[R] How to import and create time series data frames in an efficient way?
bgunter@4567 @end|ng |rom gm@||@com
Fri Nov 15 01:34:21 CET 2019
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.
"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 <lathanhnhan using 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:
> GOOGL,20150128,1.62,1.645,1.59,1.63,684835 ...................and many
> 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
> 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,
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