How to calculate Mean by Date Grouped as Fiscal Qu

问题:

I have the following table:

 Date     Country  Class Value   
6/1/2010   USA      A     45
6/1/2010  Canada    A     23 
6/1/2010  Brazil    B     65
9/1/2010   USA      B     47
9/1/2010  Canada    A     98
9/1/2010  Brazil    B     25
12/1/2010  USA      B     14 
12/1/2010 Canada    A     79
12/1/2010 Brazil    A     23
3/1/2011   USA      A     84 
3/1/2011  Canada    B     77
3/1/2011  Brazil    A     43
6/1/2011   USA      A     45
6/1/2011  Canada    A     23 
6/1/2011  Brazil    B     65
9/1/2011   USA      B     47
9/1/2011  Canada    A     98
9/1/2011  Brazil    B     25
12/1/2011  USA      B     14 
12/1/2011 Canada    A     79
12/1/2011 Brazil    A     23
3/1/2012   USA      A     84 
3/1/2012  Canada    B     77
3/1/2012  Brazil    A     43

In column "Date" years are divided by the following months - March, June, September and December. I need to group months from June to March as a Fiscal Year and by each Fiscal Year calculate the mean of column "Value" by "Country" and "Class". Could anybody help me to do that?

I am trying to do that using data.table but get the error:

d=data[,list(Val=mean(Value,na.rm=T)),by=list(Country,Class,
  Period.grp=cut(Period,list(6/1/2010,3/1/2011,6/1/2011,3/1/2012,
  6/1/2012,3/1/2013,6/1/2013,3/1/2014)))]  

Error in cut.default(Period, list(6/1/2010, 3/1/2011, 6/1/2011, 3/1/2012, : 'x' must be numeric

Thank you!

回答1:

I found the answer I was thinking I had written, but it's actually a bit different

# this should "shift" the year calculation 3 months and provide quarter
c('Q1','Q2','Q3','Q4')[ 1+((as.POSIXlt(dates)$mon+3) %/% 3)%%4]

This then pastes the FY with the quarter shifted 6 months, but you may need to adjust because your year specification was ambiguous about "naming the year":

dat$FY_Q <- paste( 1900+as.POSIXlt( dat$dates )$year+
                        1*(as.POSIXlt( dat$dates )$mon %in% 7:12) ,  
  c('Q1','Q2','Q3','Q4')[ 1+((as.POSIXlt(dat$dates)$mon-6) %/% 3)%%4] 
  , sep="-")

dat
        Date Country Class Value      dates    FY_Q
1   6/1/2010     USA     A    45 2010-06-01 2010-Q4
2   6/1/2010  Canada     A    23 2010-06-01 2010-Q4
3   6/1/2010  Brazil     B    65 2010-06-01 2010-Q4
4   9/1/2010     USA     B    47 2010-09-01 2011-Q1
5   9/1/2010  Canada     A    98 2010-09-01 2011-Q1
6   9/1/2010  Brazil     B    25 2010-09-01 2011-Q1
snipped---------

So now do a tapply or aggregate by FY_Q and Country:

> with( dat, aggregate(Value, list(FY_Q, Country),FUN=mean)  )
   Group.1 Group.2  x
1  2010-Q4  Brazil 65
2  2011-Q1  Brazil 25
3  2011-Q2  Brazil 23
4  2011-Q3  Brazil 43
5  2011-Q4  Brazil 65
6  2012-Q1  Brazil 25
7  2012-Q2  Brazil 23
8  2012-Q3  Brazil 43
9  2010-Q4  Canada 23
10 2011-Q1  Canada 98
11 2011-Q2  Canada 79
12 2011-Q3  Canada 77
13 2011-Q4  Canada 23
14 2012-Q1  Canada 98
15 2012-Q2  Canada 79
16 2012-Q3  Canada 77
17 2010-Q4     USA 45
18 2011-Q1     USA 47
19 2011-Q2     USA 14
20 2011-Q3     USA 84
21 2011-Q4     USA 45
22 2012-Q1     USA 47
23 2012-Q2     USA 14
24 2012-Q3     USA 84

See: Format date-time as seasons in R? for a somewhat similar problem and solutions.

回答2:

Try:

> dat$fiscal = rep(2011:2015,each=12, length.out=nrow(dat))
> 
> aggregate(Value~Country+Class+fiscal, data=dat, mean)
   Country Class fiscal    Value
1   Brazil     A   2011 33.00000
2   Canada     A   2011 66.66667
3      USA     A   2011 64.50000
4   Brazil     B   2011 45.00000
5   Canada     B   2011 77.00000
6      USA     B   2011 30.50000
7   Brazil     A   2012 33.00000
8   Canada     A   2012 66.66667
9      USA     A   2012 64.50000
10  Brazil     B   2012 45.00000
11  Canada     B   2012 77.00000
12     USA     B   2012 30.50000

For FY2011 etc:

dat$fiscal = paste0('FY',rep(2011:2015,each=12, length.out=nrow(dat)))