I have many separate data files in csv format for a lot of daily stock prices. Over a few years there are hundreds of those data files, whose names are the dates of data record.
In each file there are variables of ticker (or stock trading code), date, open price, high price, low price, close price, and trading volume. For example, inside a data file named 20150128.txt it looks like this:
FB,20150128,1.075,1.075,0.97,0.97,725221
AAPL,20150128,2.24,2.24,2.2,2.24,63682
AMZN,20150128,0.4,0.415,0.4,0.415,194900
NFLX,20150128,50.19,50.21,50.19,50.19,761845
GOOGL,20150128,1.62,1.645,1.59,1.63,684835
...................and many more..................
In case it's relevant, the number of stocks in these files are not necessarily the same. I need to import and create 5 separate time series data frames from those files, one each for Open, High, Low, Close and Volume. In each data frame, rows are indexed by date, and columns by ticker.
What will be an efficient way to do that? I've used the following codes to read the files into a list of data frames but don't know what to do next from here.
files = list.files(pattern="*.txt")
mydata = lapply(files, read.csv,head=FALSE)