library(openxlsx)

sample1<-read.xlsx("http://kanggc.iptime.org/book/data/cpi-e.xlsx")
weight<-read.xlsx("http://kanggc.iptime.org/book/data/cpi-weight-e.xlsx")

all<-ts(sample1$all, start=2015, end=2017, frequency=1)
fo<-ts(sample1$food, start=2015, end=2017, frequency=1)
al<-ts(sample1$alcohol, start=2015, end=2017, frequency=1)
cl<-ts(sample1$clothing, start=2015, end=2017, frequency=1)
ho<-ts(sample1$housing, start=2015, end=2017, frequency=1)
hou<-ts(sample1$household, start=2015, end=2017, frequency=1)
he<-ts(sample1$health, start=2015, end=2017, frequency=1)
tr<-ts(sample1$transport, start=2015, end=2017, frequency=1)
co<-ts(sample1$communication, start=2015, end=2017, frequency=1)
re<-ts(sample1$recreation, start=2015, end=2017, frequency=1)
ed<-ts(sample1$education, start=2015, end=2017, frequency=1)
res<-ts(sample1$restaurant, start=2015, end=2017, frequency=1)
ot<-ts(sample1$others, start=2015, end=2017, frequency=1)

w1<-weight[1,2]
w2<-weight[2,2]
w3<-weight[3,2]
w4<-weight[4,2]
w5<-weight[5,2]
w6<-weight[6,2]
w7<-weight[7,2]
w8<-weight[8,2]
w9<-weight[9,2]
w10<-weight[10,2]
w11<-weight[11,2]
w12<-weight[12,2]
w13<-weight[13,2]

z0<-as.matrix(cbind(all,fo,al,cl,ho,hou,he,tr,co,re,ed,res,ot))
z0
z1<-matrix(data=NA, nrow=2, ncol=12, byrow=T)
z1
w<-as.matrix(cbind(w1,w2,w3,w4,w5,w6,w7,w8,w9,w10,w11,w12,w13))

for(i in 1:2) {
  for(j in 1:12) {
    z1[i,j]<-(((z0[i+1,j+1]-z0[i,j+1])*w[j+1])/((z0[i+1,1]-z0[i,1])*w1))*100
       }  
}
(z1<-round(z1, digits=4))
(rowSums(z1))
z2<-matrix(data=NA, nrow=2, ncol=12, byrow=T)

for(i in 1:2) {
  for(j in 1:12) {
    z2[i,j]<-(z0[i+1,1]-z0[i,1])/(z0[i,1])*z1[i,j]
  }  
}
(z2<-round(z2, digits=4))
(rowSums(z2))