# Let's build a dataframe to store these ticker symbols, country names and YTD numbers.
library(dplyr)

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

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

ytd <- c(0.0358, 0.6314, -0.0140,  0.0721, -0.0289, 0.2198,  0.0729,  0.2029, -0.0467,  0.0897,
         0.0944, -0.1045, -0.0623, -0.0916,  0.0305, -0.1857,  0.1309,  0.3828,  0.1987,  0.1219,
         0.2458, -0.1053, -0.0052, -0.0199, -0.0410,  0.6015,  0.0017, -0.0481, -0.0408,  0.1394,
         -0.1183, -0.0428, -0.0432,  0.0462, -0.1078, -0.0244, 0.0570,  0.6397, -0.0146,  0.1424,
         0.1313,  0.0751) * 100

etf_ticker_country <- data_frame(ticker, name, ytd)

etf_ticker_country
## # A tibble: 42 × 3
##    ticker           name   ytd
##     <chr>          <chr> <dbl>
## 1     EWJ          Japan  3.58
## 2     EWZ         Brazil 63.14
## 3    INDA          India -1.40
## 4     FXI          China  7.21
## 5     EWG        Germany -2.89
## 6     EWC         Canada 21.98
## 7     EWY          Korea  7.29
## 8     EWT         Taiwan 20.29
## 9     EWU United Kingdom -4.67
## 10    EWH      Hong Kong  8.97
## # ... with 32 more rows