    "pol_part_int.2"    ,
    "no_influece.2" ,
    "cap_act_pol.2" ,
    "clear_picture.2"   ,
    "pol_compl.2"   ,
    "vote_int.2"    ,
    "lr_scale.2"    ,
    "euthanasia.2"  ,
    "income_diff.2" ,
    "immigrants.2"  ,
    "eu_integration.2"  ,
    "gov_sat.2",
    "satdem.2",
    "poltrust.2",
    "postmatfirst.2",
    "postmatsecond.2",
    "postmatthird.2",
    "workingmother.2",
    "childjobmother.2",
    "fulltimemother.2",
    "equalcontribution.2",
    "fatherworks.2",
    "fatherhousehold.2",
    "fatherschildcare.2",
    "differentculture.2",
    "foreignerculture.2",
    "asylum.2",
    "foreignsocsec.2",
    "toomanyforeigners.2",
    "foreignnotaccepted.2",
    "foreigneconomy.2",
    "neighborhood.2",
    "pol_int.3" ,
    "parl_donot_care.3" ,
    "pol_part_int.3"    ,
    "no_influece.3" ,
    "cap_act_pol.3" ,
    "clear_picture.3"   ,
    "pol_compl.3"   ,
    "vote_int.3"    ,
    "lr_scale.3"    ,
    "euthanasia.3"  ,
    "income_diff.3" ,
    "immigrants.3"  ,
    "eu_integration.3"  ,
    "gov_sat.3",
    "satdem.3",
    "poltrust.3",
    "postmatfirst.3",
    "postmatsecond.3",
    "postmatthird.3",
    "workingmother.3",
    "childjobmother.3",
    "fulltimemother.3",
    "equalcontribution.3",
    "fatherworks.3",
    "fatherhousehold.3",
    "fatherschildcare.3",
    "differentculture.3",
    "foreignerculture.3",
    "asylum.3",
    "foreignsocsec.3",
    "toomanyforeigners.3",
    "foreignnotaccepted.3",
    "foreigneconomy.3",
    "neighborhood.3",
    "pol_int.4" ,
    "parl_donot_care.4" ,
    "pol_part_int.4"    ,
    "no_influece.4" ,
    "cap_act_pol.4" ,
    "clear_picture.4"   ,
    "pol_compl.4"   ,
    "vote_int.4"    ,
    "lr_scale.4"    ,
    "euthanasia.4"  ,
    "income_diff.4" ,
    "immigrants.4"  ,
    "eu_integration.4"  ,
    "gov_sat.4",
    "satdem.4",
    "poltrust.4",
    "postmatfirst.4",
    "postmatsecond.4",
    "postmatthird.4",
    "workingmother.4",
    "childjobmother.4",
    "fulltimemother.4",
    "equalcontribution.4",
    "fatherworks.4",
    "fatherhousehold.4",
    "fatherschildcare.4",
    "differentculture.4",
    "foreignerculture.4",
    "asylum.4",
    "foreignsocsec.4",
    "toomanyforeigners.4",
    "foreignnotaccepted.4",
    "foreigneconomy.4",
    "neighborhood.4",
    "pol_int.5" ,
    "parl_donot_care.5" ,
    "pol_part_int.5"    ,
    "no_influece.5" ,
    "cap_act_pol.5" ,
    "clear_picture.5"   ,
    "pol_compl.5"   ,
    "vote_int.5"    ,
    "lr_scale.5"    ,
    "euthanasia.5"  ,
    "income_diff.5" ,
    "immigrants.5"  ,
    "eu_integration.5"  ,
    "gov_sat.5",
    "satdem.5",
    "poltrust.5",
    "postmatfirst.5",
    "postmatsecond.5",
    "postmatthird.5",
    "workingmother.5",
    "childjobmother.5",
    "fulltimemother.5",
    "equalcontribution.5",
    "fatherworks.5",
    "fatherhousehold.5",
    "fatherschildcare.5",
    "differentculture.5",
    "foreignerculture.5",
    "asylum.5",
    "foreignsocsec.5",
    "toomanyforeigners.5",
    "foreignnotaccepted.5",
    "foreigneconomy.5",
    "neighborhood.5",
    "pol_int.6" ,
    "parl_donot_care.6" ,
    "pol_part_int.6"    ,
    "no_influece.6" ,
    "cap_act_pol.6" ,
    "clear_picture.6"   ,
    "pol_compl.6"   ,
    "vote_int.6"    ,
    "lr_scale.6"    ,
    "euthanasia.6"  ,
    "income_diff.6" ,
    "immigrants.6"  ,
    "eu_integration.6"  ,
    "gov_sat.6",
    "satdem.6",
    "poltrust.6",
    "postmatfirst.6",
    "postmatsecond.6",
    "postmatthird.6",
    "workingmother.6",
    "childjobmother.6",
    "fulltimemother.6",
    "equalcontribution.6",
    "fatherworks.6",
    "fatherhousehold.6",
    "fatherschildcare.6",
    "differentculture.6",
    "foreignerculture.6",
    "asylum.6",
    "foreignsocsec.6",
    "toomanyforeigners.6",
    "foreignnotaccepted.6",
    "foreigneconomy.6",
    "neighborhood.6",
    "pol_int.7" ,
    "parl_donot_care.7" ,
    "pol_part_int.7"    ,
    "no_influece.7" ,
    "cap_act_pol.7" ,
    "clear_picture.7"   ,
    "pol_compl.7"   ,
    "vote_int.7"    ,
    "lr_scale.7"    ,
    "euthanasia.7"  ,
    "income_diff.7" ,
    "immigrants.7"  ,
    "eu_integration.7"  ,
    "gov_sat.7",
    "satdem.7",
    "poltrust.7",
    "postmatfirst.7",
    "postmatsecond.7",
    "postmatthird.7",
    "workingmother.7",
    "childjobmother.7",
    "fulltimemother.7",
    "equalcontribution.7",
    "fatherworks.7",
    "fatherhousehold.7",
    "fatherschildcare.7",
    "differentculture.7",
    "foreignerculture.7",
    "asylum.7",
    "foreignsocsec.7",
    "toomanyforeigners.7",
    "foreignnotaccepted.7",
    "foreigneconomy.7",
    "neighborhood.7",
    "pol_int.8" ,
    "parl_donot_care.8" ,
    "pol_part_int.8"    ,
    "no_influece.8" ,
    "cap_act_pol.8" ,
    "clear_picture.8"   ,
    "pol_compl.8"   ,
    "vote_int.8"    ,
    "lr_scale.8"    ,
    "euthanasia.8"  ,
    "income_diff.8" ,
    "immigrants.8"  ,
    "eu_integration.8"  ,
    "gov_sat.8",
    "satdem.8",
    "poltrust.8",
    "postmatfirst.8",
    "postmatsecond.8",
    "postmatthird.8",
    "workingmother.8",
    "childjobmother.8",
    "fulltimemother.8",
    "equalcontribution.8",
    "fatherworks.8",
    "fatherhousehold.8",
    "fatherschildcare.8",
    "differentculture.8",
    "foreignerculture.8",
    "asylum.8",
    "foreignsocsec.8",
    "toomanyforeigners.8",
    "foreignnotaccepted.8",
    "foreigneconomy.8",
    "neighborhood.8",
    "pol_int.9" ,
    "parl_donot_care.9" ,
    "pol_part_int.9"    ,
    "no_influece.9" ,
    "cap_act_pol.9" ,
    "clear_picture.9"   ,
    "pol_compl.9"   ,
    "vote_int.9"    ,
    "lr_scale.9"    ,
    "euthanasia.9"  ,
    "income_diff.9" ,
    "immigrants.9"  ,
    "eu_integration.9",
    "gov_sat.9",
    "satdem.9",
    "poltrust.9",
    "postmatfirst.9",
    "postmatsecond.9",
    "postmatthird.9",
    "workingmother.9",
    "childjobmother.9",
    "fulltimemother.9",
    "equalcontribution.9",
    "fatherworks.9",
    "fatherhousehold.9",
    "fatherschildcare.9",
    "differentculture.9",
    "foreignerculture.9",
    "asylum.9",
    "foreignsocsec.9",
    "toomanyforeigners.9",
    "foreignnotaccepted.9",
    "foreigneconomy.9",
    "neighborhood.9",
    "pol_int.10"    ,
    "parl_donot_care.10"    ,
    "pol_part_int.10"   ,
    "no_influece.10"    ,
    "cap_act_pol.10"    ,
    "clear_picture.10"  ,
    "pol_compl.10"  ,
    "vote_int.10"   ,
    "lr_scale.10"   ,
    "euthanasia.10" ,
    "income_diff.10"    ,
    "immigrants.10" ,
    "eu_integration.10",
    "gov_sat.10",
    "satdem.10",
    "poltrust.10",
    "postmatfirst.10",
    "postmatsecond.10",
    "postmatthird.10",
    "workingmother.10",
    "childjobmother.10",
    "fulltimemother.10",
    "equalcontribution.10",
    "fatherworks.10",
    "fatherhousehold.10",
    "fatherschildcare.10",
    "differentculture.10",
    "foreignerculture.10",
    "asylum.10",
    "foreignsocsec.10",
    "toomanyforeigners.10",
    "foreignnotaccepted.10",
    "foreigneconomy.10",
    "neighborhood.10",
    "pol_int.11"    ,
    "parl_donot_care.11"    ,
    "pol_part_int.11"   ,
    "no_influece.11"    ,
    "cap_act_pol.11"    ,
    "clear_picture.11"  ,
    "pol_compl.11"  ,
    "vote_int.11"   ,
    "lr_scale.11"   ,
    "euthanasia.11" ,
    "income_diff.11"    ,
    "immigrants.11" ,
    "eu_integration.11",
    "gov_sat.11",
    "satdem.11",
    "poltrust.11",
    "postmatfirst.11",
    "postmatsecond.11",
    "postmatthird.11",
    "workingmother.11",
    "childjobmother.11",
    "fulltimemother.11",
    "equalcontribution.11",
    "fatherworks.11",
    "fatherhousehold.11",
    "fatherschildcare.11",
    "differentculture.11",
    "foreignerculture.11",
    "asylum.11",
    "foreignsocsec.11",
    "toomanyforeigners.11",
    "foreignnotaccepted.11",
    "foreigneconomy.11",
    "neighborhood.11"
  )

Background data

#get the background data and rename the columns.
liss_avars <- liss_subset %>%
  select(
    nomem_encr,
    geslacht.1,
    positie.1,
    gebjaar.1,
    leeftijd.1,
    lftdcat.1,
    aantalhh.1,
    aantalki.1,
    partner.1,
    burgstat.1,
    woonvorm.1,
    woning.1,
    sted.1,
    belbezig.1,
    brutoink.1,
    nettoink.1,
    brutocat.1,
    nettocat.1,
    oplzon.1,
    oplmet.1,
    oplcat.1,
    nohouse_encr.1,
    geslacht.2,
    positie.2,
    gebjaar.2,
    leeftijd.2,
    lftdcat.2,
    aantalhh.2,
    aantalki.2,
    partner.2,
    burgstat.2,
    woonvorm.2,
    woning.2,
    sted.2,
    belbezig.2,
    brutoink.2,
    nettoink.2,
    brutocat.2,
    nettocat.2,
    oplzon.2,
    oplmet.2,
    oplcat.2,
    nohouse_encr.2,
    geslacht.3,
    positie.3,
    gebjaar.3,
    leeftijd.3,
    lftdcat.3,
    aantalhh.3,
    aantalki.3,
    partner.3,
    burgstat.3,
    woonvorm.3,
    woning.3,
    sted.3,
    belbezig.3,
    brutoink.3,
    nettoink.3,
    brutocat.3,
    nettocat.3,
    oplzon.3,
    oplmet.3,
    oplcat.3,
    nohouse_encr.3,
    geslacht.4,
    positie.4,
    gebjaar.4,
    leeftijd.4,
    lftdcat.4,
    aantalhh.4,
    aantalki.4,
    partner.4,
    burgstat.4,
    woonvorm.4,
    woning.4,
    sted.4,
    belbezig.4,
    brutoink.4,
    nettoink.4,
    brutocat.4,
    nettocat.4,
    oplzon.4,
    oplmet.4,
    oplcat.4,
    nohouse_encr.4,
    geslacht.5,
    positie.5,
    gebjaar.5,
    leeftijd.5,
    lftdcat.5,
    aantalhh.5,
    aantalki.5,
    partner.5,
    burgstat.5,
    woonvorm.5,
    woning.5,
    sted.5,
    belbezig.5,
    brutoink.5,
    nettoink.5,
    brutocat.5,
    nettocat.5,
    oplzon.5,
    oplmet.5,
    oplcat.5,
    nohouse_encr.5,
    geslacht.6,
    positie.6,
    gebjaar.6,
    leeftijd.6,
    lftdcat.6,
    aantalhh.6,
    aantalki.6,
    partner.6,
    burgstat.6,
    woonvorm.6,
    woning.6,
    sted.6,
    belbezig.6,
    brutoink.6,
    nettoink.6,
    brutocat.6,
    nettocat.6,
    oplzon.6,
    oplmet.6,
    oplcat.6,
    nohouse_encr.6,
    geslacht.7,
    positie.7,
    gebjaar.7,
    leeftijd.7,
    lftdcat.7,
    aantalhh.7,
    aantalki.7,
    partner.7,
    burgstat.7,
    woonvorm.7,
    woning.7,
    sted.7,
    belbezig.7,
    brutoink.7,
    nettoink.7,
    brutocat.7,
    nettocat.7,
    oplzon.7,
    oplmet.7,
    oplcat.7,
    nohouse_encr.7,
    geslacht.8,
    positie.8,
    gebjaar.8,
    leeftijd.8,
    lftdcat.8,
    aantalhh.8,
    aantalki.8,
    partner.8,
    burgstat.8,
    woonvorm.8,
    woning.8,
    sted.8,
    belbezig.8,
    brutoink.8,
    nettoink.8,
    brutocat.8,
    nettocat.8,
    oplzon.8,
    oplmet.8,
    oplcat.8,
    nohouse_encr.8,
    geslacht.9,
    positie.9,
    gebjaar.9,
    leeftijd.9,
    lftdcat.9,
    aantalhh.9,
    aantalki.9,
    partner.9,
    burgstat.9,
    woonvorm.9,
    woning.9,
    sted.9,
    belbezig.9,
    brutoink.9,
    nettoink.9,
    brutocat.9,
    nettocat.9,
    oplzon.9,
    oplmet.9,
    oplcat.9,
    nohouse_encr.9,
    geslacht.10,
    positie.10,
    gebjaar.10,
    leeftijd.10,
    lftdcat.10,
    aantalhh.10,
    aantalki.10,
    partner.10,
    burgstat.10,
    woonvorm.10,
    woning.10,
    sted.10,
    belbezig.10,
    brutoink.10,
    nettoink.10,
    brutocat.10,
    nettocat.10,
    oplzon.10,
    oplmet.10,
    oplcat.10,
    nohouse_encr.10,
    geslacht.11,
    positie.11,
    gebjaar.11,
    leeftijd.11,
    lftdcat.11,
    aantalhh.11,
    aantalki.11,
    partner.11,
    burgstat.11,
    woonvorm.11,
    woning.11,
    sted.11,
    belbezig.11,
    brutoink.11,
    nettoink.11,
    brutocat.11,
    nettocat.11,
    oplzon.11,
    oplmet.11,
    oplcat.11,
    nohouse_encr.11
  )

#get the background data and rename the columns.
#For the first three waves there are is no data on this variable
liss_origin <- liss_subset %>%
  select(
    nomem_encr,
    herkomstgroep.4,
    herkomstgroep.5,
    herkomstgroep.6,
    herkomstgroep.7,
    herkomstgroep.8,
    herkomstgroep.9,
    herkomstgroep.10,
    herkomstgroep.11
  )
names(liss_origin) <-
  c(
    "nomem_encr",
    "origin.4",
    "origin.5",
    "origin.6",
    "origin.7",
    "origin.8",
    "origin.9",
    "origin.10",
    "origin.11"
  )

#create ego origin 1-3 with NA's so we can later merge them into a long file.
liss_origin$origin.1 <- NA
liss_origin$origin.2 <- NA
liss_origin$origin.3 <- NA

#For some reason the reshape command bugs when the ordering is not correct.
liss_origin <- liss_origin[, c(1, 10:12, 2:9)]

Alter ids

#alter ids
liss_alter_id <- liss_subset %>%
  select(
    nomem_encr,
    name1_rec.1,
    name2_rec.1,
    name3_rec.1,
    name4_rec.1,
    name5_rec.1,
    name1_rec.2,
    name2_rec.2,
    name3_rec.2,
    name4_rec.2,
    name5_rec.2,
    name1_rec.3,
    name2_rec.3,
    name3_rec.3,
    name4_rec.3,
    name5_rec.3,
    name1_rec.4,
    name2_rec.4,
    name3_rec.4,
    name4_rec.4,
    name5_rec.4,
    name1_rec.5,
    name2_rec.5,
    name3_rec.5,
    name4_rec.5,
    name5_rec.5,
    name1_rec.6,
    name2_rec.6,
    name3_rec.6,
    name4_rec.6,
    name5_rec.6,
    name1_rec.7,
    name2_rec.7,
    name3_rec.7,
    name4_rec.7,
    name5_rec.7,
    name1_rec.8,
    name2_rec.8,
    name3_rec.8,
    name4_rec.8,
    name5_rec.8,
    name1_rec.9,
    name2_rec.9,
    name3_rec.9,
    name4_rec.9,
    name5_rec.9,
    name1_rec.10,
    name2_rec.10,
    name3_rec.10,
    name4_rec.10,
    name5_rec.10,
    name1_rec.11,
    name2_rec.11,
    name3_rec.11,
    name4_rec.11,
    name5_rec.11
  )

#rename the alter id's
names(liss_alter_id) <-
  c(
    "nomem_encr",
    "alter_id_1.1",
    "alter_id_2.1",
    "alter_id_3.1",
    "alter_id_4.1",
    "alter_id_5.1",
    "alter_id_1.2",
    "alter_id_2.2",
    "alter_id_3.2",
    "alter_id_4.2",
    "alter_id_5.2",
    "alter_id_1.3",
    "alter_id_2.3",
    "alter_id_3.3",
    "alter_id_4.3",
    "alter_id_5.3",
    "alter_id_1.4",
    "alter_id_2.4",
    "alter_id_3.4",
    "alter_id_4.4",
    "alter_id_5.4",
    "alter_id_1.5",
    "alter_id_2.5",
    "alter_id_3.5",
    "alter_id_4.5",
    "alter_id_5.5",
    "alter_id_1.6",
    "alter_id_2.6",
    "alter_id_3.6",
    "alter_id_4.6",
    "alter_id_5.6",
    "alter_id_1.7",
    "alter_id_2.7",
    "alter_id_3.7",
    "alter_id_4.7",
    "alter_id_5.7",
    "alter_id_1.8",
    "alter_id_2.8",
    "alter_id_3.8",
    "alter_id_4.8",
    "alter_id_5.8",
    "alter_id_1.9",
    "alter_id_2.9",
    "alter_id_3.9",
    "alter_id_4.9",
    "alter_id_5.9",
    "alter_id_1.10",
    "alter_id_2.10",
    "alter_id_3.10",
    "alter_id_4.10",
    "alter_id_5.10",
    "alter_id_1.11",
    "alter_id_2.11",
    "alter_id_3.11",
    "alter_id_4.11",
    "alter_id_5.11"
  )

Alter information

#extract the alter variables and rename them.
#renaming the alter variables.
#selecting the alter data.
liss_alters <-  liss %>%
  select(
    nomem_encr,
    cs08a326,
    cs08a315,
    cs08a320,
    cs08a328,
    cs08a329,
    cs08a324,
    cs08a323,
    cs08a321,
    cs08a300,
    cs08a327,
    cs08a337,
    cs08a316,
    cs08a331,
    cs08a339,
    cs08a340,
    cs08a335,
    cs08a334,
    cs08a332,
    cs08a301,
    cs08a338,
    cs08a348,
    cs08a317,
    cs08a342,
    cs08a350,
    cs08a351,
    cs08a346,
    cs08a345,
    cs08a343,
    cs08a302,
    cs08a349,
    cs08a359,
    cs08a318,
    cs08a353,
    cs08a361,
    cs08a362,
    cs08a357,
    cs08a356,
    cs08a354,
    cs08a303,
    cs08a360,
    cs08a370,
    cs08a319,
    cs08a364,
    cs08a372,
    cs08a373,
    cs08a368,
    cs08a367,
    cs08a365,
    cs08a304,
    cs08a371,
    cs09b326,
    cs09b315,
    cs09b320,
    cs09b328,
    cs09b329,
    cs09b324,
    cs09b323,
    cs09b321,
    cs09b300,
    cs09b327,
    cs09b337,
    cs09b316,
    cs09b331,
    cs09b339,
    cs09b340,
    cs09b335,
    cs09b334,
    cs09b332,
    cs09b301,
    cs09b338,
    cs09b348,
    cs09b317,
    cs09b342,
    cs09b350,
    cs09b351,
    cs09b346,
    cs09b345,
    cs09b343,
    cs09b302,
    cs09b349,
    cs09b359,
    cs09b318,
    cs09b353,
    cs09b361,
    cs09b362,
    cs09b357,
    cs09b356,
    cs09b354,
    cs09b303,
    cs09b360,
    cs09b370,
    cs09b319,
    cs09b364,
    cs09b372,
    cs09b373,
    cs09b368,
    cs09b367,
    cs09b365,
    cs09b304,
    cs09b371,
    cs10c326,
    cs10c315,
    cs10c320,
    cs10c328,
    cs10c329,
    cs10c324,
    cs10c323,
    cs10c321,
    cs10c300,
    cs10c327,
    cs10c337,
    cs10c316,
    cs10c331,
    cs10c339,
    cs10c340,
    cs10c335,
    cs10c334,
    cs10c332,
    cs10c301,
    cs10c338,
    cs10c348,
    cs10c317,
    cs10c342,
    cs10c350,
    cs10c351,
    cs10c346,
    cs10c345,
    cs10c343,
    cs10c302,
    cs10c349,
    cs10c359,
    cs10c318,
    cs10c353,
    cs10c361,
    cs10c362,
    cs10c357,
    cs10c356,
    cs10c354,
    cs10c303,
    cs10c360,
    cs10c370,
    cs10c319,
    cs10c364,
    cs10c372,
    cs10c373,
    cs10c368,
    cs10c367,
    cs10c365,
    cs10c304,
    cs10c371,
    cs11d326,
    cs11d315,
    cs11d320,
    cs11d328,
    cs11d329,
    cs11d324,
    cs11d323,
    cs11d321,
    cs11d300,
    cs11d327,
    cs11d337,
    cs11d316,
    cs11d331,
    cs11d339,
    cs11d340,
    cs11d335,
    cs11d334,
    cs11d332,
    cs11d301,
    cs11d338,
    cs11d348,
    cs11d317,
    cs11d342,
    cs11d350,
    cs11d351,
    cs11d346,
    cs11d345,
    cs11d343,
    cs11d302,
    cs11d349,
    cs11d359,
    cs11d318,
    cs11d353,
    cs11d361,
    cs11d362,
    cs11d357,
    cs11d356,
    cs11d354,
    cs11d303,
    cs11d360,
    cs11d370,
    cs11d319,
    cs11d364,
    cs11d372,
    cs11d373,
    cs11d368,
    cs11d367,
    cs11d365,
    cs11d304,
    cs11d371,
    cs12e326,
    cs12e315,
    cs12e320,
    cs12e328,
    cs12e329,
    cs12e324,
    cs12e323,
    cs12e321,
    cs12e300,
    cs12e327,
    cs12e337,
    cs12e316,
    cs12e331,
    cs12e339,
    cs12e340,
    cs12e335,
    cs12e334,
    cs12e332,
    cs12e301,
    cs12e338,
    cs12e348,
    cs12e317,
    cs12e342,
    cs12e350,
    cs12e351,
    cs12e346,
    cs12e345,
    cs12e343,
    cs12e302,
    cs12e349,
    cs12e359,
    cs12e318,
    cs12e353,
    cs12e361,
    cs12e362,
    cs12e357,
    cs12e356,
    cs12e354,
    cs12e303,
    cs12e360,
    cs12e370,
    cs12e319,
    cs12e364,
    cs12e372,
    cs12e373,
    cs12e368,
    cs12e367,
    cs12e365,
    cs12e304,
    cs12e371,
    cs13f326,
    cs13f315,
    cs13f320,
    cs13f328,
    cs13f329,
    cs13f324,
    cs13f323,
    cs13f321,
    cs13f300,
    cs13f327,
    cs13f337,
    cs13f316,
    cs13f331,
    cs13f339,
    cs13f340,
    cs13f335,
    cs13f334,
    cs13f332,
    cs13f301,
    cs13f338,
    cs13f348,
    cs13f317,
    cs13f342,
    cs13f350,
    cs13f351,
    cs13f346,
    cs13f345,
    cs13f343,
    cs13f302,
    cs13f349,
    cs13f359,
    cs13f318,
    cs13f353,
    cs13f361,
    cs13f362,
    cs13f357,
    cs13f356,
    cs13f354,
    cs13f303,
    cs13f360,
    cs13f370,
    cs13f319,
    cs13f364,
    cs13f372,
    cs13f373,
    cs13f368,
    cs13f367,
    cs13f365,
    cs13f304,
    cs13f371,
    cs14g326,
    cs14g315,
    cs14g320,
    cs14g328,
    cs14g329,
    cs14g324,
    cs14g323,
    cs14g321,
    cs14g300,
    cs14g327,
    cs14g337,
    cs14g316,
    cs14g331,
    cs14g339,
    cs14g340,
    cs14g335,
    cs14g334,
    cs14g332,
    cs14g301,
    cs14g338,
    cs14g348,
    cs14g317,
    cs14g342,
    cs14g350,
    cs14g351,
    cs14g346,
    cs14g345,
    cs14g343,
    cs14g302,
    cs14g349,
    cs14g359,
    cs14g318,
    cs14g353,
    cs14g361,
    cs14g362,
    cs14g357,
    cs14g356,
    cs14g354,
    cs14g303,
    cs14g360,
    cs14g370,
    cs14g319,
    cs14g364,
    cs14g372,
    cs14g373,
    cs14g368,
    cs14g367,
    cs14g365,
    cs14g304,
    cs14g371,
    cs15h326,
    cs15h315,
    cs15h320,
    cs15h328,
    cs15h329,
    cs15h324,
    cs15h323,
    cs15h321,
    cs15h300,
    cs15h327,
    cs15h337,
    cs15h316,
    cs15h331,
    cs15h339,
    cs15h340,
    cs15h335,
    cs15h334,
    cs15h332,
    cs15h301,
    cs15h338,
    cs15h348,
    cs15h317,
    cs15h342,
    cs15h350,
    cs15h351,
    cs15h346,
    cs15h345,
    cs15h343,
    cs15h302,
    cs15h349,
    cs15h359,
    cs15h318,
    cs15h353,
    cs15h361,
    cs15h362,
    cs15h357,
    cs15h356,
    cs15h354,
    cs15h303,
    cs15h360,
    cs15h370,
    cs15h319,
    cs15h364,
    cs15h372,
    cs15h373,
    cs15h368,
    cs15h367,
    cs15h365,
    cs15h304,
    cs15h371,
    cs16i326,
    cs16i315,
    cs16i320,
    cs16i328,
    cs16i329,
    cs16i324,
    cs16i323,
    cs16i321,
    cs16i300,
    cs16i327,
    cs16i337,
    cs16i316,
    cs16i331,
    cs16i339,
    cs16i340,
    cs16i335,
    cs16i334,
    cs16i332,
    cs16i301,
    cs16i338,
    cs16i348,
    cs16i317,
    cs16i342,
    cs16i350,
    cs16i351,
    cs16i346,
    cs16i345,
    cs16i343,
    cs16i302,
    cs16i349,
    cs16i359,
    cs16i318,
    cs16i353,
    cs16i361,
    cs16i362,
    cs16i357,
    cs16i356,
    cs16i354,
    cs16i303,
    cs16i360,
    cs16i370,
    cs16i319,
    cs16i364,
    cs16i372,
    cs16i373,
    cs16i368,
    cs16i367,
    cs16i365,
    cs16i304,
    cs16i371,
    cs17j326,
    cs17j315,
    cs17j320,
    cs17j328,
    cs17j329,
    cs17j324,
    cs17j323,
    cs17j321,
    cs17j300,
    cs17j327,
    cs17j337,
    cs17j316,
    cs17j331,
    cs17j339,
    cs17j340,
    cs17j335,
    cs17j334,
    cs17j332,
    cs17j301,
    cs17j338,
    cs17j348,
    cs17j317,
    cs17j342,
    cs17j350,
    cs17j351,
    cs17j346,
    cs17j345,
    cs17j343,
    cs17j302,
    cs17j349,
    cs17j359,
    cs17j318,
    cs17j353,
    cs17j361,
    cs17j362,
    cs17j357,
    cs17j356,
    cs17j354,
    cs17j303,
    cs17j360,
    cs17j370,
    cs17j319,
    cs17j364,
    cs17j372,
    cs17j373,
    cs17j368,
    cs17j367,
    cs17j365,
    cs17j304,
    cs17j371,
    cs18k326,
    cs18k315,
    cs18k320,
    cs18k328,
    cs18k329,
    cs18k324,
    cs18k323,
    cs18k321,
    cs18k300,
    cs18k327,
    cs18k337,
    cs18k316,
    cs18k331,
    cs18k339,
    cs18k340,
    cs18k335,
    cs18k334,
    cs18k332,
    cs18k301,
    cs18k338,
    cs18k348,
    cs18k317,
    cs18k342,
    cs18k350,
    cs18k351,
    cs18k346,
    cs18k345,
    cs18k343,
    cs18k302,
    cs18k349,
    cs18k359,
    cs18k318,
    cs18k353,
    cs18k361,
    cs18k362,
    cs18k357,
    cs18k356,
    cs18k354,
    cs18k303,
    cs18k360,
    cs18k370,
    cs18k319,
    cs18k364,
    cs18k372,
    cs18k373,
    cs18k368,
    cs18k367,
    cs18k365,
    cs18k304,
    cs18k371,
    cs08a305,
    cs08a306,
    cs08a307,
    cs08a308,
    cs08a309,
    cs08a310,
    cs08a311,
    cs08a312,
    cs08a313,
    cs08a314,
    cs09b305,
    cs09b306,
    cs09b307,
    cs09b308,
    cs09b309,
    cs09b310,
    cs09b311,
    cs09b312,
    cs09b313,
    cs09b314,
    cs10c305,
    cs10c306,
    cs10c307,
    cs10c308,
    cs10c309,
    cs10c310,
    cs10c311,
    cs10c312,
    cs10c313,
    cs10c314,
    cs11d305,
    cs11d306,
    cs11d307,
    cs11d308,
    cs11d309,
    cs11d310,
    cs11d311,
    cs11d312,
    cs11d313,
    cs11d314,
    cs12e305,
    cs12e306,
    cs12e307,
    cs12e308,
    cs12e309,
    cs12e310,
    cs12e311,
    cs12e312,
    cs12e313,
    cs12e314,
    cs13f305,
    cs13f306,
    cs13f307,
    cs13f308,
    cs13f309,
    cs13f310,
    cs13f311,
    cs13f312,
    cs13f313,
    cs13f314,
    cs14g305,
    cs14g306,
    cs14g307,
    cs14g308,
    cs14g309,
    cs14g310,
    cs14g311,
    cs14g312,
    cs14g313,
    cs14g314,
    cs15h305,
    cs15h306,
    cs15h307,
    cs15h308,
    cs15h309,
    cs15h310,
    cs15h311,
    cs15h312,
    cs15h313,
    cs15h314,
    cs16i305,
    cs16i306,
    cs16i307,
    cs16i308,
    cs16i309,
    cs16i310,
    cs16i311,
    cs16i312,
    cs16i313,
    cs16i314,
    cs17j305,
    cs17j306,
    cs17j307,
    cs17j308,
    cs17j309,
    cs17j310,
    cs17j311,
    cs17j312,
    cs17j313,
    cs17j314,
    cs18k305,
    cs18k306,
    cs18k307,
    cs18k308,
    cs18k309,
    cs18k310,
    cs18k311,
    cs18k312,
    cs18k313,
    cs18k314,
    cs08a325,
    cs08a336,
    cs08a347,
    cs08a358,
    cs08a369,
    cs09b325,
    cs09b336,
    cs09b347,
    cs09b358,
    cs09b369,
    cs10c325,
    cs10c336,
    cs10c347,
    cs10c358,
    cs10c369,
    cs11d325,
    cs11d336,
    cs11d347,
    cs11d358,
    cs11d369,
    cs12e325,
    cs12e336,
    cs12e347,
    cs12e358,
    cs12e369,
    cs13f325,
    cs13f336,
    cs13f347,
    cs13f358,
    cs13f369,
    cs14g325,
    cs14g336,
    cs14g347,
    cs14g358,
    cs14g369,
    cs15h325,
    cs15h336,
    cs15h347,
    cs15h358,
    cs15h369,
    cs16i325,
    cs16i336,
    cs16i347,
    cs16i358,
    cs16i369,
    cs17j325,
    cs17j336,
    cs17j347,
    cs17j358,
    cs17j369,
    cs18k325,
    cs18k336,
    cs18k347,
    cs18k358,
    cs18k369,
    cs08a_m,
    cs09b_m,
    cs10c_m,
    cs11d_m,
    cs12e_m,
    cs13f_m,
    cs14g_m,
    cs15h_m,
    cs16i_m,
    cs17j_m,
    cs18k_m
  )

#Rename the alter variables.
names(liss_alters) <-
  c(
    "nomem_encr",
    "educ_alter1.1",
    "gender_alter1.1",
    "origin_alter1.1",
    "prof_alter1.1",
    "age_alter1.1",
    "poltalk_alter1.1",
    "talk_alter1.1",
    "rel_alter1.1",
    "dear_alter1.1",
    "work_a1.1",
    "educ_alter2.1",
    "gender_alter2.1",
    "origin_alter2.1",
    "prof_alter2.1",
    "age_alter2.1",
    "poltalk_alter2.1",
    "talk_alter2.1",
    "rel_alter2.1",
    "dear_alter2.1",
    "work_a2.1",
    "educ_alter3.1",
    "gender_alter3.1",
    "origin_alter3.1",
    "prof_alter3.1",
    "age_alter3.1",
    "poltalk_alter3.1",
    "talk_alter3.1",
    "rel_alter3.1",
    "dear_alter3.1",
    "work_a3.1",
    "educ_alter4.1",
    "gender_alter4.1",
    "origin_alter4.1",
    "prof_alter4.1",
    "age_alter4.1",
    "poltalk_alter4.1",
    "talk_alter4.1",
    "rel_alter4.1",
    "dear_alter4.1",
    "work_a4.1",
    "educ_alter5.1",
    "gender_alter5.1",
    "origin_alter5.1",
    "prof_alter5.1",
    "age_alter5.1",
    "poltalk_alter5.1",
    "talk_alter5.1",
    "rel_alter5.1",
    "dear_alter5.1",
    "work_a5.1",
    "educ_alter1.2",
    "gender_alter1.2",
    "origin_alter1.2",
    "prof_alter1.2",
    "age_alter1.2",
    "poltalk_alter1.2",
    "talk_alter1.2",
    "rel_alter1.2",
    "dear_alter1.2",
    "work_a1.2",
    "educ_alter2.2",
    "gender_alter2.2",
    "origin_alter2.2",
    "prof_alter2.2",
    "age_alter2.2",
    "poltalk_alter2.2",
    "talk_alter2.2",
    "rel_alter2.2",
    "dear_alter2.2",
    "work_a2.2",
    "educ_alter3.2",
    "gender_alter3.2",
    "origin_alter3.2",
    "prof_alter3.2",
    "age_alter3.2",
    "poltalk_alter3.2",
    "talk_alter3.2",
    "rel_alter3.2",
    "dear_alter3.2",
    "work_a3.2",
    "educ_alter4.2",
    "gender_alter4.2",
    "origin_alter4.2",
    "prof_alter4.2",
    "age_alter4.2",
    "poltalk_alter4.2",
    "talk_alter4.2",
    "rel_alter4.2",
    "dear_alter4.2",
    "work_a4.2",
    "educ_alter5.2",
    "gender_alter5.2",
    "origin_alter5.2",
    "prof_alter5.2",
    "age_alter5.2",
    "poltalk_alter5.2",
    "talk_alter5.2",
    "rel_alter5.2",
    "dear_alter5.2",
    "work_a5.2",
    "educ_alter1.3",
    "gender_alter1.3",
    "origin_alter1.3",
    "prof_alter1.3",
    "age_alter1.3",
    "poltalk_alter1.3",
    "talk_alter1.3",
    "rel_alter1.3",
    "dear_alter1.3",
    "work_a1.3",
    "educ_alter2.3",
    "gender_alter2.3",
    "origin_alter2.3",
    "prof_alter2.3",
    "age_alter2.3",
    "poltalk_alter2.3",
    "talk_alter2.3",
    "rel_alter2.3",
    "dear_alter2.3",
    "work_a2.3",
    "educ_alter3.3",
    "gender_alter3.3",
    "origin_alter3.3",
    "prof_alter3.3",
    "age_alter3.3",
    "poltalk_alter3.3",
    "talk_alter3.3",
    "rel_alter3.3",
    "dear_alter3.3",
    "work_a3.3",
    "educ_alter4.3",
    "gender_alter4.3",
    "origin_alter4.3",
    "prof_alter4.3",
    "age_alter4.3",
    "poltalk_alter4.3",
    "talk_alter4.3",
    "rel_alter4.3",
    "dear_alter4.3",
    "work_a4.3",
    "educ_alter5.3",
    "gender_alter5.3",
    "origin_alter5.3",
    "prof_alter5.3",
    "age_alter5.3",
    "poltalk_alter5.3",
    "talk_alter5.3",
    "rel_alter5.3",
    "dear_alter5.3",
    "work_a5.3",
    "educ_alter1.4",
    "gender_alter1.4",
    "origin_alter1.4",
    "prof_alter1.4",
    "age_alter1.4",
    "poltalk_alter1.4",
    "talk_alter1.4",
    "rel_alter1.4",
    "dear_alter1.4",
    "work_a1.4",
    "educ_alter2.4",
    "gender_alter2.4",
    "origin_alter2.4",
    "prof_alter2.4",
    "age_alter2.4",
    "poltalk_alter2.4",
    "talk_alter2.4",
    "rel_alter2.4",
    "dear_alter2.4",
    "work_a2.4",
    "educ_alter3.4",
    "gender_alter3.4",
    "origin_alter3.4",
    "prof_alter3.4",
    "age_alter3.4",
    "poltalk_alter3.4",
    "talk_alter3.4",
    "rel_alter3.4",
    "dear_alter3.4",
    "work_a3.4",
    "educ_alter4.4",
    "gender_alter4.4",
    "origin_alter4.4",
    "prof_alter4.4",
    "age_alter4.4",
    "poltalk_alter4.4",
    "talk_alter4.4",
    "rel_alter4.4",
    "dear_alter4.4",
    "work_a4.4",
    "educ_alter5.4",
    "gender_alter5.4",
    "origin_alter5.4",
    "prof_alter5.4",
    "age_alter5.4",
    "poltalk_alter5.4",
    "talk_alter5.4",
    "rel_alter5.4",
    "dear_alter5.4",
    "work_a5.4",
    "educ_alter1.5",
    "gender_alter1.5",
    "origin_alter1.5",
    "prof_alter1.5",
    "age_alter1.5",
    "poltalk_alter1.5",
    "talk_alter1.5",
    "rel_alter1.5",
    "dear_alter1.5",
    "work_a1.5",
    "educ_alter2.5",
    "gender_alter2.5",
    "origin_alter2.5",
    "prof_alter2.5",
    "age_alter2.5",
    "poltalk_alter2.5",
    "talk_alter2.5",
    "rel_alter2.5",
    "dear_alter2.5",
    "work_a2.5",
    "educ_alter3.5",
    "gender_alter3.5",
    "origin_alter3.5",
    "prof_alter3.5",
    "age_alter3.5",
    "poltalk_alter3.5",
    "talk_alter3.5",
    "rel_alter3.5",
    "dear_alter3.5",
    "work_a3.5",
    "educ_alter4.5",
    "gender_alter4.5",
    "origin_alter4.5",
    "prof_alter4.5",
    "age_alter4.5",
    "poltalk_alter4.5",
    "talk_alter4.5",
    "rel_alter4.5",
    "dear_alter4.5",
    "work_a4.5",
    "educ_alter5.5",
    "gender_alter5.5",
    "origin_alter5.5",
    "prof_alter5.5",
    "age_alter5.5",
    "poltalk_alter5.5",
    "talk_alter5.5",
    "rel_alter5.5",
    "dear_alter5.5",
    "work_a5.5",
    "educ_alter1.6",
    "gender_alter1.6",
    "origin_alter1.6",
    "prof_alter1.6",
    "age_alter1.6",
    "poltalk_alter1.6",
    "talk_alter1.6",
    "rel_alter1.6",
    "dear_alter1.6",
    "work_a1.6",
    "educ_alter2.6",
    "gender_alter2.6",
    "origin_alter2.6",
    "prof_alter2.6",
    "age_alter2.6",
    "poltalk_alter2.6",
    "talk_alter2.6",
    "rel_alter2.6",
    "dear_alter2.6",
    "work_a2.6",
    "educ_alter3.6",
    "gender_alter3.6",
    "origin_alter3.6",
    "prof_alter3.6",
    "age_alter3.6",
    "poltalk_alter3.6",
    "talk_alter3.6",
    "rel_alter3.6",
    "dear_alter3.6",
    "work_a3.6",
    "educ_alter4.6",
    "gender_alter4.6",
    "origin_alter4.6",
    "prof_alter4.6",
    "age_alter4.6",
    "poltalk_alter4.6",
    "talk_alter4.6",
    "rel_alter4.6",
    "dear_alter4.6",
    "work_a4.6",
    "educ_alter5.6",
    "gender_alter5.6",
    "origin_alter5.6",
    "prof_alter5.6",
    "age_alter5.6",
    "poltalk_alter5.6",
    "talk_alter5.6",
    "rel_alter5.6",
    "dear_alter5.6",
    "work_a5.6",
    "educ_alter1.7",
    "gender_alter1.7",
    "origin_alter1.7",
    "prof_alter1.7",
    "age_alter1.7",
    "poltalk_alter1.7",
    "talk_alter1.7",
    "rel_alter1.7",
    "dear_alter1.7",
    "work_a1.7",
    "educ_alter2.7",
    "gender_alter2.7",
    "origin_alter2.7",
    "prof_alter2.7",
    "age_alter2.7",
    "poltalk_alter2.7",
    "talk_alter2.7",
    "rel_alter2.7",
    "dear_alter2.7",
    "work_a2.7",
    "educ_alter3.7",
    "gender_alter3.7",
    "origin_alter3.7",
    "prof_alter3.7",
    "age_alter3.7",
    "poltalk_alter3.7",
    "talk_alter3.7",
    "rel_alter3.7",
    "dear_alter3.7",
    "work_a3.7",
    "educ_alter4.7",
    "gender_alter4.7",
    "origin_alter4.7",
    "prof_alter4.7",
    "age_alter4.7",
    "poltalk_alter4.7",
    "talk_alter4.7",
    "rel_alter4.7",
    "dear_alter4.7",
    "work_a4.7",
    "educ_alter5.7",
    "gender_alter5.7",
    "origin_alter5.7",
    "prof_alter5.7",
    "age_alter5.7",
    "poltalk_alter5.7",
    "talk_alter5.7",
    "rel_alter5.7",
    "dear_alter5.7",
    "work_a5.7",
    "educ_alter1.8",
    "gender_alter1.8",
    "origin_alter1.8",
    "prof_alter1.8",
    "age_alter1.8",
    "poltalk_alter1.8",
    "talk_alter1.8",
    "rel_alter1.8",
    "dear_alter1.8",
    "work_a1.8",
    "educ_alter2.8",
    "gender_alter2.8",
    "origin_alter2.8",
    "prof_alter2.8",
    "age_alter2.8",
    "poltalk_alter2.8",
    "talk_alter2.8",
    "rel_alter2.8",
    "dear_alter2.8",
    "work_a2.8",
    "educ_alter3.8",
    "gender_alter3.8",
    "origin_alter3.8",
    "prof_alter3.8",
    "age_alter3.8",
    "poltalk_alter3.8",
    "talk_alter3.8",
    "rel_alter3.8",
    "dear_alter3.8",
    "work_a3.8",
    "educ_alter4.8",
    "gender_alter4.8",
    "origin_alter4.8",
    "prof_alter4.8",
    "age_alter4.8",
    "poltalk_alter4.8",
    "talk_alter4.8",
    "rel_alter4.8",
    "dear_alter4.8",
    "work_a4.8",
    "educ_alter5.8",
    "gender_alter5.8",
    "origin_alter5.8",
    "prof_alter5.8",
    "age_alter5.8",
    "poltalk_alter5.8",
    "talk_alter5.8",
    "rel_alter5.8",
    "dear_alter5.8",
    "work_a5.8",
    "educ_alter1.9",
    "gender_alter1.9",
    "origin_alter1.9",
    "prof_alter1.9",
    "age_alter1.9",
    "poltalk_alter1.9",
    "talk_alter1.9",
    "rel_alter1.9",
    "dear_alter1.9",
    "work_a1.9",
    "educ_alter2.9",
    "gender_alter2.9",
    "origin_alter2.9",
    "prof_alter2.9",
    "age_alter2.9",
    "poltalk_alter2.9",
    "talk_alter2.9",
    "rel_alter2.9",
    "dear_alter2.9",
    "work_a2.9",
    "educ_alter3.9",
    "gender_alter3.9",
    "origin_alter3.9",
    "prof_alter3.9",
    "age_alter3.9",
    "poltalk_alter3.9",
    "talk_alter3.9",
    "rel_alter3.9",
    "dear_alter3.9",
    "work_a3.9",
    "educ_alter4.9",
    "gender_alter4.9",
    "origin_alter4.9",
    "prof_alter4.9",
    "age_alter4.9",
    "poltalk_alter4.9",
    "talk_alter4.9",
    "rel_alter4.9",
    "dear_alter4.9",
    "work_a4.9",
    "educ_alter5.9",
    "gender_alter5.9",
    "origin_alter5.9",
    "prof_alter5.9",
    "age_alter5.9",
    "poltalk_alter5.9",
    "talk_alter5.9",
    "rel_alter5.9",
    "dear_alter5.9",
    "work_a5.9",
    "educ_alter1.10",
    "gender_alter1.10",
    "origin_alter1.10",
    "prof_alter1.10",
    "age_alter1.10",
    "poltalk_alter1.10",
    "talk_alter1.10",
    "rel_alter1.10",
    "dear_alter1.10",
    "work_a1.10",
    "educ_alter2.10",
    "gender_alter2.10",
    "origin_alter2.10",
    "prof_alter2.10",
    "age_alter2.10",
    "poltalk_alter2.10",
    "talk_alter2.10",
    "rel_alter2.10",
    "dear_alter2.10",
    "work_a2.10",
    "educ_alter3.10",
    "gender_alter3.10",
    "origin_alter3.10",
    "prof_alter3.10",
    "age_alter3.10",
    "poltalk_alter3.10",
    "talk_alter3.10",
    "rel_alter3.10",
    "dear_alter3.10",
    "work_a3.10",
    "educ_alter4.10",
    "gender_alter4.10",
    "origin_alter4.10",
    "prof_alter4.10",
    "age_alter4.10",
    "poltalk_alter4.10",
    "talk_alter4.10",
    "rel_alter4.10",
    "dear_alter4.10",
    "work_a4.10",
    "educ_alter5.10",
    "gender_alter5.10",
    "origin_alter5.10",
    "prof_alter5.10",
    "age_alter5.10",
    "poltalk_alter5.10",
    "talk_alter5.10",
    "rel_alter5.10",
    "dear_alter5.10",
    "work_a5.10",
    "educ_alter1.11",
    "gender_alter1.11",
    "origin_alter1.11",
    "prof_alter1.11",
    "age_alter1.11",
    "poltalk_alter1.11",
    "talk_alter1.11",
    "rel_alter1.11",
    "dear_alter1.11",
    "work_a1.11",
    "educ_alter2.11",
    "gender_alter2.11",
    "origin_alter2.11",
    "prof_alter2.11",
    "age_alter2.11",
    "poltalk_alter2.11",
    "talk_alter2.11",
    "rel_alter2.11",
    "dear_alter2.11",
    "work_a2.11",
    "educ_alter3.11",
    "gender_alter3.11",
    "origin_alter3.11",
    "prof_alter3.11",
    "age_alter3.11",
    "poltalk_alter3.11",
    "talk_alter3.11",
    "rel_alter3.11",
    "dear_alter3.11",
    "work_a3.11",
    "educ_alter4.11",
    "gender_alter4.11",
    "origin_alter4.11",
    "prof_alter4.11",
    "age_alter4.11",
    "poltalk_alter4.11",
    "talk_alter4.11",
    "rel_alter4.11",
    "dear_alter4.11",
    "work_a4.11",
    "educ_alter5.11",
    "gender_alter5.11",
    "origin_alter5.11",
    "prof_alter5.11",
    "age_alter5.11",
    "poltalk_alter5.11",
    "talk_alter5.11",
    "rel_alter5.11",
    "dear_alter5.11",
    "work_a5.11",
    "close_12.1",
    "close_13.1",
    "close_14.1",
    "close_15.1",
    "close_23.1",
    "close_24.1",
    "close_25.1",
    "close_34.1",
    "close_35.1",
    "close_45.1",
    "close_12.2",
    "close_13.2",
    "close_14.2",
    "close_15.2",
    "close_23.2",
    "close_24.2",
    "close_25.2",
    "close_34.2",
    "close_35.2",
    "close_45.2",
    "close_12.3",
    "close_13.3",
    "close_14.3",
    "close_15.3",
    "close_23.3",
    "close_24.3",
    "close_25.3",
    "close_34.3",
    "close_35.3",
    "close_45.3",
    "close_12.4",
    "close_13.4",
    "close_14.4",
    "close_15.4",
    "close_23.4",
    "close_24.4",
    "close_25.4",
    "close_34.4",
    "close_35.4",
    "close_45.4",
    "close_12.5",
    "close_13.5",
    "close_14.5",
    "close_15.5",
    "close_23.5",
    "close_24.5",
    "close_25.5",
    "close_34.5",
    "close_35.5",
    "close_45.5",
    "close_12.6",
    "close_13.6",
    "close_14.6",
    "close_15.6",
    "close_23.6",
    "close_24.6",
    "close_25.6",
    "close_34.6",
    "close_35.6",
    "close_45.6",
    "close_12.7",
    "close_13.7",
    "close_14.7",
    "close_15.7",
    "close_23.7",
    "close_24.7",
    "close_25.7",
    "close_34.7",
    "close_35.7",
    "close_45.7",
    "close_12.8",
    "close_13.8",
    "close_14.8",
    "close_15.8",
    "close_23.8",
    "close_24.8",
    "close_25.8",
    "close_34.8",
    "close_35.8",
    "close_45.8",
    "close_12.9",
    "close_13.9",
    "close_14.9",
    "close_15.9",
    "close_23.9",
    "close_24.9",
    "close_25.9",
    "close_34.9",
    "close_35.9",
    "close_45.9",
    "close_12.10",
    "close_13.10",
    "close_14.10",
    "close_15.10",
    "close_23.10",
    "close_24.10",
    "close_25.10",
    "close_34.10",
    "close_35.10",
    "close_45.10",
    "close_12.11",
    "close_13.11",
    "close_14.11",
    "close_15.11",
    "close_23.11",
    "close_24.11",
    "close_25.11",
    "close_34.11",
    "close_35.11",
    "close_45.11",
    "length_1.1",
    "length_2.1",
    "length_3.1",
    "length_4.1",
    "length_5.1",
    "length_1.2",
    "length_2.2",
    "length_3.2",
    "length_4.2",
    "length_5.2",
    "length_1.3",
    "length_2.3",
    "length_3.3",
    "length_4.3",
    "length_5.3",
    "length_1.4",
    "length_2.4",
    "length_3.4",
    "length_4.4",
    "length_5.4",
    "length_1.5",
    "length_2.5",
    "length_3.5",
    "length_4.5",
    "length_5.5",
    "length_1.6",
    "length_2.6",
    "length_3.6",
    "length_4.6",
    "length_5.6",
    "length_1.7",
    "length_2.7",
    "length_3.7",
    "length_4.7",
    "length_5.7",
    "length_1.8",
    "length_2.8",
    "length_3.8",
    "length_4.8",
    "length_5.8",
    "length_1.9",
    "length_2.9",
    "length_3.9",
    "length_4.9",
    "length_5.9",
    "length_1.10",
    "length_2.10",
    "length_3.10",
    "length_4.10",
    "length_5.10",
    "length_1.11",
    "length_2.11",
    "length_3.11",
    "length_4.11",
    "length_5.11",
    "leisure_part.1",
    "leisure_part.2",
    "leisure_part.3",
    "leisure_part.4",
    "leisure_part.5",
    "leisure_part.6",
    "leisure_part.7",
    "leisure_part.8",
    "leisure_part.9",
    "leisure_part.10",
    "leisure_part.11"
  )

Combine dataframes

Combine all modules into one datafile.

#combine the different data sources into one wide data file. 
liss_wide <- liss_pol %>% 
  left_join(liss_alters, by = "nomem_encr") %>% 
  left_join(liss_alter_id, by = "nomem_encr") %>% 
  left_join(liss_origin, by = "nomem_encr") %>% 
  left_join(liss_avars, by = "nomem_encr")

#remove all labels
liss_wide <- sjlabelled::remove_all_labels(liss_wide)

Export

Pivot data in long format and export data.

#--------------------------- Reshape to long file. -------------------------#
#create a long file.
liss_long <- liss_wide %>% 
  pivot_longer(cols = 2:1398, 
               names_to = c("measure", "survey_wave"),
               names_pattern = "(.+)\\.(.+)",
               values_to = "value") %>%
  pivot_wider(names_from = measure,
              values_from = value) %>% 
  mutate(survey_wave = as.numeric(survey_wave))

#clean the environment. 
rm(list=ls()[! ls() %in% c("liss_wide", "liss_long")])

#save RData.
save.image("data/data-processed/liss_merged/liss_core_merged_V3_240624.Rdata")

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Copyright © 2024 Jeroense Thijmen