[R] How to import and create time series data frames in an efficient way?

[Nhan La]

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.4567 using 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 <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:
>>
>> 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]]
>>
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