gistfile1.txt

pop <- read.csv("pop_tots.csv", stringsAsFactors = F)
pop <- melt(pop, id=c("Country.Name","Country")) #transforms 1970-2015 columns to year variable
str_sub(pop$variable, 1,1)  <- '' #remove appended character added to all rows when transforming

passenger <- read.csv("passenger_tots.csv", stringsAsFactors = F)
passenger <- melt(passenger, id=c("Country.Name","Country")) #transforms 1970-2015 columns to year variable
str_sub(passenger$variable, 1,1)  <- '' #remove appended character added to all rows when transforming

gdp <- read.csv("gdp_totals.csv", stringsAsFactors = F)
gdp <- melt(gdp, id=c("Country.Name","Country")) #transforms 1970-2015 columns to year variable
str_sub(gdp$variable, 1,1)  <- '' #remove appended character added to all rows when transforming

country <- inner_join(pop,passenger, by = c("Country.Name","Country","variable")) #joining population, passenger dataframes
country <- inner_join(country,gdp, by = c("Country.Name","Country","variable")) # then joining with gdp
names(country) <- c("Country.Name","Country","Year",'Population','Passenger','GDP') #changing column names

country_subset$Year<- as.numeric(country_subset$Year) #converting year column to numeric for googlevis