data-raw/create_trust_tables.R

In antaldaniel/eurobarometer: Tidy Eurobarometer Survey Microdata For Retrospective Harmonization

library(eurobarometer)
library(dplyr)
library(tibble)

metadata_database <-  readRDS(
  file.path('data-raw',
  'eb_metadata_database_large.rds'))

select_metadata_vars <- c("filename", "var_name_orig", "var_label_orig", "var_label_norm",
                          "val_label_orig", "val_label_norm" , "val_numeric_orig",
                          "val_order_alpha", "n_categories")

trust_metadata <- metadata_database  %>%
  filter (
    grepl( "trust", var_label_norm )
  )  %>%
  select ( all_of(select_metadata_vars)) %>%
  arrange ( var_label_norm, val_label_norm, filename )

trust_metadata <- trust_metadata %>%
  filter (
    grepl( "trust_in_institutions|trust_political_parties|_trust$", var_label_norm )
  ) %>%
  filter( ! (filename == "ZA4744_v5-0-0.sav" & var_name_orig %in% c("v336", "v347","v292", "v303",
                                                                    "v314", "v325", "v270", "v281")))
trust_metadata <- trust_metadata %>%
  filter( ! (filename == "ZA3938_v1-0-1.sav" & var_name_orig == "v511"))


trust_metadata <- trust_metadata %>%
  filter( ! (filename == "ZA6861_v1-2-0.sav" & var_name_orig == "qd5.6"))


exclusions <- trust_metadata %>%
  filter(val_label_norm %in% c("mentioned", "not_mentioned")) %>%
  print() %>%
  count(filename, var_name_orig)

trust_metadata <- trust_metadata %>%
  anti_join(exclusions)

val_labels_trust <- trust_metadata %>%
  count(val_label_norm)

trust_vocabulary <- tibble::tibble (
  # maybe we can use a generic controlled vocabulary, for example
  # Library of Congress
  topic_1 = 'trust institutions',
  # And if we find them, we can add GESIS or TNS/Kantar keywords here
  topic_2 = 'trust, binary',
  val_label_norm = val_labels_trust %>% pull(val_label_norm),
  level = 3 # missingness should be harmonized in character form
)

trust_values_table <- trust_vocabulary %>%
  mutate(character_value = case_when(
      # and create a surely harmonized character representation
      grepl("dk|inap|na", val_label_norm) ~ NA_character_,
      substr(val_label_norm, 1,7) == "tend_to" ~ "tend_to_trust",
      substr(val_label_norm, 1,11) == "tend_not_to" ~ "tend_not_to_trust",
    TRUE ~ "ERROR"),
    numeric_value = case_when (
      character_value == "tend_to_trust" ~ 1,
      character_value == "tend_not_to_trust" ~ 0,
      TRUE ~ NA_real_),
    missing = case_when (
      # it is useful for faster filtering of missingness
      # and true value labels
      is.na(numeric_value) ~ TRUE,
      TRUE ~ FALSE)
  ) %>%
  arrange(numeric_value)


trust_variable_table <- trust_metadata %>%
  filter ( val_label_norm %in% trust_values_table$val_label_norm ) %>%
  filter ( ! grepl("_recoded", var_label_norm ) ) %>%
  mutate ( institution = gsub("trust_in_institutions_|in_|_trust|trust_|[0-9]_|^a_|^b_|^q16c_|the_", "",
                              var_label_norm ),
           institution = gsub("charitable_org", "charities", institution ),
           institution = gsub("europ_court_of_auditors", "eu_court_of_auditors", institution ),
           institution = gsub("eur_court_of_auditors", "eu_court_of_auditors", institution ),
           institution = gsub("europ_court_of_justice", "eu_court_of_justice", institution ),
           institution = gsub("european_court_of_justice", "eu_court_of_justice", institution ),
           institution = gsub("justice_legal_system", "justice", institution ),
           institution = gsub("justice_nat_legal_system", "justice", institution ),
           institution = gsub("nat_parliament", "national_parliament", institution ),
           institution = gsub("nat_government", "national_government", institution ),
           institution = gsub("non_govmnt_org|non_govnmt_org", "ngo", institution ),
           institution = gsub("polit_parties", "political_parties", institution ),
           institution = gsub("reg_loc_public_authorities", "reg_loc_authorities", institution ),
           institution = gsub("reg_local_authorities", "reg_loc_authorities", institution ),
           institution = gsub("reg_local_public_authorities", "reg_loc_authorities", institution ),
           institution = gsub("rg_lc_authorities", "reg_loc_authorities", institution ),
           institution = gsub("written_press", "press", institution ),
           institution = gsub("econ_and_social_committee", "econ_and_soc_committee", institution ),
           institution = gsub("economic_and_soc_committee", "econ_and_soc_committee", institution )) %>%
  mutate ( geo_qualifier = case_when(
    grepl("_tcc", institution) ~ "tcc",
    TRUE ~ NA_character_),
    institution = gsub("_tcc", "", institution)
  ) %>%
  mutate ( var_name_suggested = paste0("trust_",
                                    institution, "_",
                                    geo_qualifier) ) %>%
  mutate ( var_name_suggested = gsub("_NA", "", var_name_suggested),
           topic_1 = "trust institutions",
           topic_2 = "trust, binary") %>%
  select ( -contains("label"),
           -val_numeric_orig, -val_order_alpha, -n_categories) %>%
  rename ( keyword_1 = institution ) %>%
  distinct_all() %>%
  mutate(var_name_suggested = ifelse(var_name_suggested == "trust_political_partiess_tcc",
                                    "trust_political_parties_tcc", var_name_suggested)) %>%
  arrange(var_name_suggested)