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Daily average precip for many years

Asked by Thomas Burbey on 17 May 2018
Latest activity Commented on by dpb
on 18 May 2018
Accepted Answer by dpb
I have a large matrix of two columns containing dates (eg 1/1/1991) and precip values. I want to create the average daily precip for all years from 1991-2016 such that the day of the year is in column 1 and the ave precip value is in column 2. I'm getting bogged down I believe because of leap years (not sure how to eliminate that date, which is fine). Any help would be greatly appreciated.

  2 Comments

Can you (ideally) upload your data in a MAT file, or (less ideally) give an example -- not just a description -- of how your input data are stored?
Here is the precip file you requested showing you the format.

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2 Answers

Answer by dpb
on 18 May 2018
Edited by dpb
on 18 May 2018
 Accepted Answer

Just taking a stab presuming the file is something recognizable--
precip=table2timetable(readtable('yourfile.ext')); % read the file; convert to timetable
daily=retime(precip,'daily','mean');
Done!
"Here is the precip file..."
Oh...only daily data and already in table--OK, then create the grouping variable
>> precip.DOM=day(precip.DATE); % day of month each entry
>>daily=varfun(@mean,precip,'groupingvariables','DOM','inputvariables','Precipitation');
...
ADDENDUM OK, answer the question asked... :)
>> precip.DOY=day(precip.DATE,'dayofyear');
>> daily=varfun(@mean,precip,'groupingvariables','DOY','inputvariables','Precipitation');
>> whos daily
Name Size Bytes Class Attributes
daily 366x3 10149 table
>> daily(1:10,:)
ans =
10×3 table
DOY GroupCount mean_Precipitation
___ __________ __________________
1 26 3.27692307692308
2 26 3.8
3 26 2.5
4 26 2.84230769230769
5 26 3.94615384615385
6 26 5.21923076923077
7 26 2.13461538461539
8 26 2.53461538461538
9 26 3.65
10 26 3.33461538461538
>>
should work; I believe the 'dayofyear' option came about R2104 or so...there's just too much stuff to try to recall introduced too rapidly.

  11 Comments

no NaN in evidence in the initial dataset--
>> sum(isnan(precip.Precipitation))
ans =
0
>>
You have another dataset that does have NaN? I just got the second link to the .mat file and it appears identical to the first; no NaN.
Something's not kosher it would seem...to illustrate works,
>> precip(precip.DOY==1,:)
ans =
26×3 table
DATE Precipitation DOY
________ _____________ ___
1/1/1991 3.4 1
1/1/1992 0 1
1/1/1993 0 1
1/1/1994 2.1 1
1/1/1995 0.3 1
1/1/1996 0 1
1/1/1997 0 1
1/1/1998 0 1
1/1/1999 3.4 1
1/1/2000 0.6 1
1/1/2001 0 1
1/1/2002 0 1
1/1/2003 10.7 1
1/1/2004 8.7 1
1/1/2005 1.3 1
1/1/2006 6.9 1
1/1/2007 9.4 1
1/1/2008 0 1
1/1/2009 0 1
1/1/2010 0 1
1/1/2011 0 1
1/1/2012 10.2 1
1/1/2013 0 1
1/1/2014 22 1
1/1/2015 0.2 1
1/1/2016 6 1
>> mean(ans.Precipitation)
ans =
3.27692307692308
>>
which matches the varfun solution. If that's not working like that for you, post exact code...
>> sum(isnan(daily.mean_Precipitation))
ans =
0
>>
Everything works fine. I gave you the matrix after I had eliminated NaN, which is what the raw precip file has when no precip for a day is measured. Sorry for the confusion. Thanks again for your help.
Ah, so! That 'splains things, indeed! :)
The time-related stuff has gotten pretty deep as well as facilities to use the various varieties of tables. Seems to me like there's an excess of types that are similar but not quite the same that makes for very difficult learning task; I wish TMW would slow down some and work more on consistency of interfaces and methods.

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Answer by Razvan Carbunescu on 18 May 2018

An alternative if using R2018a is groupsummary, it has a few different ways of selecting days for each week, month, year from a date:
>> GT = groupsummary(precip,'DATE','dayofyear','mean','Precipitation');
>> head(GT)
ans =
8×3 table
dayofyear_DATE GroupCount mean_Precipitation
______________ __________ __________________
1 26 3.2769
2 26 3.8
3 26 2.5
4 26 2.8423
5 26 3.9462
6 26 5.2192
7 26 2.1346
8 26 2.5346

  2 Comments

That is cool! I wish I had that latest version. SO helpful
There's only a one-liner difference to generate the grouping variable, though, so it's only a relatively minor syntactic sugar refinement.
Note same results as above which is another independent confirmation of correct result.

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