Hi Bert, I've attempted to find the answer and actually been able to import the individual data sets into a list of data frames.
But I'm not sure how to go ahead with the next step. I'm not necessarily asking for a final answer. Perhaps if you (I mean others as well) would like a constructive coaching, you would suggest a few key words to look at? Sorry for the HTML thing, this is my first post. I'll do better next times. Thanks, Nathan On Fri, Nov 15, 2019 at 11:34 AM Bert Gunter <bgunter.4...@gmail.com> wrote: > So you've made no attempt at all to do this for yourself?! > > That suggests to me that you need to spend time with some R tutorials. > > Also, please post in plain text on this plain text list. HTML can get > mangled, as it may have here. > > -- Bert > "The trouble with having an open mind is that people keep coming along and > sticking things into it." > -- Opus (aka Berkeley Breathed in his "Bloom County" comic strip ) > > > On Thu, Nov 14, 2019 at 4:11 PM Nhan La <lathanhn...@gmail.com> wrote: > >> 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 (so there will be missing data). 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. For example, the data frame Open may look >> like >> this: >> >> DATE,FB,AAPL,AMZN,NFLX,GOOGL,... 20150128,1.5,2.2,0.4,5.1,1.6,... >> 20150129,NA,2.3,0.5,5.2,1.7,... ... >> >> 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) >> >> Thanks, >> >> Nathan >> >> Disclaimer: In case it's relevant, this question is also posted on >> stackoverflow. >> >> [[alternative HTML version deleted]] >> >> ______________________________________________ >> R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see >> https://stat.ethz.ch/mailman/listinfo/r-help >> PLEASE do read the posting guide >> http://www.R-project.org/posting-guide.html >> and provide commented, minimal, self-contained, reproducible code. >> > [[alternative HTML version deleted]] ______________________________________________ R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
