tickers <-  c("ECH", "EDEN", "EFNL", "EIDO", "EIRL", "EIS", "ENOR", "ENZL", 
"EPHE", "EPOL", "EPU", "ERUS", "EWA", "EWC", "EWD", "EWG", "EWH", 
"EWI", "EWJ", "EWK", "EWL", "EWM", "EWN", "EWO", "EWP", "EWQ", 
"EWS", "EWT", "EWU", "EWW", "EWY", "EWZ", "EZA", "FXI", "ICOL", 
"INDA", "KSA", "QAT", "THD", "TUR", "UAE")

name <-   c("Chile", "Denmark", "Finland", "Indonesia", "Ireland", "Israel", 
"Norway", "New Zealand", "Philippines", "Poland", "Peru", "Russia", 
"Australia", "Canada", "Sweden", "Germany", "Hong Kong", "Italy", 
"Japan", "Belgium", "Switzerland", "Malaysia", "Netherlands", 
"Austria", "Spain", "France", "Singapore", "Taiwan", "United Kingdom", 
"Mexico", "Korea", "Brazil", "South Africa", "China", "Colombia", 
"India", "Saudi Arabia", "Qatar", "Thailand", "Turkey", "United Arab Emirates"
)

ytd <-   c(15.82, 17.85, 14.29, 8.8, 13.53, 11.41, 1.51, 7.18, 11.79, 
32.2, 3.45, -5.95, 9.03, -0.13, 16.2, 12.45, 17.26, 9.78, 5.98, 
12.02, 14.13, 13.18, 17.36, 18.18, 21.76, 13.93, 16.61, 15.6, 
7.17, 16.58, 18.3, 12.93, 10.41, 9.18, 5.51, 19.69, -1.29, -2.26, 
7.52, 21.51, 2.85)


etf_ticker_country <- data_frame(tickers, name, ytd)

etf_ticker_country

# getSymbols is part of the 'quantmod' package.

library(quantmod)
library(purrr)

# Using getSymbols to import the ETF price histories will take a minute or two or 
# five - 42 time series is a lot of data. 

# Let select just the Adjusted prices of the ETFs and merge them into a list.
# We'll use map and the piper operator for that purpose. Again, this is for testing. It's not 
# going into production in our app.

etf_prices <- 
  getSymbols(etf_ticker_country$tickers, auto.assign = TRUE, warnings = FALSE) %>%
  # Let's use the map function to get just the Adjusted prices
  map(~Ad(get(.))) %>% 
  # Now use the reduce() function to combine to one xts object
  reduce(merge) %>% 
  # some name cleanup
  `colnames<-`(etf_ticker_country$name)

# Take a peek at the last 5 rows of each of the time series, 
# just to make sure it looks complete.

tail(etf_prices, n = 5)

library(rnaturalearth)

world <- ne_countries(type = "countries", returnclass = 'sf')

head(world[c("name", "gdp_md_est", "economy")], n = 6)

gdpPal <- colorQuantile("Blues", world$gdp_md_est, n = 20)


economyPopup <- paste0("<strong>Country: </strong>", 
                world$name, 
                "<br><strong>Market Stage: </strong>", 
                 world$economy)

leaf_world_economy <- leaflet(world) %>%
  addProviderTiles("CartoDB.Positron") %>% 
  setView(lng =  20, lat =  15, zoom = 2) %>%
      addPolygons(stroke = FALSE, smoothFactor = 0.2, fillOpacity = .7, color =
      ~gdpPal(gdp_md_est), layerId = ~name, popup = economyPopup)

leaf_world_economy

library(sf)

world_etf <- merge(world, etf_ticker_country, by = "name")

ytdPal <- colorQuantile("RdYlGn", world_etf$ytd, n = 20)

ytdPopup <- paste0("<strong>Country: </strong>", 
                world_etf$name,
                "<br><strong> Year-to-date: </strong>", 
                world_etf$ytd, "%")

leaf_world_etf <- leaflet(world_etf) %>%
  addProviderTiles("CartoDB.Positron") %>% 
  setView(lng =  20, lat =  15, zoom = 2) %>%
  addPolygons(stroke = FALSE, smoothFactor = 0.2, fillOpacity = .7,
              color =~ytdPal(ytd), layerId = ~tickers, popup = ytdPopup)

leaf_world_etf

save(leaf_world_etf, file = 'etfData.RDat')

# YAML header
title: "Global ETF Map"
runtime: shiny
output:
 flexdashboard::flex_dashboard:
  source_code: embed

# Load the data from the Notebook

load('etfData.RDat')


output$map1 <- renderLeaflet({
    leaf_world_etf
})

clickedCountry <- eventReactive(input$map1_shape_click, {
     return(input$map1_shape_click$id)
     })

etf <- getSymbols(as.character(clickedCountry()), auto.assign = FALSE)

dygraph(Ad(etf), main = clickedCountry())