library(gtrendsR)
library(reshape2)
library(ggplot2)
library(wbstats)

What the Heaven Happened to Bhutan?

gdp_per_capita <- wb_data(country = c("BTN", "IND", "NPL", "MMR", "BGD"), indicator = "NY.GDP.PCAP.CD")
gdp_per_capita$Country <- gdp_per_capita$country

ggdp<-ggplot(gdp_per_capita, aes(x=as.numeric(date), y=NY.GDP.PCAP.CD, color= Country)) + 
  geom_line() + ggtitle("GDP per capita (US$)")+
  theme(plot.title = element_text(size = 11, face = "bold")) +
  theme( plot.title = element_text(hjust = 0.5)) +
  ylab("GDP per capita") + xlab("Year") +
  theme(axis.title = element_text(size = 8))+ theme_classic()
ggdp

Covid-19 and Travel

Did Covid-19 affect leisurely travel? Although a causal link cannot be maintained yet without a rigorous econometric analysis, we can see the correlations between Covid-19 spike and the ensuing policies, and leisurely travel. For this, I use the trend in the number of hits on the keyword “airbnb” on Google Trend over the last five years as a proxy for travel behavior. I do this for the United States, Germany, South Korea, and India. We can observe that the no. of hits for the keyword decreases with the spike. While the hits resurged for the US, Germany and India as soon as the Covid situation got better, the travel plans for South Koreans hasn’t gone back to the pre-Covid levels yet.

airbnb<-gtrends(c('airbnb'),gprop = "web", time = "today+5-y",geo='US')
trend<-airbnb$interest_over_time
plot(trend[,'date'],trend[,'hits'],main='Google Search count for Airbnb in the United States (2017-22)',xlab='Date',ylab='No. of searches',type='l')

airbnb<-gtrends(c('airbnb'),gprop = "web", time = "today+5-y",geo='DE')
trend<-airbnb$interest_over_time
plot(trend[,'date'],trend[,'hits'],main='Google Search count for Airbnb in Germany (2017-22)',xlab='Date',ylab='No. of searches',type='l')

airbnb<-gtrends(c('airbnb'),gprop = "web", time = "today+5-y",geo='KR')
trend<-airbnb$interest_over_time
plot(trend[,'date'],trend[,'hits'],main='Google Search count for Airbnb in South Korea (2017-22)',xlab='Date',ylab='No. of searches',type='l')

airbnb<-gtrends(c('airbnb'),gprop = "web", time = "today+5-y",geo='IN')
trend<-airbnb$interest_over_time
plot(trend[,'date'],trend[,'hits'],main='Google Search count for Airbnb in India (2017-22)',xlab='Date',ylab='No. of searches',type='l')

Power of the Internet

Something very interesting. Google searches for “VPN” increased by around 400 percent in Russia since the start of this year, specifically since the 2022 Russian invasion of Ukraine. It only shows how much the general public really wants a free access to information despite the government control. Nonetheless, the maximum number of hits per day is 100, which is still not as high as I expected for a country that humongous.

vpn<-gtrends(c('vpn'),gprop = "web", time = "today+5-y",geo='RU')
trend<-vpn$interest_over_time
plot(trend[,'date'],trend[,'hits'],main='Google Search count for VPN in Russia (2017-22)',xlab='Date',ylab='No. of searches',type='l')