calculate regressivity stats

[claireboyd]

calculate PRB, COD, PRD at the county, city level if we are able to run the geospatial join

code snippet

#' #' @param data dataframe of data
#' #' @param assessment_value_col string of assessment_value_col name
#' #' @param sale_price_col string of sale_price_col name
#' #' @return dataframe 
#' gen_iaao_stats <- function(data, assessment_value_col, sale_price_col) { 
#'   df_iaao <- data %>% 
#'     rename_at(vars(c(assessment_value_col, sale_price_col)), function(x) c('assessment_value','sale_price')) %>% 
#'     mutate(av_ratio = assessment_value/sale_price,
#'            log2 = log(2),
#'            count = 1) %>%
#'     mutate(cod = 100 * sum(abs(av_ratio - stats::median(av_ratio)))/(n() * stats::median(av_ratio)),
#'            prd = mean(av_ratio, na.rm = TRUE)/stats::weighted.mean(av_ratio, sale_price, na.rm = TRUE),
#'            prb_value = 0.50 * (assessment_value/median(av_ratio)) + 0.50 * sale_price,
#'            prb_ln_value = log(prb_value)/log(2),
#'            prb_pct_diff = (av_ratio - median(av_ratio))/median(av_ratio)
#'     )
#'   prb_model <- linear_reg() %>% 
#'     fit(prb_pct_diff ~ prb_ln_value,  data = df_iaao) %>%
#'     tidy(., conf.int = TRUE) 
#'   iaao_out <- list('cod' = c(unique(df_iaao$cod)) ,
#'                    'prd' = c(unique(df_iaao$prd)) ,
#'                    'prb' = c(prb_model %>% filter(term == 'prb_ln_value') %>% select(estimate) %>% distinct() %>% pull())) %>%
#'     as.data.frame()
#'   return(iaao_out)
#' }
#' 
#' test <- gen_iaao_stats(data = data_input, assessment_value_col = 'assessed_value', sale_price_col = 'sale_price')