Price 2 income vs Housing per capita (The Satisfaction of Cowgill)

price2income-vs-housingper1000-by-Country.R

library(rsdmx)
library(data.table)
library(hutils)
library(magrittr)
library(ggplot2)
library(ggrepel)
ISO3166 <- Census2016.spec:::ISO3166   # hughparsonage/Census2016.spec

to_name <- function(a3) {
  ISO3166[["name"]][match(a3, ISO3166[["alpha_3"]])]
}

testthat::expect_equal(to_name(c("AUS", "USA", "AUS")),
                       c("Australia", "United States of America", "Australia"))



price2income_ratios_since_1990 <-
  readSDMX("http://stats.oecd.org/restsdmx/sdmx.ashx/GetData/HOUSE_PRICES/AUS+AUT+BEL+CAN+CHL+CZE+DNK+EST+FIN+FRA+DEU+GRC+HUN+ISL+IRL+ISR+ITA+JPN+KOR+LVA+LUX+MEX+NLD+NZL+NOR+POL+PRT+SVK+SVN+ESP+SWE+CHE+TUR+GBR+USA+EA+OECD+BRA+CHN+COL+IND+IDN+LTU+RUS+ZAF.HPI_YDH/all?startTime=1990&endTime=2015") %>%
  as.data.frame %>%
  as.data.table 

price2income_ratios_1990_2000_2015 <-
  price2income_ratios_since_1990 %>%
  .[obsTime %chin% c("1990", "2000", "2010", "2015")] %>%
  .[, Country := to_name(COU)] %>%
  .[complete.cases(.), .(Country, Year = obsTime, price2income = obsValue)] %>%
  .[Country == "United States of America", Country := "United States"] %>%
  .[Country == "United Kingdom of Great Britain and Northern Ireland", Country := "United Kingdom"] %>%
  .[Country == "Korea (Republic of)", Country := "Korea"] %>%
  .[Country == "Slovak Republic", Country := "Slovakia"] %>%
  .[Country == "Slovak Republic", Country := "Slovakia"] %>%
  setkey(Country, Year) %>%
  .[]

Houses_per_100 <- 
fread("
  1990 (1)	2000 (2)	2010 (3)	2015 (4)
  377	407	411	401
  440	482	530	525 
  358	453	527	546
  370	407	428	..
  249	281	280	290
  329	387	451	..
  378	352	487	510
  393	425	454	..
  437	453	463	464
  353	414	495	..
  441	487	531	536
  474	506	533	546
  ..	467	506	510
  ..	..	573	..
  371	398	441	449
  335	372	440	437
  441	479	526	..
  346	402	477	..
  ..	..	357	383
  ..	413	493	..
  310	373	437	445
  376	390	406	414
  340	478	539	..
  ..	..	314	..
  393	420	440	449
  339	353	339	398
  414	436	437	448
  294	327	350	363
  416	486	554	571
  ..	..	432	..
  ..	..	360	..
  ..	..	412	410
  ..	515	540	..
  474	482	485	476
  475	498	527	527
  408	431	439	436
  412	413	416	419", 
  sep = "\t",
  header = TRUE,
  na.strings = "..",
  colClasses = list(integer = 1:4)) %>%
  setnames(1:4, c("1990", "2000", "2010", "2015")) %>%
  .[, "1990" := as.integer(`1990`)] 

Countries <- fread(
   "Country
    Australia
    Austria
    Bulgaria
    Canada
    Chile
    Croatia
    Cyprus (a,b)
    Czech Republic
    Denmark
    Estonia
    Finland
    France
    Germany
    Greece
    Hungary
    Ireland
    Italy
    Japan
    Korea
    Latvia
    Lithuania
    Luxembourg
    Malta
    Mexico
    Netherlands
    New Zealand
    Norway
    Poland
    Portugal
    Romania
    Slovak Republic
    Slovenia
    Spain
    Sweden
    Switzerland
    United Kingdom
    United States", header = TRUE, sep = ",")

cbind(Countries,Houses_per_100)  %>%
  melt.data.table(id.vars = "Country",
                  variable.factor = FALSE,
                  variable.name = "Year",
                  value.name = "HousingPer1000") %>%
  .[Country %pin% "Cyprus", Country := "Cyprus"] %>%
  .[price2income_ratios_1990_2000_2015, on = c("Country", "Year"), nomatch=0L] %>%
  .[!is.na(HousingPer1000)] %>%
  .[, is_min_yr := min(Year, na.rm = TRUE), keyby = "Country"] %>%
  .[Year == is_min_yr, label := as.character(Country)] %>%
  
  ggplot(aes(x = HousingPer1000, y = price2income,
             group = Country, color = Country,
             label = label)) +
  geom_path(arrow = arrow(), na.rm = TRUE) + 
  scale_alpha_continuous(range = c(0.5, 1)) + 
  geom_text_repel(na.rm = TRUE) + 
  theme(legend.position = "none")