R/archive/make-acs-indicators.R

In tiernanmartin/NeighborhoodChangeTypology: Project: Neighborhood Change Typology for King County, WA

#' Make The Census Data (ACS) Indicators
#'
#' @param acs_data desc
#' @param acs_tables desc
#'
#' @return a `tibble`
#' @export
make_acs_indicators <- function(acs_data, acs_tables){

  # Join each acs variable its respective indicator

  all_vars <- tidycensus::load_variables(2016, "acs5", cache = TRUE) %>%
    dplyr::transmute(NAME = stringr::str_extract(name,".*(?=_\\d{3})"), # regex lookahead for '_001'
                     VARIABLE = name,
                     LABEL = label,
                     CONCEPT = concept)

  indicator_vars <- dplyr::inner_join(acs_tables, all_vars, by = "NAME")


  # FIGURE OUT THE PROPORTION INDICATORS

  race_vars_join <- indicator_vars %>%
    dplyr::filter(INDICATOR %in% "RACE") %>%
    dplyr::transmute(INDICATOR,
              TOPIC,
              VARIABLE,
              PROPORTION = dplyr::case_when(
                stringr::str_detect(LABEL, "Total$") ~ "DENOMINATOR",
                stringr::str_detect(LABEL, "Not Hispanic or Latino!!White alone") ~ "OMIT",
                stringr::str_detect(LABEL, "Latino$") ~ "OMIT",
                TRUE ~ "NUMERATOR"

              )
    )

  ed_vars_join <- indicator_vars %>%
    dplyr::filter(INDICATOR %in% "EDUCATION") %>%
    dplyr::transmute(INDICATOR,
                     TOPIC,
                     VARIABLE,
                     PROPORTION = dplyr::case_when(
                       stringr::str_detect(LABEL, "Total$") ~ "DENOMINATOR",
                       stringr::str_detect(LABEL, "No schooling") ~ "NUMERATOR",
                       stringr::str_detect(LABEL, "grade") ~ "NUMERATOR",
                       stringr::str_detect(LABEL, "High school") ~ "NUMERATOR",
                       stringr::str_detect(LABEL, "Some college") ~ "NUMERATOR",
                       stringr::str_detect(LABEL, "Associate's degree") ~ "NUMERATOR",
                       TRUE ~ "OMIT"

                     ))

  inc_vars_join <- indicator_vars %>%
    dplyr::filter(INDICATOR %in% "INCOME") %>%
    dplyr::transmute(INDICATOR,
                     TOPIC,
                     VARIABLE,
                     PROPORTION = dplyr::case_when(
                       stringr::str_detect(LABEL, "Total$") ~ "DENOMINATOR",
                       stringr::str_detect(LABEL, "Less") ~ "NUMERATOR",
                       stringr::str_detect(LABEL, "\\$14,999") ~ "NUMERATOR",
                       stringr::str_detect(LABEL, "\\$19,999") ~ "NUMERATOR",
                       stringr::str_detect(LABEL, "\\$24,999") ~ "NUMERATOR",
                       stringr::str_detect(LABEL, "\\$29,999") ~ "NUMERATOR",
                       stringr::str_detect(LABEL, "\\$34,999") ~ "NUMERATOR",
                       stringr::str_detect(LABEL, "\\$39,999") ~ "NUMERATOR",
                       stringr::str_detect(LABEL, "\\$44,999") ~ "NUMERATOR",
                       stringr::str_detect(LABEL, "\\$49,999") ~ "NUMERATOR",
                       stringr::str_detect(LABEL, "\\$59,999") ~ "NUMERATOR",
                       stringr::str_detect(LABEL, "\\$74,999") ~ "NUMERATOR",
                       TRUE ~ "OMIT"

                     ))

  tenure_vars_join <- indicator_vars %>%
    dplyr::filter(INDICATOR %in% "TENURE") %>%
    dplyr::transmute(INDICATOR,
                     TOPIC,
                     VARIABLE,
                     PROPORTION = dplyr::case_when(
                       stringr::str_detect(LABEL, "Total$") ~ "DENOMINATOR",
                       stringr::str_detect(LABEL, "Renter occupied$") ~ "NUMERATOR",
                       TRUE ~ "OMIT"

                     ))


  burden_own_vars_join <- indicator_vars %>%
    dplyr::filter(INDICATOR %in% "COST BURDEN") %>%
    dplyr::transmute(INDICATOR = "COST BURDEN OWN",
                     TOPIC,
                     VARIABLE,
                     PROPORTION = dplyr::case_when(
                       LABEL %in% "Estimate!!Total!!Owner-occupied housing units" ~ "DENOMINATOR",
                       stringr::str_detect(LABEL, "Owner") & stringr::str_detect(LABEL, "30 percent") ~ "NUMERATOR",
                       stringr::str_detect(LABEL, "Owner") & stringr::str_detect(LABEL, "Zero or negative income") ~ "NUMERATOR",
                       TRUE ~ "OMIT"

                     ))

  burden_rent_vars_join <- indicator_vars %>%
    dplyr::filter(INDICATOR %in% "COST BURDEN") %>%
    dplyr::transmute(INDICATOR = "COST BURDEN RENT",
                     TOPIC,
                     VARIABLE,
                     PROPORTION = dplyr::case_when(
                       LABEL %in% "Estimate!!Total!!Renter-occupied housing units" ~ "DENOMINATOR",
                       stringr::str_detect(LABEL, "Renter") & stringr::str_detect(LABEL, "30 percent") ~ "NUMERATOR",
                       stringr::str_detect(LABEL, "Renter") & stringr::str_detect(LABEL, "Zero or negative income") ~ "NUMERATOR",
                       TRUE ~ "OMIT"

                     ))

  vars_join <- list(race_vars_join,
                    ed_vars_join,
                    inc_vars_join,
                    tenure_vars_join,
                    burden_own_vars_join,
                    burden_rent_vars_join) %>%
    purrr::reduce(dplyr::bind_rows)


  indicator_values <- acs_data %>%
    dplyr::left_join(vars_join,  by = "VARIABLE") %>%
    dplyr::group_by(GEOID, ENDYEAR, INDICATOR, PROPORTION) %>%
    dplyr::summarise(ESTIMATE = sum(ESTIMATE, na.rm = TRUE),
                     MOE = tidycensus::moe_sum(moe = MOE, estimate = ESTIMATE, na.rm = TRUE)) %>%
    dplyr::ungroup() %>%
    dplyr::filter(!PROPORTION %in% "OMIT") %>%
    tidyr::gather(TYPE, VALUE, ESTIMATE, MOE) %>%
    tidyr::unite(PROP_TYPE, PROPORTION:TYPE) %>%
    tidyr::spread(PROP_TYPE, VALUE) %>%
    dplyr::group_by(GEOID, ENDYEAR, INDICATOR) %>%
    dplyr::summarise(PROPORTION = NUMERATOR_ESTIMATE/DENOMINATOR_ESTIMATE,
                     PROPORTION_MOE = tidycensus::moe_prop(
                       num = NUMERATOR_ESTIMATE,
                       denom = DENOMINATOR_ESTIMATE,
                       moe_num = NUMERATOR_MOE,
                       moe_denom = DENOMINATOR_MOE)
    ) %>%
    dplyr::ungroup()

  indicator_values_comparison <-
    indicator_values %>%
    dplyr::mutate(TYPE = dplyr::case_when(
      GEOID %in% "53033" ~ "COUNTY",
      TRUE ~ "TRACT"
    )) %>%
    tidyr::gather(VAL_TYPE, VALUE, tidyselect::matches("PROPORTION")) %>%
    tidyr::unite(GEOG_VAL, TYPE:VAL_TYPE) %>%
    tidyr::spread(GEOG_VAL, VALUE) %>%
    dplyr::group_by(INDICATOR, ENDYEAR) %>%
    dplyr::arrange(COUNTY_PROPORTION) %>%
    tidyr::fill(matches("COUNTY")) %>%
    dplyr::ungroup() %>%
    dplyr::mutate(GREATER_THAN_COUNTY = TRACT_PROPORTION >= COUNTY_PROPORTION)

  acs_indicators <- indicator_values_comparison

  return(acs_indicators)



}