Data-table experts, Thanks to this stack-overflow post ( http://stackoverflow.com/questions/10748253/idiomatic-r-code-for-partitioning-a-vector-by-an-index-and-performing-an-operati ) i am convinced that my projects may be sped up via use of data table.
At least i hope. I have read the introduction, and it is not clear if it is possible to do time series sub-setting. I presently use XTS, and find that it is decently quick for my large data sets which contain observations each second. Most importantly XTS is robust. My XTS sets have date/time strings that are formatted “%Y-%m-%d %H:%M:%S”, and i wish to sub set across these: A typical data frame might be formatted as follows: >Names(dfx) = c(’price’, ’volume’) >Head(dfx) 2012-06-01 09:07:34 97.55 60 2012-06-01 09:07:36 97.58 150 2012-06-01 09:07:36 97.56 10 2012-06-01 09:07:36 97.57 19 2012-06-01 09:07:39 97.58 100 2012-06-01 09:07:40 97.55 40 I would like to perform the following sub setting operations: for example, the minute after 9:07am (on a single date or all dates); the first ten hours on each monday; 12 to 16 on the first friday of each month; all days in June for years 2001 to 2011; all mondays in 2008. Additionally, i would like to sub-set such that i obtain a table showing the volume of trades at each price on a given time interval. For example, the printed data above would present as: Price volume 97.55 100 97.56 10 97.57 19 97.58 250 Are such sub-setting operations possible in data.table? I anticipate that the time series sub-setting in data.table may be more restrictive, but as my data sets are very large and the reported speed up relative to most methods is huge in big-data cases, i am hopeful that a modest amount of trouble will bring large performance gains. Is this the case? Sorry for the long question - an answer in the affirmative and link to a manual that i have missed would suffice. Thanks + best regards matt johnson
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