第二次写的均线数据

#包的下载
install.packages("RODBC") #数据包

#MYSQL链接
library(RODBC)
channel<-odbcConnect(dsn="mysql")
odbcGetInfo(channel=channel) #得到数据库信息
start_date<-"2015-01-01"
close_date<-as.character(Sys.Date()-1)
sql<-paste("select","Date,2_industry,todayConsume","from","industry2_consume","where","Date",">=","‘",start_date,"‘","and","Date","<","‘",close_date,"‘","and","2_departmentID=200","and","2_industry!=‘其他‘",";",sep=" ")
raw_data<-sqlQuery(channel=channel,query=sql)

#数据消费处理

col_consuption<-raw_data[order(raw_data[,2]),]
matrix_consuption<-split(col_consuption$todayConsume,list(col_consuption[[2]]))
matrix_industry_result<-do.call(‘cbind‘,matrix_consuption)
frame_industry_result<-data.frame(matrix_industry_result)
#移动平均
ma<-function(cdata){
ldata<-cdata
ldata<-cbind(ldata,SMA(cdata,7))
ldata<-cbind(ldata,SMA(cdata,14))
ldata<-na.locf(ldata,fromLast=TRUE)
return(ldata)
}
result<-lapply(frame_industry_result,ma)
final_result<-NULL
for (i in 1:length(result)){
final_result<-cbind(final_result,result[[i]])
}

name_result<-NULL

for (i in 1:length(names(frame_industry_result))){
name_result<-c(name_result,paste(names(frame_industry_result)[i],c("","7天日均","14天日均消费"),sep=""))
}

dimnames(final_result)[[2]]<-list(name_result)[[1]]

length_date=max(col_consuption$Date)-min(col_consuption$Date)+1

Date<-col_consuption$Date[1:length_date]

final_result_Date<-cbind(final_result,as.character(Date))

list_names_result<-list(name_result)[[1]]
list_names_result[length(list(name_result)[[1]])+1]<-c("日期")
dimnames(final_result_Date)[[2]]<-list_names_result

write.csv(final_result_Date,file="D:/result.csv")