Thank you very much.

More and more methods are coming. That sounds great!


Thanks,

kevin



On Fri, Apr 26, 2013 at 7:51 PM, Duncan Murdoch <murdoch.duncan@gmail.com>wrote:

> On 13-04-26 3:00 PM, Kevin Hao wrote:
>
>> Hi Ye,
>>
>> Thanks.
>>
>> That is a good method. have any other methods instead of using database?
>>
>
> If you know the format of the file, you can probably write something in C
> (or other language) that is faster than R.  Convert your .csv file to a
> nice binary format, and R will read it in no time at all.
>
> If writing it in C is hard, then R is probably a better use of your time.
>  Read the file once, write it out using saveRDS(), and read it in using
> readRDS() after that.
>
> In either case, the secret is to do the conversion from ugly character
> encoded numbers to beautiful binary numbers just once.
>
> Duncan Murdoch
>
>
>
>> kevin
>>
>>
>> On Fri, Apr 26, 2013 at 1:58 PM, Ye Lin <yelin@lbl.gov> wrote:
>>
>>  Have you think of build a database then then let R read it thru that db
>>> instead of your desktop?
>>>
>>>
>>> On Fri, Apr 26, 2013 at 8:09 AM, Kevin Hao <rfans4chemo@gmail.com>
>>> wrote:
>>>
>>>  Hi all scientists,
>>>>
>>>> Recently, I am dealing with big data ( >3G  txt or csv format ) in my
>>>> desktop (windows 7 - 64 bit version), but I can not read them faster,
>>>> thought I search from internet. [define colClasses for read.table,
>>>> cobycol
>>>> and limma packages I have use them, but it is not so fast].
>>>>
>>>> Could you share your methods to read big data to R faster?
>>>>
>>>> Though this is an odd question, but we need it really.
>>>>
>>>> Any suggest appreciates.
>>>>
>>>> Thank you very much.
>>>>
>>>>
>>>> kevin
>>>>
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>>>>
>>>>
>>>
>>>
>>         [[alternative HTML version deleted]]
>>
>> ______________________________**________________
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>>
>>
>

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