R/prep_data.R

In manuelbetin/OECDHousingToolkit: Provide Rmarkdown template to generate chapters of the OECD housing toolkit and OECD Housing horizontal project country fiches

#' @title prepare datasets for figures
#' @description provide the dataset with the main variables
#' or the substitutes if variables ranking 1 are missing
#' @param mydata a dataframe with all the policy variables
#' columns and iso3 codes in rows.
#' @param ranking a dataframe with the ranking of the policy variables
#' @param ctry the iso3 code for the selected country
#' @param var_codes a dataframe with the the code of the policy variables
#' @return a list with data a tibble with the proper selection of
#' variables, var_codes the codes of the selected vars and
#' var_names the names of the selected vars
#' @author Manuel Betin, Federica Depace

prep_data=function(mydata,ranking,ctry,var_codes,type_var){
  # reshape the variables
  dt_non_na<-mydata %>%
    filter(Iso_code3==ctry)  %>%
    select(Iso_code3, var_codes) %>%
    gather(variable , value,  var_codes) %>% filter(!is.na(value)) %>%
    merge(ranking,# %>% dplyr::select(variable,rank),
          by="variable") %>%
    filter(!is.na(value))

  #select relevant variables according to availability
  n_rank1 <- nrow(dt_non_na[with( dt_non_na,which(rank==1 )), ])
  n_rank2 <- nrow(dt_non_na[with( dt_non_na,which(rank==2 )), ])
  n_rank3 <- nrow(dt_non_na[with( dt_non_na,which(rank==3 )), ])

  dt_non_na=get_vars(n_rank1, n_rank2, n_rank3, dt_non_na,type_var) %>% filter(!is.na(variable))

  #store final variable codes and names
  var_codes=dt_non_na$variable
  var_names=dt_non_na$variable_name
  var_names_long=dt_non_na$variable_name_long
  var_direction = dt_non_na$direction

  # keep only relevant variables in the dataset
  vars_needed=mydata %>%
    select(Iso_code3, var_codes) %>%
    data.frame()

  # transform in numeric
  vars_needed[var_codes] <- sapply(vars_needed[var_codes],as.numeric)

  #compute OECD average
  OECD_mean <-vars_needed %>%
    summarise_at(.vars = var_codes,.funs=list(~mean(.,na.rm=T)))%>%
    mutate(Iso_code3="OECD")%>%
    arrange(Iso_code3, "") %>%
    select(Iso_code3, everything())

  # Aggregate OECD average to the dataset
  vars_needed<-rbind(vars_needed, OECD_mean)

  #compute relevant statistics
  vars_needed_plus<- vars_needed  %>%
    mutate_at(vars(var_codes),.funs=list(mean=~mean(.,na.rm=T),
                                         min=~min(.,na.rm=T),
                                         max=~max(.,na.rm=T),
                                         rank=~percent_rank(.,na.rm=T)))

  return(list(data=vars_needed_plus,var_codes=var_codes,var_names=var_names,var_direction=var_direction,var_names_long=var_names_long))
}