Aggregate can do both which.max and group length calculations, but the
result ends up as a matrix inside the data frame, which I find cumbersome
to work with.
Daily <- read.table( text =
" Date wyr Q
1911-04-01 1990 4.530695
1911-04-02 1990 4.700596
1911-04-03 1990 4.898814
1911-04-04 1990 5.097032
1911-04-05 1991 5.295250
1911-04-06 1991 6.569508
1911-04-07 1991 5.861587
1911-04-08 1991 5.153666
1911-04-09 1992 4.445745
1911-04-10 1992 3.737824
1911-04-11 1992 3.001586
1911-04-12 1992 3.001586
1911-04-13 1993 2.350298
1911-04-14 1993 2.661784
1911-04-16 1993 3.001586
1911-04-17 1993 2.661784
1911-04-19 1994 2.661784
1911-04-28 1994 3.369705
1911-04-29 1994 3.001586
1911-05-20 1994 2.661784
", header = TRUE, stringsAsFactors=FALSE)
# this algorithm only works if wyr groups are contiguous
out <- out[ order(out$wyr), ]
# generate a data frame with key column wyr and matrix Q as the second
column
out <- aggregate( Q ~ wyr
, data = Daily
, FUN = function(x) {
c( WM = which.max(x)
, n=length( x )
)
}
)
# put matrix into separate columns Q.WM
out[ , paste( "Q", colnames( out$Q ), sep="." ) ] <- out$Q
# drop the matrix
out$Q <- NULL
# form absolute indexes Q.N
out <- within( out, {
Q.maxidx <- cumsum( c( 0, Q.n[ -length(Q.n) ] ) ) + Q.WM
})
result <- Daily[ with( out, Q.maxidx ), ]
# or save ourselves some effort
library(dplyr)
result2 <- ( Daily
%>% group_by( wyr )
%>% slice( which.max( Q ) )
%>% as.data.frame
)
On Tue, 6 Jun 2017, Bert Gunter wrote:
cumsum() seems to be what you need.
This can probably be done more elegantly, but ...
out <- aggregate(Q ~ wyr, data = Daily, which.max)
tbl <- table(Daily$wyr)
out$Q <- out$Q + cumsum(c(0,tbl[-length(tbl)]))
out
## yields
wyr Q
1 1990 4
2 1991 6
3 1992 9
4 1993 15
5 1994 18
I leave the matter of Julian dates to you or others.
Cheers,
Bert
Bert Gunter
"The trouble with having an open mind is that people keep coming along
and sticking things into it."
-- Opus (aka Berkeley Breathed in his "Bloom County" comic strip )
On Tue, Jun 6, 2017 at 6:30 PM, Morway, Eric <emor...@usgs.gov> wrote:
Using the dataset below, I got close to what I'm after, but not quite all
the way there. Any suggestions appreciated:
Daily <- read.table(textConnection(" Date wyr Q
1911-04-01 1990 4.530695
1911-04-02 1990 4.700596
1911-04-03 1990 4.898814
1911-04-04 1990 5.097032
1911-04-05 1991 5.295250
1911-04-06 1991 6.569508
1911-04-07 1991 5.861587
1911-04-08 1991 5.153666
1911-04-09 1992 4.445745
1911-04-10 1992 3.737824
1911-04-11 1992 3.001586
1911-04-12 1992 3.001586
1911-04-13 1993 2.350298
1911-04-14 1993 2.661784
1911-04-16 1993 3.001586
1911-04-17 1993 2.661784
1911-04-19 1994 2.661784
1911-04-28 1994 3.369705
1911-04-29 1994 3.001586
1911-05-20 1994 2.661784"),header=TRUE)
aggregate(Q ~ wyr, data = Daily, which.max)
# gives:
# wyr Q
# 1 1990 4
# 2 1991 2
# 3 1992 1
# 4 1993 3
# 5 1994 2
I can 'see' that it is returning the which.max() relative to each
grouping. Is there a way to instead return the absolute position (row) of
the max value within each group. i.e.:
# Would instead like to have
# wyr Q
# 1 1990 4
# 2 1991 6
# 3 1992 9
# 4 1993 15
# 5 1994 18
The icing on the cake would be to get the Julien Day corresponding to the
date on which each year's maximum occurs?
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