基本R功能用於加載和清理庫存數據

Question

我正在嘗試編寫一個函數來爲我用「getSymbols」加載的所有股票代碼執行以下任務。我試過使用lapply，但功能似乎沒有工作。

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

你們雖然在輸出提到的收盤價，而不是因爲它是調整公司行爲等 股票分割，分紅會建議使用 調整後的價格欄等

我已經使用了試業矢量，你需要用實際值來替換它們。

您可以使用new.env和lapply如下：

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) 

} 

輸出：

#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