


On 1/17/10 1:06 PM, David Winsemius wrote:
>
> On Jan 17, 2010, at 12:37 PM, James Rome wrote:
>
>> I don't think it is that simple because it is not a one-to-one match. In
>> the arr data frame, there are many arrivals in a quarter hour with good
>> weather on a given day. So I need to match the date and the quarter
>> hour.
>>
>> And all of the rows in the weather data frame are times with good
>> weather--unique date + quarter hour. That is why I needed the loop. For
>> each date and quarter hour in weather, I want to mark all the entries
>> with the corresponding date and weather as TRUE in the arr$gw column.
>>
>> I did convert the dates to POSIXlt dates and rewrote my function as
>> gooddates = function(all, good) {
>>   la = length(all)   # All the arrivals
>>  lw = length(good)  # The good 15-minute periods
>>  for(j in 1:lw) {
>>    d=good$Date[j]
>>    q=good$quarter[j]
>>    all$gw[all$Date==d && all$quarter==q]=TRUE
>
>
> You are attempting a vectorized test and assignment with "&&" which
> seems unlikely to succeed, but even then I am not sure your problems
> would be over. (I'm also guessing that you might not have reported a
> warning.)

Why shouldn't the && succeed? You are correct there, because I do get
items if I use either part of this and test, when I insert the &&, I get
no hits. And I got no warnings.
>
> Why not merge arr to gw by date and quarter?
The sets contain different data, and the only thing I want from the
weather set is the fact that it has an entry for a given date and time
>
> Answering these questions would be greatly speeded up with a small
> sample dataset. Are you aware of the virtues of the dput function?
>

What I want is for a 1 to be in the gw column in the quarter
60,61,62,63,...

For example, here is some data from the good weather set:
Date 	minute 	hour 	quarter 	Efficiency Val
1/1/09 	5 	15 	60 	
1/1/09 	15 	15 	61 	72
1/1/09 	30 	15 	62 	63.3
1/1/09 	45 	15 	63 	85.4



And this is from the arrivals set:
DateTime 	weekday 	month 	quarter 	ICAO 	Flight 	gw

1/1/09 	5 	1 	59 	COA 	COA349  	0
1/1/09 	5 	1 	59 	NWA 	NWA9934 	0
1/1/09 	5 	1 	60 	JBU 	JBU554  	0
1/1/09 	5 	1 	60 	BTA 	BTA2347 	0
1/1/09 	5 	1 	60 	COA 	COA886  	0
1/1/09 	5 	1 	60 	BTA 	BTA2916 	0
1/1/09 	5 	1 	60 	CJC 	CJC3225 	0
1/1/09 	5 	1 	60 	BTA 	BTA2085 	0
1/1/09 	5 	1 	60 	BTA 	BTA2064 	0
1/1/09 	5 	1 	60 	AAL 	AAL842  	0
1/1/09 	5 	1 	60 	BTA 	BTA1234 	0
1/1/09 	5 	1 	60 	CJC 	CJC3359 	0
1/1/09 	5 	1 	60 	BTA 	BTA3072 	0
1/1/09 	5 	1 	61 	BTA 	BTA3086 	0
1/1/09 	5 	1 	61 	COA 	COA1166 	0
1/1/09 	5 	1 	61 	COA 	COA855  	0
1/1/09 	5 	1 	61 	AWE 	AWE307  	0
1/1/09 	5 	1 	66 	CHQ 	CHQ5312 	0
1/1/09 	5 	1 	67 	BTA 	BTA2405 	0



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