基本Rストックデータの読み込みおよびクレンジング機能

Question

"getSymbols"で読み込んでいるすべての株価テスターのために、次のタスクを実行する関数を記述しようとしています。私はlapplyを使用しようとしましたが、機能は動作していないようです。

library(quantmod) 
getSymbols(c("XLF","VFH","XLI","VIS","RWO","IYR","VNQI","VGT","RYT","VPU","IDU"), src = "yahoo",from="2012-01-01") 

#NEED TO FIGURE OUT A FUNCTION FOR THIS 
XLF = as.data.frame(XLF) 
XLF$date = row.names(XLF) 
XLI[,c("XLI.Open","XLI.High", "XLI.Low", "XLI.Adjusted")] = NULL 
XLI["ticker"]="XLI" 
XLI["industry"]="industrials" 
colnames(XLI) <- c("date","close","volume","ticker","industry") 

Answer 1

あなたの出力に近い価格を記載しているもののに、それは企業活動など 株式分割のために調整されているので、代わりに 調整価格の列を使用することをお勧めします、配当金など

私は、テスト業界を使用していました実際の値に置き換える必要があります。

あなたは new.envを使用することができます

とlapply次のように：

library(quantmod) 


tickerVec = c("XLF","VFH","XLI","VIS","RWO","IYR","VNQI","VGT","RYT","VPU","IDU") 

#test industry vector, replace with actual sector names 
industryVec = c("industrials","financials","materials","energy", 
      "materials","energy","financials","technology","industrials","technology","energy") 


startDt = as.Date("2012-01-01") 

#create new data environment for storing all price timeseries 

data.env = new.env() 

getSymbols(tickerVec,env=data.env,src = "yahoo",from=startDt)  


#convert to list class for ease in manipulation 

data.env.lst = as.list(data.env) 

#create an anoynmous function to reshape timeseries into required shape 

fn_modifyData = function(x) { 

TS = data.env.lst[[x]] 

#xts to data.frame 
TS_DF = data.frame(date=as.Date(index(TS)),coredata(TS),stringsAsFactors=FALSE) 

#retain only required columns 
TS_DF = TS_DF[,c(1,5,6)] 

TS_DF$ticker = tickerVec[x] 
TS_DF$industry = industryVec[x] 
colnames(TS_DF) = c("date","close","volume","ticker","industry") 
row.names(TS_DF) = NULL 

return(TS_DF) 

} 

出力：

#apply function to all timeseries using lapply 
outList = lapply(1:length(data.env.lst),function(z) fn_modifyData(z)) 


head(outList[[1]]) 
#  date close volume ticker industry 
#1 2012-01-03 13.34 103362000 XLF industrials 
#2 2012-01-04 13.30 69833900 XLF industrials 
#3 2012-01-05 13.48 89935300 XLF industrials 
#4 2012-01-06 13.40 83878600 XLF industrials 
#5 2012-01-09 13.47 69189600 XLF industrials 
#6 2012-01-10 13.71 86035100 XLF industrials 
head(outList[[11]]) 
#  date close volume ticker industry 
#1 2012-01-03 50.55 6100 IDU energy 
#2 2012-01-04 50.41 2700 IDU energy 
#3 2012-01-05 50.83 1700 IDU energy 
#4 2012-01-06 50.82 7700 IDU energy 
#5 2012-01-09 51.25 1800 IDU energy 
#6 2012-01-10 51.71 5500 IDU energy 


#if you wish to combine all datasets 
outDF = do.call(rbind,outList) 

head(outDF) 
#  date close volume ticker industry 
#1 2012-01-03 13.34 103362000 XLF industrials 
#2 2012-01-04 13.30 69833900 XLF industrials 
#3 2012-01-05 13.48 89935300 XLF industrials 
#4 2012-01-06 13.40 83878600 XLF industrials 
#5 2012-01-09 13.47 69189600 XLF industrials 
#6 2012-01-10 13.71 86035100 XLF industrials