1975-2011年的數(shù)據(jù)中。

1）分別統(tǒng)計(jì)每年人口最多的國家是哪個(gè)?有多少

2）統(tǒng)計(jì)出各個(gè)國家的1975-2011年的平均人口增長率

3）統(tǒng)計(jì)每年人口最多的十個(gè)國家

4）統(tǒng)計(jì)出每年人口最少的十個(gè)國家

5）結(jié)合洲的語言的分類，請得出如下結(jié)果

? ?5.1）哪個(gè)洲的人口最多?哪個(gè)洲的人口最少?

? ?每個(gè)洲的前3個(gè)國家人口排名

? ?5.2）哪種語言的國家人口最多?

librery(xlsx)

data<-read.xlsx("urbanpop.xlsx",sheet_index=3)
i<-0

for(dt in data){
if(i==0){
i<-2
next}
else{
index<-which(dt == max(dt,na.rm=TRUE))
cat(as.character(data$country[index]),dt[index],"\n")

}

}

data$country[1]

(data$X2011[1]-data$X1975[1])^(1/(2011-1975))-1

paste(((data$X2011[1]-data$X1975[1])^(1/(2011-1975))-1)*100,"%",sep="")

for(i in 1:209){
cat(as.character(data$country[i]),"\t",paste(((data$X2011[i]-data$X1975[i])^(1/(2011-1975))-1)*100,"%",sep=""),"\n")

}

i<-0
year<-1975
for(dt in data){
if(i==0){
i<-2
next}
else{
countrys_id <- order(dt,decreasing=TRUE)[1:10]
cat(year,"\t")
for(index in countrys_id){
cat(as.character(data$country[index]),"\t")
}
year=year+1
cat("\n")

}

}

i<-0
year<-1975
for(dt in data){
if(i==0){
i<-2
next}
else{
countrys_id <- order(dt,decreasing=FALSE)[1:10]
cat(year,"\t")
for(index in countrys_id){
cat(as.character(data$country[index]),"\t")
}
year=year+1
cat("\n")

}

}

?

?

Asian<-c("Afghanistan", "Armenia", "Azerbaijan", "Bahrain", "Bhutan", "Cambodia", "Indonesia",
"Iran", "Iraq", "Israel", "Japan", "Kazakhstan", "Kuwait", "Malaysia", "Myanmar", "Nepal", "Oman",
"Pakistan", "Qatar", "Saudi Arabia", "Singapore", "Tajikistan", "Thailand", "Turkmenistan", "Uzbekistan", "Yemen",
"Bangladesh", "Georgia", "India", "Jordan", "North Korea", "South Korea", "Lao", "Lebanon", "Maldives", "Mongolia",
"Philippines", "Sri Lanka", "Timor-Leste", "Turkey", "United Arab Emirates","Brunei", "China", "Hong Kong, China",
"Kyrgyz Republic", "Macao, China", "Syria", "Vietnam")

Europe<-c("Albania", "Austria", "Belgium", "Bosnia and Herzegovina", "Bulgaria", "Croatia",
"Cyprus", "Czech Republic", "Denmark", "Estonia", "France", "Germany", "Greece", "Hungary", "Latvia",
"Liechtenstein", "Lithuania", "Malta", "Netherlands", "Norway", "Portugal", "Russia", "Serbia", "Slovenia", "Sweden", "Ukraine",
"Andorra","Channel Islands", "Faeroe Islands", "Finland", "Iceland", "Ireland", "Isle of Man", "Italy", "Luxembourg", "Macedonia, FYR",
"Moldova", "Monaco", "Montenegro", "Poland", "Romania", "San Marino", "Slovak Republic", "Spain", "Switzerland", "United Kingdom")

Afrain<-c("Algeria", "Angola", "Benin", "Botswana", "Burkina Faso", "Burundi", "Chad", "Comoros",
"Cote d'Ivoire", "Djibouti", "Eritrea", "Ethiopia", "Guinea", "Kenya", "Lesotho", "Liberia", "Libya",
"Mauritania", "Mauritius", "Mozambique", "Namibia", "Niger", "Rwanda", "Sao Tome and Principe", "Seychelles",
"Sierra Leone", "Swaziland", "Tanzania", "Uganda", "Zambia", "Zimbabwe", "South Sudan","Cameroon",
"Central African Republic", "Egypt", "Equatorial Guinea", "Gabon", "Gambia", "Ghana", "Guinea-Bissau",
"Madagascar", "Malawi", "Mali", "Morocco", "Nigeria", "Senegal", "Somalia", "South Africa", "Sudan", "Togo","Tunisia",
"Cape Verde", "Congo, Dem. Rep.", "Congo, Rep.")

SouthAmerican<-c("Argentina", "Guyana", "Paraguay", "Peru", "Suriname", "Uruguay", "Venezuela","Brazil", "Chile",
"Colombia", "Ecuador","Aruba","Belarus","Bolivia")

NorthAmerican<-c("Antigua and Barbuda", "Bahamas", "Barbados", "Canada", "Greenland", "Grenada",
"Guatemala", "Honduras", "Jamaica", "Nicaragua", "St. Kitts and Nevis", "Trinidad and Tobago","Belize",
"Bermuda", "Cayman Islands", "Costa Rica", "Cuba", "Dominica", "Dominican Republic", "El Salvador",
"Haiti", "Mexico", "Panama", "Puerto Rico", "St. Lucia", "St. Vincent and the Grenadines", "Turks and Caicos Islands",
"United States", "Virgin Islands (U.S.)")

Oceania<-c("Australia", "Kiribati", "New Caledonia", "New Zealand", "Palau", "Papua New Guinea", "Solomon Islands", "Tuvalu",
"American Samoa", "Fiji", "French Polynesia", "Guam", "Marshall Islands", "Northern Mariana Islands", "Samoa", "Tonga", "Vanuatu",
"Micronesia, Fed. Sts.")

?

AS_number<-0
AF_number<-0
EU_number<-0
SA_number<-0
NA_number<-0
OC_number<-0
other_number<-0
index<-1
for(country in data$country){
if(country %in% Asian){
AS_number= AS_number+data$X2011[index]
}else if(country %in% Europe){
EU_number = EU_number+data$X2011[index]
}else if(country %in% Afrain){
AF_number= AF_number+data$X2011[index]
}else if(country %in% SouthAmerican){
SA_number= SA_number+data$X2011[index]
}else if(country %in% NorthAmerican){
NA_number= NA_number+data$X2011[index]
}else if(country %in% Oceania){
OC_number= OC_number+data$X2011[index]
}else{
other_number= other_number +data$X2011[index]
}
index=index+1
}

cat("亞洲人口數(shù)","歐洲人口數(shù)","北美洲人口數(shù)","南美洲人口數(shù)","非洲人口數(shù)","大洋洲人口數(shù)","\n")
population<-c(AS_number,EU_number,NA_number,SA_number,AF_number,OC_number)
sort_pl<-order(population)
sort_pl

AS<-c()
AF<-c()
EU<-c()
SA<-c()
NAA<-c()
OC<-c()
AS_I<-c()
AF_I<-c()
EU_I<-c()
SA_I<-c()
NAA_I<-c()
OC_I<-c()
index<-1
dt_2011<-data$X2011
for(country in data$country){
if(country %in% Asian){
AS_I=c(AS_I,country)
AS=c(AS,dt_2011[index])
}else if(country %in% Europe){
EU_I=c(EU_I,country)
EU=c(EU,dt_2011[index])
}else if(country %in% Afrain){
AF_I=c(AF_I,country)
AF=c(AF,dt_2011[index])
}else if(country %in% SouthAmerican){
SA_I=c(SA_I,country)
SA=c(SA,dt_2011[index])
}else if(country %in% NorthAmerican){
NAA_I=c(NAA_I,country)
NAA=c(NAA,dt_2011[index])
}else if(country %in% Oceania){
OC_I=c(OC_I,country)
OC=c(OC,dt_2011[index])
}else{
print(country)
}
index=index+1
}
for(x in order(AS,decreasing=TRUE)[1:3]){
cat(AS_I[x],"\t","人口數(shù)",AS[x],"\n")
}
for(x in order(AF,decreasing=TRUE)[1:3]){
cat(AF_I[x],"\t","人口數(shù)",AF[x],"\n")
}
for(x in order(EU,decreasing=TRUE)[1:3]){
cat(EU_I[x],"\t","人口數(shù)",EU[x],"\n")
}
for(x in order(SA,decreasing=TRUE)[1:3]){
cat(SA_I[x],"\t","人口數(shù)",SA[x],"\n")
}
for(x in order(NAA,decreasing=TRUE)[1:3]){
cat(NAA_I[x],"\t","人口數(shù)",NAA[x],"\n")
}
for(x in order(OC,decreasing=TRUE)[1:3]){
cat(OC_I[x],"\t","人口數(shù)",OC[x],"\n")
}

?

沒想到?jīng)]有R語言的代碼貼士。這里面最麻煩的是第五題，數(shù)據(jù)要自己去爬，去了百度百科還有個(gè)data.cn的網(wǎng)站，爬，但是還剩下50幾個(gè)爬不出來，心里很難受。

說下注意的東西吧。1.是工作目錄得注意，不然讀取不到csv文件。

2.因?yàn)閲颐Q是以因子的形式讀取出來的，因此得使用as.character()來轉(zhuǎn)換一下。

感覺就這兩點(diǎn)東西需要注意，這東西不難，但是第五題太繁瑣。

轉(zhuǎn)載于:https://www.cnblogs.com/ke-T3022/p/7811057.html

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