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## # A tibble: 41 × 3
## tickers name ytd
## <chr> <chr> <dbl>
## 1 ECH Chile 15.82
## 2 EDEN Denmark 17.85
## 3 EFNL Finland 14.29
## 4 EIDO Indonesia 8.80
## 5 EIRL Ireland 13.53
## 6 EIS Israel 11.41
## 7 ENOR Norway 1.51
## 8 ENZL New Zealand 7.18
## 9 EPHE Philippines 11.79
## 10 EPOL Poland 32.20
## # ... with 31 more rows# 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)## Simple feature collection with 6 features and 3 fields
## geometry type: MULTIPOLYGON
## dimension: XY
## bbox: xmin: -73.41544 ymin: -55.25 xmax: 75.15803 ymax: 42.68825
## epsg (SRID): 4326
## proj4string: +proj=longlat +datum=WGS84 +no_defs
## name gdp_md_est economy
## 0 Afghanistan 22270 7. Least developed region
## 1 Angola 110300 7. Least developed region
## 2 Albania 21810 6. Developing region
## 3 United Arab Emirates 184300 6. Developing region
## 4 Argentina 573900 5. Emerging region: G20
## 5 Armenia 18770 6. Developing region
## geometry
## 0 MULTIPOLYGON(((61.210817091...
## 1 MULTIPOLYGON(((16.326528354...
## 2 MULTIPOLYGON(((20.590247430...
## 3 MULTIPOLYGON(((51.579518670...
## 4 MULTIPOLYGON(((-65.5 -55.2,...
## 5 MULTIPOLYGON(((43.582745802...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