1 Email or Phone, a choice to leave your social comfortable zone

Facebook, twitter and wechat provide an effective and comfortable social network for us to frequently interact with our family, friends and colleagues. In fact, we are not always living within these comformatble zones. When we move to a new country, new school, or even a new neighborhood, there must have some moments we need helps from others. How will you introduce yourself and request helps from a person you have never met before? Do you prefer to sending an email to write down all your thoughts clearly, or making a phone call to get an immediate response? The pros and cons of either choices varies with the specific situtations. For me, my first choice is always emails. My experience tells me never try to explain or discuss an important and complicated thing in a call. If we cannot make the listener understand our purpose in the first three minites, the call deems to be a failure. In this homework, I search the data from world bank and google trend to study whether emails are popular than telphones.

2 Part 1: Emails and Telephones in the United States

2.1 Which way is easiler to get a touch unacquainted people?

In figure 1, we can find the comparison of mobile cellular subscription per 100 people and the percentage of invididuals using internet on total population. From 2000 to 2017, we can find that the both lines have a upward trend, it implies that people more rely on phone and internet in their daily life. But we can find that, since 2011, the value of mobile cellular subscription per 100 people, which is shown as the blue dots, has been above 100, it indicates that each person is likely to obtain at least one telephone number. However, the percentage of website usage per person does not reach 100 percentage. The data indicates that calling a person is an easier way to communicate with them in the United States, since some people probably do not have an access to use internet.

3 Part 2: Comparisons of Website Search for Emails and Telephones

3.1 Western Countries: Canada, United States and Great Britain

In Figure3 a) and b), we can find there is no significant indifference between email address and telephones in U.K.. However, we can find that Canada has much higher website search on email address than telephone numbers from 2004 to 2020 (first 2 months).

Emailw= gtrends(c("Email address"),gprop="web", geo=c("US","CA","GB"),time="all")[[1]]
Emaile= gtrends(c("Email address"),gprop="web", geo=c("CN","KR","HK","JP"),time="all")[[1]]
Telw= gtrends(c("Telephone number"),gprop="web", geo=c("US","CA","GB"), time="all")[[1]]
Tele= gtrends(c("Telephone number"),gprop="web", geo=c("CN","KR","HK","JP"),time="all")[[1]]
#Change Monthly data into Yearly data
Emailw$year<-substring(Emailw$date,1,4)
Emaile$year<-substring(Emaile$date,1,4)
Telw$year<-substring(Telw$date,1,4)
Tele$year<-substring(Tele$date,1,4)
emailAnnualw<-Emailw%>%group_by(year,geo)%>% summarise(hits=sum(hits))
emailAnnuale<-Emaile%>%group_by(year,geo)%>% summarise(hits=sum(as.numeric(hits)))
telAnnualw<-Telw%>%group_by(year,geo)%>% summarise(hits=sum(hits))
telAnnuale<-Tele%>%group_by(year,geo)%>% summarise(hits=sum(as.numeric(hits)))
#Merge into Western and Eastern data
eAtAnnualw<-merge(emailAnnualw,telAnnualw, by=c("year", "geo"))
names(eAtAnnualw)[names(eAtAnnualw) == "hits.x"] <- "Email"
names(eAtAnnualw)[names(eAtAnnualw) == "hits.y"] <- "Tel"
eAtAnnualw1<-melt(eAtAnnualw,id.vars = c('year','geo'))
eAtAnnuale<-merge(emailAnnuale,telAnnuale, by=c("year", "geo"))
names(eAtAnnuale)[names(eAtAnnuale) == "hits.x"] <- "Email"
names(eAtAnnuale)[names(eAtAnnuale) == "hits.y"] <- "Tel"
eAtAnnuale1<-melt(eAtAnnuale,id.vars = c('year','geo'))


P1<-ggplot(emailAnnualw,aes(fill=geo,y=hits,x=year))+
  geom_bar(position="dodge",stat="identity")+
  labs(y="Search Interest",x="Year",title="Figure3 a).Website Search For Email Address")+
  scale_x_discrete(breaks=seq(2004,2020,2),expand=c(0,0))+
  theme_classic()+
  theme(axis.text.x=element_text(angle = 75))+
  theme(axis.ticks.x=element_blank())+
  theme(plot.title = element_text( face="bold",size=10, hjust=0.5))+
  theme(axis.title.x= element_text(face="bold",size=8,vjust=0.5, color="black"))+
  theme(axis.title.y= element_text(face="bold",size=8,vjust=0.5, color="black"))+
  theme(axis.ticks = element_blank(),axis.text.x = element_text(face="bold", color="black",size=8,margin=margin(t=8)),axis.text.y = element_text(face="bold",color="black",size=9))+
  theme(axis.line = element_line(size=1, color = "black"))+
  theme(legend.text= element_text(face="bold",size=8))+
  theme(legend.title = element_blank())+
  theme(plot.margin = unit(c(2,0.5,4,0.25),"cm"))+
  scale_fill_manual(values=c("#999999", "#E69F00", "#56B4E9"),
                    breaks=c("CA", "GB","US"),
                    labels=c("Canada", "Great Britain","United States"))

P2<-ggplot(telAnnualw,aes(fill=geo,y=hits,x=year))+
  geom_bar(position="dodge",stat="identity")+
  labs(y="Search Interest",x="Year",title="Figure 3 b).Website Search For Telephone Numbers")+
  scale_x_discrete(breaks=seq(2004,2020,2),expand=c(0,0))+
  theme_classic()+
  theme(axis.text.x=element_text(angle = 75))+
  theme(axis.ticks.x=element_blank())+
  theme(plot.title = element_text( face="bold",size=10, hjust=0.5))+
  theme(axis.title.x= element_text(face="bold",size=8,vjust=0.5, color="black"))+
  theme(axis.title.y= element_text(face="bold",size=8,vjust=0.5, color="black"))+
  theme(axis.ticks = element_blank(),axis.text.x = element_text(face="bold", color="black",size=8,margin=margin(t=8)),axis.text.y = element_text(face="bold",color="black",size=9))+
  theme(axis.line = element_line(size=1, color = "black"))+
  theme(legend.text= element_text(face="bold",size=8))+
  theme(legend.title = element_blank())+
  theme(plot.margin = unit(c(2,0.5,4,0.25),"cm"))+
  scale_fill_manual(values=c("#999999", "#E69F00", "#56B4E9"),
                    breaks=c("CA", "GB","US"),
                    labels=c("Canada", "Great Britain","United States"))

print(P1)

3.2 Aisan Countries and Regions: Japan, Korean, HongKong and Taiwan

I did not include the mainland China because google service is blocked, and the hits of website research cannot indicate the truth. In Figure 4 a) and b), we can find that HongKong has the highest hits of website searches on both email address and telephone numbers, while Japan is another extreme that very few search on either email addresses or telephone numbers. And both Korea and Taiwan, the figures show that the people would prefer to searching telephone numbers. This implies that people are more likely to use telephone to communicate with strangers in these two areas.

P3<-ggplot(emailAnnuale,aes(fill=geo,y=hits,x=year))+
  geom_bar(position="dodge",stat="identity")+
  labs(y="Search Interest",x="Year",title="Figure 4 a).Website Search For Email Address")+
  scale_x_discrete(breaks=seq(2004,2020,2),expand=c(0,0))+
  theme_classic()+
  theme(axis.text.x=element_text(angle = 75))+
  theme(axis.ticks.x=element_blank())+
  theme(plot.title = element_text( face="bold",size=10, hjust=0.5))+
  theme(axis.title.x= element_text(face="bold",size=8,vjust=0.5, color="black"))+
  theme(axis.title.y= element_text(face="bold",size=8,vjust=0.5, color="black"))+
  theme(axis.ticks = element_blank(),axis.text.x = element_text(face="bold", color="black",size=8,margin=margin(t=8)),axis.text.y = element_text(face="bold",color="black",size=9))+
  theme(axis.line = element_line(size=1, color = "black"))+
  theme(legend.text= element_text(face="bold",size=8))+
  theme(legend.title = element_blank())+
  theme(plot.margin = unit(c(2,0.5,4,0.25),"cm"))+
  scale_fill_manual(values=c("hotpink", "orange", "plum","green"),
                    breaks=c("CN", "HK","JP","KR"),
                    labels=c("Taiwan", "HongKong","Japan","Korea"))

P4<-ggplot(telAnnuale,aes(fill=geo,y=hits,x=year))+
  geom_bar(position="dodge",stat="identity")+
  labs(y="Search Interest",x="Year",title="Figure 4 b).Website Search For Telephone Numbers")+
  scale_x_discrete(breaks=seq(2004,2020,2),expand=c(0,0))+
  theme_classic()+
  theme(axis.text.x=element_text(angle = 75))+
  theme(axis.ticks.x=element_blank())+
  theme(plot.title = element_text( face="bold",size=10, hjust=0.5))+
  theme(axis.title.x= element_text(face="bold",size=8,vjust=0.5, color="black"))+
  theme(axis.title.y= element_text(face="bold",size=8,vjust=0.5, color="black"))+
  theme(axis.ticks = element_blank(),axis.text.x = element_text(face="bold", color="black",size=8,margin=margin(t=8)),axis.text.y = element_text(face="bold",color="black",size=9))+
  theme(axis.line = element_line(size=1, color = "black"))+
  theme(legend.text= element_text(face="bold",size=8))+
  theme(legend.title = element_blank())+
  theme(plot.margin = unit(c(2,0.5,4,0.25),"cm"))+
  scale_fill_manual(values=c("hotpink", "orange", "plum","green"),
                    breaks=c("CN", "HK","JP","KR"),
                    labels=c("Taiwan", "HongKong","Japan","Korea"))
  print(P3)