#读取
WD<-getwd()
WD
## [1] "C:/Users/林/Desktop"
if(!is.null(WD)) setwd("/Users/林/Desktop/R作业/homework1")
getwd()
## [1] "C:/Users/林/Desktop/R作业/homework1"
Kenya<-read.csv("Kenya.csv")
Sweden<-read.csv("Sweden.csv")
World<-read.csv("World.csv")
#第一题
#函数
CBR <- function(x, y, z) {
a<-sum(x)
b<-sum(y)+sum(z)
CBR<-a/b
return(CBR)
}
#Kenya
##1950-1955
###总出生
Kenya_births1<-Kenya$births[Kenya$period=="1950-1955"]
###总人口
Kenya_men1<-Kenya$py.men[Kenya$period=="1950-1955"]
Kenya_women1<-Kenya$py.women[Kenya$period=="1950-1955"]
###CBR
Kenya_CBR1<-CBR(Kenya_births1,Kenya_men1,Kenya_women1)
##2005-2010
###总出生
Kenya_births2<-Kenya$births[Kenya$period=="2005-2010"]
###总人口
Kenya_men2<-Kenya$py.men[Kenya$period=="2005-2010"]
Kenya_women2<-Kenya$py.women[Kenya$period=="2005-2010"]
###CBR
Kenya_CBR2<-CBR(Kenya_births2,Kenya_men2,Kenya_women2)
kenya_CBR<-c(Kenya_CBR1,Kenya_CBR2)
kenya_CBR
## [1] 0.05209490 0.03851507
#Sweden
##1950-1955
###总出生
Sweden_births1<-Sweden$births[Sweden$period=="1950-1955"]
###总人口
Sweden_men1<-Sweden$py.men[Sweden$period=="1950-1955"]
Sweden_women1<-Sweden$py.women[Sweden$period=="1950-1955"]
###CBR
Sweden_CBR1<-CBR(Sweden_births1,Sweden_men1,Sweden_women1)
##2005-2010
###总出生
Sweden_births2<-Sweden$births[Sweden$period=="2005-2010"]
###总人口
Sweden_men2<-Sweden$py.men[Sweden$period=="2005-2010"]
Sweden_women2<-Sweden$py.women[Sweden$period=="2005-2010"]
###CBR
Sweden_CBR2<-CBR(Sweden_births2,Sweden_men2,Sweden_women2)
Sweden_CBR<-c(Sweden_CBR1,Sweden_CBR2)
Sweden_CBR
## [1] 0.01539614 0.01192554
#World
##1950-1955
###总出生
World_births1<-World$births[World$period=="1950-1955"]
###总人口
World_men1<-World$py.men[World$period=="1950-1955"]
World_women1<-World$py.women[World$period=="1950-1955"]
###CBR
World_CBR1<-CBR(World_births1,World_men1,World_women1)
##2005-2010
###总出生
World_births2<-World$births[World$period=="2005-2010"]
###总人口
World_men2<-World$py.men[World$period=="2005-2010"]
World_women2<-World$py.women[World$period=="2005-2010"]
###CBR
World_CBR2<-CBR(World_births2,World_men2,World_women2)
World_CBR<-c(World_CBR1,World_CBR2)
World_CBR
## [1] 0.03732863 0.02021593
#所观察到的规律:随着经济发展，出生率（CBR）越来越低，且越发达地区的减缓速率越低
#第二题
#Kenya
##1950-1955各年龄段生育率、总生育率
Kenya_ASFR1 <-c(c(Kenya_births1[c(4:10)])/c(Kenya_women1[c(4:10)]))
Kenya_ASFR1
## [1] 0.16884585 0.35596942 0.34657814 0.28946367 0.20644016 0.11193267 0.03905205
Kenya_TFR1 <-sum(Kenya_births1[c(4:10)])/sum(Kenya_women1[c(4:10)])
Kenya_TFR1
## [1] 0.2345367
##2005-2010各年龄段生育率、总生育率
Kenya_ASFR2 <-c(c(Kenya_births2[c(4:10)])/c(Kenya_women2[c(4:10)]))
Kenya_ASFR2
## [1] 0.10057087 0.23583536 0.23294721 0.18087964 0.13126805 0.05626214 0.03815044
Kenya_TFR2 <-sum(Kenya_births2[c(4:10)])/sum(Kenya_women2[c(4:10)])
Kenya_TFR2
## [1] 0.1583425
#Sweden
##1950-1955各年龄段生育率、总生育率
Sweden_ASFR1<-c(c(Sweden_births1[c(4:10)])/c(Sweden_women1[c(4:10)]))
Sweden_ASFR1
## [1] 0.038908952 0.127710883 0.125243665 0.087364159 0.048603771 0.016210186
## [7] 0.001341829
Sweden_TFR1 <-sum(Sweden_births1[c(4:10)])/sum(Sweden_women1[c(4:10)])
Sweden_TFR1
## [1] 0.0628168
##2005-2010各年龄段生育率、总生育率
Sweden_ASFR2<-c(c(Sweden_births2[c(4:10)])/c(Sweden_women2[c(4:10)]))
Sweden_ASFR2
## [1] 0.0059709097 0.0507320271 0.1162085625 0.1322744621 0.0625923991
## [6] 0.0121600765 0.0006143942
Sweden_TFR2 <-sum(Sweden_births2[c(4:10)])/sum(Sweden_women2[c(4:10)])
Sweden_TFR2
## [1] 0.0530123
#World
##1950-1955各年龄段生育率、总生育率
World_ASFR1<-c(c(World_births1[c(4:10)])/c(World_women1[c(4:10)]))
World_ASFR1
## [1] 0.09029521 0.23763370 0.25245229 0.20416410 0.13810534 0.06360832 0.01519064
World_TFR1 <-sum(World_births1[c(4:10)])/sum(World_women1[c(4:10)])
World_TFR1
## [1] 0.1517275
##2005-2010各年龄段生育率、总生育率
World_ASFR2<-c(c(World_births2[c(4:10)])/c(World_women2[c(4:10)]))
World_ASFR2
## [1] 0.048489719 0.151971307 0.146980966 0.093813813 0.046689639 0.016268995
## [7] 0.004510245
World_TFR2 <-sum(World_births2[c(4:10)])/sum(World_women2[c(4:10)])
World_TFR2
## [1] 0.0777116
#所观察到的规律:随着经济发展，ASFR与TFR均会降低，且越发达地区减缓速率越低；越发达地区生育年龄段越推迟
#第三题
#函数
CBR <- function(x,y,z) {
a=sum(x)
b=sum(y)+sum(z)
CBR=a/b
return(CBR)
}
#Kenya
##1950-1955
Kenya_deaths1 <-Kenya$deaths[Kenya$period=="1950-1955"]
Kenya_CDR1 <-CBR(Kenya_deaths1,Kenya_men1,Kenya_women1)
##2005-2010
Kenya_deaths2 <-Kenya$deaths[Kenya$period=="2005-2010"]
Kenya_CDR2 <-CBR(Kenya_deaths2,Kenya_men2,Kenya_women2)
Kenya_CDR<-c(Kenya_CDR1,Kenya_CDR2)
Kenya_CDR
## [1] 0.02396254 0.01038914
#Sweden
##1950-1955
Sweden_deaths1 <-Sweden$deaths[Sweden$period=="1950-1955"]
Sweden_CDR1 <-CBR(Sweden_deaths1,Sweden_men1,Sweden_women1)
##2005-2010
Sweden_deaths2 <-Sweden$deaths[Sweden$period=="2005-2010"]
Sweden_CDR2 <-CBR(Sweden_deaths2,Sweden_men2,Sweden_women2)
Sweden_CDR<-c(Sweden_CDR1,Sweden_CDR2)
Sweden_CDR
## [1] 0.009844842 0.009968455
#World
##1950-1955
World_deaths1 <-World$deaths[World$period=="1950-1955"]
World_CDR1 <-CBR(World_deaths1,World_men1,World_women1)
##2005-2010
World_deaths2 <-World$deaths[World$period=="2005-2010"]
World_CDR2 <-CBR(World_deaths2,World_men2,World_women2)
World_CDR<-c(World_CDR1,World_CDR2)
World_CDR
## [1] 0.019318929 0.008166083
#所观察到的规律:随着经济发展，CDR会降低，且越发达地区减缓速率越低；但瑞士似乎成为例外，被世界平均反超而且对比自身也有所升高
#第四题
#Kenya
##1950-1955
Kenya_ASFR1<-c(c(Kenya_deaths1)/c(c(Kenya_women1)+c(Kenya_men1)))
Kenya_ASFR1
## [1] 0.066826532 0.009321789 0.005972093 0.005869582 0.007651103 0.008838750
## [7] 0.009677594 0.010986891 0.012633744 0.014760408 0.018260395 0.024433007
## [13] 0.041996801 0.093683927 0.200016381
##2005-2010
Kenya_ASFR2<-c(c(Kenya_deaths2)/c(c(Kenya_women2)+c(Kenya_men2)))
Kenya_ASFR2
## [1] 0.020920755 0.002911301 0.002918895 0.002942986 0.003885368 0.006558131
## [7] 0.010603913 0.013881062 0.013474598 0.011288057 0.011152339 0.013898334
## [13] 0.025395531 0.061261551 0.158620510
#Sweden
##1950-1955
Sweden_ASFR1<-c(c(Sweden_deaths1)/c(c(Sweden_women1)+c(Sweden_men1)))
Sweden_ASFR1
## [1] 0.0047456697 0.0004320537 0.0004896406 0.0007431865 0.0010177339
## [6] 0.0011140910 0.0013343851 0.0017429491 0.0025095541 0.0039668755
## [11] 0.0063486410 0.0101672774 0.0214156644 0.0599823093 0.1678170255
##2005-2010
Sweden_ASFR2<-c(c(Sweden_deaths2)/c(c(Sweden_women2)+c(Sweden_men2)))
Sweden_ASFR2
## [1] 6.790712e-04 8.138094e-05 1.135496e-04 2.687775e-04 4.697344e-04
## [6] 4.941440e-04 5.057066e-04 6.689578e-04 1.039256e-03 1.769621e-03
## [11] 2.988715e-03 4.709913e-03 9.828772e-03 2.803963e-02 1.098892e-01
#World
##1950-1955
World_ASFR1<-c(c(World_deaths1)/c(c(World_women1)+c(World_men1)))
World_ASFR1
## [1] 0.054589755 0.005600412 0.004261869 0.004752908 0.005891020 0.006325420
## [7] 0.007132501 0.008534487 0.010572557 0.013459846 0.017335769 0.024265320
## [13] 0.042262017 0.086910343 0.184364978
##2005-2010
World_ASFR2<-c(c(World_deaths2)/c(c(World_women2)+c(World_men2)))
World_ASFR2
## [1] 0.012802492 0.001256903 0.001079067 0.001302818 0.001832602 0.002278500
## [7] 0.002623982 0.003031563 0.003753402 0.005085583 0.007126588 0.010477192
## [13] 0.020235894 0.047457519 0.120679385
#所观察到的规律:随着经济发展，ASFR会降低，且越发达地区减缓速率越低；随着年龄的增长，死亡率逐渐上升的趋势。尤其是在 60 岁以上年龄段，死亡率有较为明显的升高，80+ 年龄段的死亡率达到各时期中的较高水平。