第一次作业

##数据读取

rm(list=ls())
WD<-getwd()
WD

## [1] "E:/homework1"

if(!is.null(WD)) setwd("E:\\homework1")
getwd()

## [1] "E:/homework1"

Kenya<-read.csv("Kenya.csv")
Sweden<-read.csv("Sweden.csv")
World<-read.csv("World.csv")

##第一题

###Kenya出生率计算
####Kenya总人口计算
Kenya$KenyaTotalPopulation<-Kenya$py.men+Kenya$py.women
####1950-1955年CBR计算
K_CBR1<-sum(Kenya$births[1:15])/sum(Kenya$KenyaTotalPopulation[1:15])
####2005-2010年CBR计算
K_CBR2<-sum(Kenya$births[16:30])/sum(Kenya$KenyaTotalPopulation[16:30])
###Sweden出生率计算
####Sweden总人口计算
Sweden$SwedenTotalPopulation<-Sweden$py.men+Sweden$py.women
####1950-1955年CBR计算
S_CBR1<-sum(Sweden$births[1:15])/sum(Sweden$SwedenTotalPopulation[1:15])
####2005-2010年CBR计算
S_CBR2<-sum(Sweden$births[16:30])/sum(Sweden$SwedenTotalPopulation[16:30])
###World出生率计算
####World总人口计算
World$WorldTotalPopulation<-World$py.men+World$py.women
####1950-1955年CBR计算
W_CBR1<-sum(World$births[1:15])/sum(World$WorldTotalPopulation[1:15])
####2005-2010年CBR计算
W_CBR2<-sum(World$births[16:30])/sum(World$WorldTotalPopulation[16:30])
###数据输出
K_CBR<-c(K_CBR1,K_CBR2)
S_CBR<-c(S_CBR1,S_CBR2)
W_CBR<-c(W_CBR1,W_CBR2)
K_CBR

## [1] 0.05209490 0.03851507

S_CBR

## [1] 0.01539614 0.01192554

W_CBR

## [1] 0.03732863 0.02021593

print("在同一时期，Kenya的出生率高于世界出生率，Sweden的出生率低于世界出生率；在不同时期，从1950-1955到2005-2010，Kenya,Sweden,World的出生率均下降")

## [1] "在同一时期，Kenya的出生率高于世界出生率，Sweden的出生率低于世界出生率；在不同时期，从1950-1955到2005-2010，Kenya,Sweden,World的出生率均下降"

##第二题

###各年龄组生育率计算
####Kenya1950-1955各年龄组生育率计算
for (i in 1:15) {
  K_ASFR1<-Kenya$births[i]/Kenya$py.women[i]
  print(K_ASFR1)
}

## [1] 0
## [1] 0
## [1] 0
## [1] 0.1688459
## [1] 0.3559694
## [1] 0.3465781
## [1] 0.2894637
## [1] 0.2064402
## [1] 0.1119327
## [1] 0.03905205
## [1] 0
## [1] 0
## [1] 0
## [1] 0
## [1] 0

####Kenya2005-2010各年龄组生育率计算
for (i in 16:30) {
  K_ASFR2<-Kenya$births[i]/Kenya$py.women[i]
  print(K_ASFR2)
}

## [1] 0
## [1] 0
## [1] 0
## [1] 0.1005709
## [1] 0.2358354
## [1] 0.2329472
## [1] 0.1808796
## [1] 0.131268
## [1] 0.05626214
## [1] 0.03815044
## [1] 0
## [1] 0
## [1] 0
## [1] 0
## [1] 0

####Sweden1950-1955
for (i in 1:15) {
  S_ASFR1<-Sweden$births[i]/Sweden$py.women[i]
  print(S_ASFR1)
}

## [1] 0
## [1] 0
## [1] 0
## [1] 0.03890895
## [1] 0.1277109
## [1] 0.1252437
## [1] 0.08736416
## [1] 0.04860377
## [1] 0.01621019
## [1] 0.001341829
## [1] 0
## [1] 0
## [1] 0
## [1] 0
## [1] 0

####Sweden2005-2010
for (i in 16:30) {
  S_ASFR2<-Sweden$births[i]/Sweden$py.women[i]
  print(S_ASFR2)
}

## [1] 0
## [1] 0
## [1] 0
## [1] 0.00597091
## [1] 0.05073203
## [1] 0.1162086
## [1] 0.1322745
## [1] 0.0625924
## [1] 0.01216008
## [1] 0.0006143942
## [1] 0
## [1] 0
## [1] 0
## [1] 0
## [1] 0

####World1950-1955
for (i in 1:15) {
  W_ASFR1<-World$births[i]/World$py.women[i]
  print(W_ASFR1)
}

## [1] 0
## [1] 0
## [1] 0
## [1] 0.09029521
## [1] 0.2376337
## [1] 0.2524523
## [1] 0.2041641
## [1] 0.1381053
## [1] 0.06360832
## [1] 0.01519064
## [1] 0
## [1] 0
## [1] 0
## [1] 0
## [1] 0

####World2005-2010
for (i in 16:30) {
  W_ASFR2<-World$births[i]/World$py.women[i]
  print(W_ASFR2)
}

## [1] 0
## [1] 0
## [1] 0
## [1] 0.04848972
## [1] 0.1519713
## [1] 0.146981
## [1] 0.09381381
## [1] 0.04668964
## [1] 0.01626899
## [1] 0.004510245
## [1] 0
## [1] 0
## [1] 0
## [1] 0
## [1] 0

###总生育率计算
####Kenya总生育率计算
K_TFR1<-sum(Kenya$births[4:10])/sum(Kenya$KenyaTotalPopulation[4:10])
K_TFR2<-sum(Kenya$births[19:25])/sum(Kenya$KenyaTotalPopulation[19:25])
K_TFR<-c(K_TFR1,K_TFR2)
####Sweden总生育率计算
S_TFR1<-sum(Sweden$births[4:10])/sum(Sweden$SwedenTotalPopulation[4:10])
S_TFR2<-sum(Sweden$births[19:25])/sum(Sweden$SwedenTotalPopulation[19:25])
S_TFR<-c(S_TFR1,S_TFR2)
####World总生育率计算
W_TFR1<-sum(World$births[4:10])/sum(World$WorldTotalPopulation[4:10])
W_TFR2<-sum(World$births[19:25])/sum(World$WorldTotalPopulation[19:25])
W_TFR<-c(W_TFR1,W_TFR2)
####TFR数据整合
TFR<-data.frame(
  country<-c("Kenya1950-1955","Kenya2005-2010","Sweden1950-1955","Sweden2005-2010","World1950-1955","World2005-2010"),
  TFR<-c(K_TFR,S_TFR,W_TFR)
)
TFR

##   country....c..Kenya1950.1955....Kenya2005.2010....Sweden1950.1955...
## 1                                                       Kenya1950-1955
## 2                                                       Kenya2005-2010
## 3                                                      Sweden1950-1955
## 4                                                      Sweden2005-2010
## 5                                                       World1950-1955
## 6                                                       World2005-2010
##   TFR....c.K_TFR..S_TFR..W_TFR.
## 1                    0.11294380
## 2                    0.07908350
## 3                    0.03116940
## 4                    0.02596623
## 5                    0.07563245
## 6                    0.03821785

##第3题

###死亡人数计算
####KenyaCDR计算
K_CDR1<-sum(Kenya$deaths[1:15])/sum(Kenya$KenyaTotalPopulation[1:15])
K_CDR2<-sum(Kenya$deaths[16:30])/sum(Kenya$KenyaTotalPopulation[16:30])
K_CDR<-c(K_CDR1,K_CDR2)
####SwedenCDR计算
S_CDR1<-sum(Sweden$deaths[1:15])/sum(Sweden$SwedenTotalPopulation[1:15])
S_CDR2<-sum(Sweden$deaths[16:30])/sum(Sweden$SwedenTotalPopulation[16:30])
S_CDR<-c(S_CDR1,S_CDR2)
####WorldCDR计算
W_CDR1<-sum(World$deaths[1:15])/sum(World$WorldTotalPopulation[1:15])
W_CDR2<-sum(World$deaths[16:30])/sum(World$WorldTotalPopulation[16:30])
W_CDR<-c(W_CDR1,W_CDR2)
###结果输出
print(K_CDR)

## [1] 0.02396254 0.01038914

print(S_CDR)

## [1] 0.009844842 0.009968455

print(W_CDR)

## [1] 0.019318929 0.008166083

##第4题

###各年龄组死亡率计算
####Kenya1950-1955各年龄组死亡率计算
for (i in 1:15) {
  K_ADFR1<-Kenya$deaths[i]/Kenya$KenyaTotalPopulation[i]
  print(K_ADFR1)
}

## [1] 0.06682653
## [1] 0.009321789
## [1] 0.005972093
## [1] 0.005869582
## [1] 0.007651103
## [1] 0.00883875
## [1] 0.009677594
## [1] 0.01098689
## [1] 0.01263374
## [1] 0.01476041
## [1] 0.0182604
## [1] 0.02443301
## [1] 0.0419968
## [1] 0.09368393
## [1] 0.2000164

####Kenya2005-2010各年龄组死亡率计算
for (i in 16:30) {
  K_ADFR2<-Kenya$deaths[i]/Kenya$KenyaTotalPopulation[i]
  print(K_ADFR2)
}

## [1] 0.02092075
## [1] 0.002911301
## [1] 0.002918895
## [1] 0.002942986
## [1] 0.003885368
## [1] 0.006558131
## [1] 0.01060391
## [1] 0.01388106
## [1] 0.0134746
## [1] 0.01128806
## [1] 0.01115234
## [1] 0.01389833
## [1] 0.02539553
## [1] 0.06126155
## [1] 0.1586205

#####Sweden1950-1955各年龄组死亡率计算
####Kenya1950-1955各年龄组生育率计算
for (i in 1:15) {
  S_ADFR1<-Sweden$deaths[i]/Sweden$SwedenTotalPopulation[i]
  print(S_ADFR1)
}

## [1] 0.00474567
## [1] 0.0004320537
## [1] 0.0004896406
## [1] 0.0007431865
## [1] 0.001017734
## [1] 0.001114091
## [1] 0.001334385
## [1] 0.001742949
## [1] 0.002509554
## [1] 0.003966876
## [1] 0.006348641
## [1] 0.01016728
## [1] 0.02141566
## [1] 0.05998231
## [1] 0.167817

#####Sweden2005-2010各年龄组死亡率计算
for (i in 16:30) {
  S_ADFR2<-Sweden$deaths[i]/Sweden$SwedenTotalPopulation[i]
  print(K_ADFR2)
}

## [1] 0.1586205
## [1] 0.1586205
## [1] 0.1586205
## [1] 0.1586205
## [1] 0.1586205
## [1] 0.1586205
## [1] 0.1586205
## [1] 0.1586205
## [1] 0.1586205
## [1] 0.1586205
## [1] 0.1586205
## [1] 0.1586205
## [1] 0.1586205
## [1] 0.1586205
## [1] 0.1586205

#####World各年龄组死亡率计算
####World1950-1955各年龄组生育率计算
for (i in 1:15) {
  W_ADFR1<-World$deaths[i]/World$WorldTotalPopulation[i]
  print(W_ADFR1)
}

## [1] 0.05458976
## [1] 0.005600412
## [1] 0.004261869
## [1] 0.004752908
## [1] 0.00589102
## [1] 0.00632542
## [1] 0.007132501
## [1] 0.008534487
## [1] 0.01057256
## [1] 0.01345985
## [1] 0.01733577
## [1] 0.02426532
## [1] 0.04226202
## [1] 0.08691034
## [1] 0.184365

#####World2005-2010各年龄组死亡率计算
for (i in 16:30) {
  W_ADFR2<-World$deaths[i]/World$WorldTotalPopulation[i]
  print(W_ADFR2)
}

## [1] 0.01280249
## [1] 0.001256903
## [1] 0.001079067
## [1] 0.001302818
## [1] 0.001832602
## [1] 0.0022785
## [1] 0.002623982
## [1] 0.003031563
## [1] 0.003753402
## [1] 0.005085583
## [1] 0.007126588
## [1] 0.01047719
## [1] 0.02023589
## [1] 0.04745752
## [1] 0.1206794

第一次作业

尹欣雨

2025-04-01