Kenya<-getwd()
Kenya## [1] "/Users/lvyang/Documents/IR"if(!is.null(Kenya))
setwd("/Users/lvyang/Documents/IR")
getwd()## [1] "/Users/lvyang/Documents/IR"Kenya<-read.csv("Kenya.csv")
Sweden<-read.csv("Sweden.csv")
World<-read.csv("World.csv")
Kenya$age<-gsub("9-May","5-9",Kenya$age)
Kenya$age<-gsub("14-Oct","10-14",Kenya$age)
Sweden$age<-gsub("9-May","5-9",Kenya$age)
Sweden$age<-gsub("14-Oct","10-14",Kenya$age)
calculate_CBR<-function(birth,population){
CBR<-birth/population
return(CBR)
}
periods<-unique(Kenya$period)
CBR_Kenya<-numeric(length(periods))
for (i in seq_along(periods)) {
sub<-subset(Kenya,period==periods[i])
population<-sum(sub$py.men+sub$py.women)
birth<-sum(sub$births)
CBR_Kenya[i]<-birth/population
}
names(CBR_Kenya)<-periods
periods<-unique(Sweden$period)
CBR_Sweden<-numeric(length(periods))
for (i in seq_along(periods)) {
sub<-subset(Sweden,period==periods[i])
population<-sum(sub$py.men+sub$py.women)
birth<-sum(sub$births)
CBR_Sweden[i]<-birth/population
}
names(CBR_Sweden)<-periods
periods<-unique(World$period)
CBR_World<-numeric(length(periods))
for (i in seq_along(periods)) {
sub<-subset(World,period==periods[i])
population<-sum(sub$py.men+sub$py.women)
birth<-sum(sub$births)
CBR_World[i]<-birth/population
}
names(CBR_World)<-periods
CBR_Kenya## 1950-1955 2005-2010
## 0.05209490 0.03851507CBR_Sweden## 1950-1955 2005-2010
## 0.01539614 0.01192554CBR_World## 1950-1955 2005-2010
## 0.03732863 0.02021593CBR_vector_1<-c(CBR_Kenya)
print(CBR_vector_1)## 1950-1955 2005-2010
## 0.05209490 0.03851507CBR_vector_2<-c(CBR_Sweden)
print(CBR_vector_2)## 1950-1955 2005-2010
## 0.01539614 0.01192554CBR_vector_3<-c(CBR_World)
print(CBR_vector_3)## 1950-1955 2005-2010
## 0.03732863 0.02021593print("以肯尼亚为例的发展中国家出生率高于世界平均水平，出生率随着时间的发展而下降；
以瑞典为例的发达国家出生率低于世界平均水平，出生率随着时间的发展而下降;
全球的出生率总体随时间发展呈下降趋势")## [1] "以肯尼亚为例的发展中国家出生率高于世界平均水平，出生率随着时间的发展而下降；\n 以瑞典为例的发达国家出生率低于世界平均水平，出生率随着时间的发展而下降;\n 全球的出生率总体随时间发展呈下降趋势"##第二题
calculate_ASFR_TFR1_TFR2<- function(births,py.women) {
ASFR <- births / py.women
TFR1 <- sum(births[3:10]) / sum(py.women[3:10])
TFR2 <- sum(births[19:25]) / sum(py.women[19:25])
return(list(ASFR = ASFR,TFR1 = TFR1,TFR2 = TFR2))
}
ASFR_TFR1_TFR2_Kenya <- calculate_ASFR_TFR1_TFR2(Kenya$births,Kenya$py.women)
ASFR_TFR1_TFR2_Sweden <- calculate_ASFR_TFR1_TFR2(Sweden$births,Sweden$py.women)
ASFR_TFR1_TFR2_World <- calculate_ASFR_TFR1_TFR2(World$births,World$py.women)
print(ASFR_TFR1_TFR2_Kenya)## $ASFR
## [1] 0.00000000 0.00000000 0.00000000 0.16884585 0.35596942 0.34657814
## [7] 0.28946367 0.20644016 0.11193267 0.03905205 0.00000000 0.00000000
## [13] 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000
## [19] 0.10057087 0.23583536 0.23294721 0.18087964 0.13126805 0.05626214
## [25] 0.03815044 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000
##
## $TFR1
## [1] 0.1908144
##
## $TFR2
## [1] 0.1583425print(ASFR_TFR1_TFR2_Sweden)## $ASFR
## [1] 0.0000000000 0.0000000000 0.0000000000 0.0389089519 0.1277108826
## [6] 0.1252436647 0.0873641591 0.0486037714 0.0162101857 0.0013418290
## [11] 0.0000000000 0.0000000000 0.0000000000 0.0000000000 0.0000000000
## [16] 0.0000000000 0.0000000000 0.0000000000 0.0059709097 0.0507320271
## [21] 0.1162085625 0.1322744621 0.0625923991 0.0121600765 0.0006143942
## [26] 0.0000000000 0.0000000000 0.0000000000 0.0000000000 0.0000000000
##
## $TFR1
## [1] 0.05512657
##
## $TFR2
## [1] 0.0530123print(ASFR_TFR1_TFR2_World)## $ASFR
## [1] 0.000000000 0.000000000 0.000000000 0.090295213 0.237633702 0.252452289
## [7] 0.204164096 0.138105344 0.063608319 0.015190644 0.000000000 0.000000000
## [13] 0.000000000 0.000000000 0.000000000 0.000000000 0.000000000 0.000000000
## [19] 0.048489719 0.151971307 0.146980966 0.093813813 0.046689639 0.016268995
## [25] 0.004510245 0.000000000 0.000000000 0.000000000 0.000000000 0.000000000
##
## $TFR1
## [1] 0.1267327
##
## $TFR2
## [1] 0.0777116print("规律：肯尼亚的生育率集中在20-29岁，女性生育年龄较小;
瑞典的生育率集中在30-39岁，女性生育年龄较大;
以肯尼亚为例的发展中国家的总生育率高于全球总生育率;
以瑞典为例的发达国家的总生育率低于全球总生育率")## [1] "规律：肯尼亚的生育率集中在20-29岁，女性生育年龄较小;\n 瑞典的生育率集中在30-39岁，女性生育年龄较大;\n 以肯尼亚为例的发展中国家的总生育率高于全球总生育率;\n 以瑞典为例的发达国家的总生育率低于全球总生育率"##第三题
Kenya<-getwd()
Kenya## [1] "/Users/lvyang/Documents/IR"if(!is.null(Kenya))
setwd("/Users/lvyang/Documents/IR")
getwd()## [1] "/Users/lvyang/Documents/IR"Kenya<-read.csv("Kenya.csv")
Sweden<-read.csv("Sweden.csv")
World<-read.csv("World.csv")
Kenya$age<-gsub("9-May","5-9",Kenya$age)
Kenya$age<-gsub("14-Oct","10-14",Kenya$age)
Sweden$age<-gsub("9-May","5-9",Kenya$age)
Sweden$age<-gsub("14-Oct","10-14",Kenya$age)
calculate_CDR<-function(death,population){
CDR<-death/population
return(CDR)
}
periods<-unique(Kenya$period)
CDR_Kenya<-numeric(length(periods))
for (i in seq_along(periods)) {
sub<-subset(Kenya,period==periods[i])
population<-sum(sub$py.men+sub$py.women)
death<-sum(sub$deaths)
CDR_Kenya[i]<-death/population
}
names(CDR_Kenya)<-periods
periods<-unique(Sweden$period)
CDR_Sweden<-numeric(length(periods))
for (i in seq_along(periods)) {
sub<-subset(Sweden,period==periods[i])
population<-sum(sub$py.men+sub$py.women)
death<-sum(sub$deaths)
CDR_Sweden[i]<-death/population
}
names(CDR_Sweden)<-periods
periods<-unique(World$period)
CDR_World<-numeric(length(periods))
for (i in seq_along(periods)) {
sub<-subset(World,period==periods[i])
population<-sum(sub$py.men+sub$py.women)
death<-sum(sub$deaths)
CDR_World[i]<-death/population
}
names(CDR_World)<-periods
CDR_Kenya## 1950-1955 2005-2010
## 0.02396254 0.01038914CDR_Sweden## 1950-1955 2005-2010
## 0.009844842 0.009968455CDR_World## 1950-1955 2005-2010
## 0.019318929 0.008166083CDR_vector_1<-c(CDR_Kenya)
print(CDR_vector_1)## 1950-1955 2005-2010
## 0.02396254 0.01038914CDR_vector_2<-c(CDR_Sweden)
print(CDR_vector_2)## 1950-1955 2005-2010
## 0.009844842 0.009968455CDR_vector_3<-c(CDR_World)
print(CDR_vector_3)## 1950-1955 2005-2010
## 0.019318929 0.008166083print("全球的死亡率随着时代发展和社会进步有所下降，尤其是以肯尼亚为例的发展中国家;
以肯尼亚为例的发展中国家死亡率高于全球平均水平;
以瑞典为例的发达国家死亡率低于全球平均水平")## [1] "全球的死亡率随着时代发展和社会进步有所下降，尤其是以肯尼亚为例的发展中国家;\n 以肯尼亚为例的发展中国家死亡率高于全球平均水平;\n 以瑞典为例的发达国家死亡率低于全球平均水平"##第四题
calculate_ASDR<- function(deaths,total_population_age_group) {
ASDR <- deaths / total_population_age_group
return(ASDR)}
ASDR_Kenya <- calculate_ASDR(Kenya$deaths,sum(Kenya$py.men+Kenya$py.women))
print(ASDR_Kenya)## [1] 1.802340e-03 1.665126e-04 8.832648e-05 8.374726e-05 9.665687e-05
## [6] 9.241701e-05 8.587850e-05 8.531289e-05 8.733553e-05 8.999618e-05
## [11] 9.591930e-05 1.058108e-04 2.491241e-04 2.411965e-04 1.104986e-04
## [16] 2.992109e-03 3.471747e-04 2.992648e-04 2.825407e-04 3.410660e-04
## [21] 4.767730e-04 6.003531e-04 5.937422e-04 4.475012e-04 3.068214e-04
## [26] 2.497938e-04 2.362733e-04 5.057732e-04 6.765166e-04 5.241896e-04ASDR_Sweden <- calculate_ASDR(Sweden$deaths,sum(Sweden$py.men+Sweden$py.women))
print(ASDR_Sweden)## [1] 1.690582e-04 1.599083e-05 1.493460e-05 1.941743e-05 2.780586e-05
## [6] 3.543282e-05 4.433709e-05 5.777331e-05 8.362638e-05 1.230016e-04
## [11] 1.747078e-04 2.439399e-04 8.091580e-04 1.307576e-03 1.177438e-03
## [16] 2.211942e-05 2.345813e-06 3.930157e-06 1.021841e-05 1.618733e-05
## [21] 1.662948e-05 1.845946e-05 2.646715e-05 4.097189e-05 6.521605e-05
## [26] 1.055002e-04 1.776677e-04 6.335905e-04 1.134649e-03 3.336261e-03ASDR_World <- calculate_ASDR(World$deaths,sum(World$py.men+World$py.women))
print(ASDR_World)## [1] 2.175589e-03 1.713275e-04 1.194466e-04 1.254945e-04 1.431038e-04
## [6] 1.382786e-04 1.336173e-04 1.480047e-04 1.720649e-04 1.912321e-04
## [11] 2.085586e-04 2.390848e-04 5.961701e-04 5.746636e-04 3.086474e-04
## [16] 8.629661e-04 8.085952e-05 6.989588e-05 8.701459e-05 1.162005e-04
## [21] 1.290575e-04 1.403050e-04 1.557712e-04 1.767451e-04 2.089614e-04
## [26] 2.572892e-04 3.105749e-04 8.236224e-04 1.176893e-03 1.268213e-03print("老年人和婴幼儿死亡率较高，青壮年人群死亡率较低；
肯尼亚婴幼儿死亡率有所降低；
瑞典老年人群死亡率上升")## [1] "老年人和婴幼儿死亡率较高，青壮年人群死亡率较低；\n 肯尼亚婴幼儿死亡率有所降低；\n 瑞典老年人群死亡率上升"