[R] 3-day moving average for block maxima

Jeff Newmiller jdnewmil at dcn.davis.ca.us
Sun Jul 23 04:14:06 CEST 2017 

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# The following assumes you have no missing days in your time sequence.
# There are better solutions in packages if that is not true for your data
# (e.g. zoo or data.table)
# The filter function looks backward, while your example appears 
# to look foward from the "current" value. The filter function presumes 
# that near the beginning the "previous" values are missing, which causes
# it to generate NA values, which I remove and tack on the NAs at the end.
# (It would be misleading to put in alternate calculations at the end 
# because those would be different than all the rest.)
dt$`Moving Average` <- c( filter( dt$Amount
                                 , rep(1/3,3)
                                 , sides = 1
                                 )[ -(1:2) ]
                         , NA
                         , NA
                         )
dta <- aggregate( list( Max3dMA = dt$`Moving Average` )
                 , dt[ , "Year", drop=FALSE ]
                 , FUN = max
                 , na.rm = TRUE
                 )

On Sat, 22 Jul 2017, roslinazairimah zakaria wrote:

> Dear r-users,
>
> I would like to construct 3-day moving average for block maxima series.
>
> I tried this:
>
> bmthree <- lapply(split(dt, dt$Year), function(x) max(sapply(1:(nrow(x)-2),
>                    function(i) with(x, mean(Amount[i:(i+2)],na.rm=TRUE)))))
> bmthree
>
> and got the following output.
>
> $`1971`
> [1] 70.81667
>
> $`1972`
> [1] 68.94553
>
> $`1973`
> [1] 102.7236
>
> $`1974`
> [1] 73.6625
>
> $`1975`
> [1] 92.98889
>
> $`1976`
> [1] 95.8125
>
> $`1977`
> [1] 31.33974
>
> $`1978`
> [1] 141
>
> $`1979`
> [1] 71.4
>
> $`1980`
> [1] 115.9667
>
> $`1981`
> [1] 66.73718
>
> $`1982`
> [1] 189.5
>
> $`1983`
> [1] 183.1
>
> $`1984`
> [1] 131.5667
>
> $`1985`
> [1] 96.83333
>
> $`1986`
> [1] 267.9667
>
> $`1987`
> [1] 113.6667
>
> $`1988`
> [1] 246.6667
>
> $`1989`
> [1] 83.33333
>
> $`1990`
> [1] 79.5
>
> $`1991`
> [1] 138.3333
>
> $`1992`
> [1] 117
>
> $`1993`
> [1] 99.66667
>
> $`1994`
> [1] 205.3333
>
> $`1995`
> [1] 142.6667
>
> $`1996`
> [1] 106.6667
>
> $`1997`
> [1] 112.5
>
> $`1998`
> [1] 62.66667
>
> $`1999`
> [1] 100.6333
>
> $`2000`
> [1] 146.1333
>
> $`2001`
> [1] 171.7333
>
> $`2002`
> [1] 78.33333
>
> $`2003`
> [1] 100.5667
>
> $`2004`
> [1] 115.0667
>
> $`2005`
> [1] 163.8667
>
> $`2006`
> [1] 79.93333
>
> $`2007`
> [1] 130.6667
>
> $`2008`
> [1] 156.5
>
> $`2009`
> [1] 162.1667
>
> $`2010`
> [1] 99.33333
> $`2011`
> [1] 162.8333
>
> $`2012`
> [1] 247.6667
>
> How do I tweak the code so that I can see the moving average for the block
> maxima before the maximum for that particular year is chosen.
>
> Year                  Month              Day Amount
> Moving average 3-day BM
> 1971 1 1 55.81429 47.63658
> 1971 1 2 49.01818 34.95606
> 1971 1 3 38.07727
> 1971 1 4 17.77273
>
> This is my data:
>
>> dput(try.dt)
> structure(list(Year = c(1971L, 1971L, 1971L, 1971L, 1971L, 1971L,
> 1971L, 1971L, 1971L, 1971L, 1971L, 1971L, 1971L, 1971L, 1971L,
> 1971L, 1971L, 1971L, 1971L, 1971L, 1971L, 1971L, 1971L, 1971L,
> 1971L, 1971L, 1971L, 1971L, 1971L, 1971L, 1971L, 1971L, 1971L,
> 1971L, 1971L, 1971L, 1971L, 1971L, 1971L, 1971L, 1971L, 1971L,
> 1971L, 1971L, 1971L, 1971L, 1971L, 1971L, 1971L, 1971L, 1971L,
> 1971L, 1971L, 1971L, 1971L, 1971L, 1971L, 1971L, 1971L, 1971L,
> 1971L, 1971L, 1971L, 1971L, 1971L, 1971L, 1971L, 1971L, 1971L,
> 1971L, 1971L, 1971L, 1971L, 1971L, 1971L, 1971L, 1971L, 1971L,
> 1971L, 1971L, 1971L, 1971L, 1971L, 1971L, 1971L, 1971L, 1971L,
> 1971L, 1971L, 1971L, 1971L, 1971L, 1971L, 1971L, 1971L, 1971L,
> 1971L, 1971L, 1971L, 1971L, 1971L, 1971L, 1971L, 1971L, 1971L,
> 1971L, 1971L, 1971L, 1971L, 1971L, 1971L, 1971L, 1971L, 1971L,
> 1971L, 1971L, 1971L, 1971L, 1971L, 1971L, 1971L, 1971L, 1971L,
> 1971L, 1971L, 1971L, 1971L, 1971L, 1971L, 1971L, 1971L, 1971L,
> 1971L, 1971L, 1971L, 1971L, 1971L, 1971L, 1971L, 1971L, 1971L,
> 1971L, 1971L, 1971L, 1971L, 1971L, 1971L, 1971L, 1971L, 1971L,
> 1971L, 1971L, 1971L, 1971L, 1971L, 1971L, 1971L, 1971L, 1971L,
> 1971L, 1971L, 1971L, 1971L, 1971L, 1971L, 1971L, 1971L, 1971L,
> 1971L, 1971L, 1971L, 1971L, 1971L, 1971L, 1971L, 1971L, 1971L,
> 1971L, 1971L, 1971L, 1971L, 1971L, 1971L, 1971L, 1971L, 1971L,
> 1971L, 1971L, 1971L, 1971L, 1971L, 1971L, 1971L, 1971L, 1971L,
> 1971L, 1971L, 1971L, 1971L, 1971L, 1971L, 1971L, 1971L, 1971L,
> 1971L, 1971L, 1971L, 1971L, 1971L, 1971L, 1971L, 1971L, 1971L,
> 1971L, 1971L, 1971L, 1971L, 1971L, 1971L, 1971L, 1971L, 1971L,
> 1971L, 1971L, 1971L, 1971L, 1971L, 1971L, 1971L, 1971L, 1971L,
> 1971L, 1971L, 1971L, 1971L, 1971L, 1971L, 1971L, 1971L, 1971L,
> 1971L, 1971L, 1971L, 1971L, 1971L, 1971L, 1971L, 1971L, 1971L,
> 1971L, 1971L, 1971L, 1971L, 1971L, 1971L, 1971L, 1971L, 1971L,
> 1971L, 1971L, 1971L, 1971L, 1971L, 1971L, 1971L, 1971L, 1971L,
> 1971L, 1971L, 1971L, 1971L, 1971L, 1971L, 1971L, 1971L, 1971L,
> 1971L, 1971L, 1971L, 1971L, 1971L, 1971L, 1971L, 1971L, 1971L,
> 1971L, 1971L, 1971L, 1971L, 1971L, 1971L, 1971L, 1971L, 1971L,
> 1971L, 1971L, 1971L, 1971L, 1971L, 1971L, 1971L, 1971L, 1971L,
> 1971L, 1971L, 1971L, 1971L, 1971L, 1971L, 1971L, 1971L, 1971L,
> 1971L, 1971L, 1971L, 1971L, 1971L, 1971L, 1971L, 1971L, 1971L,
> 1971L, 1971L, 1971L, 1971L, 1971L, 1971L, 1971L, 1971L, 1971L,
> 1971L, 1971L, 1971L, 1971L, 1971L, 1971L, 1971L, 1971L, 1971L,
> 1971L, 1971L, 1971L, 1971L, 1971L, 1971L, 1971L, 1971L, 1971L,
> 1971L, 1971L, 1971L, 1971L, 1971L, 1971L, 1971L, 1971L, 1971L,
> 1971L, 1971L, 1971L, 1971L, 1971L, 1971L, 1971L, 1971L), Month = c(1L,
> 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
> 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L,
> 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
> 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L,
> 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
> 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
> 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
> 4L, 4L, 4L, 4L, 4L, 4L, 4L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L,
> 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L,
> 5L, 5L, 5L, 5L, 5L, 5L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L,
> 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L,
> 6L, 6L, 6L, 6L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L,
> 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L,
> 7L, 7L, 7L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L,
> 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L,
> 8L, 8L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L,
> 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L,
> 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L,
> 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L,
> 10L, 10L, 10L, 10L, 10L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L,
> 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L,
> 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 12L, 12L, 12L, 12L,
> 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L,
> 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L,
> 12L), Day = c(1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L,
> 13L, 14L, 15L, 16L, 17L, 18L, 19L, 20L, 21L, 22L, 23L, 24L, 25L,
> 26L, 27L, 28L, 29L, 30L, 31L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L,
> 9L, 10L, 11L, 12L, 13L, 14L, 15L, 16L, 17L, 18L, 19L, 20L, 21L,
> 22L, 23L, 24L, 25L, 26L, 27L, 28L, 1L, 2L, 3L, 4L, 5L, 6L, 7L,
> 8L, 9L, 10L, 11L, 12L, 13L, 14L, 15L, 16L, 17L, 18L, 19L, 20L,
> 21L, 22L, 23L, 24L, 25L, 26L, 27L, 28L, 29L, 30L, 31L, 1L, 2L,
> 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 13L, 14L, 15L, 16L,
> 17L, 18L, 19L, 20L, 21L, 22L, 23L, 24L, 25L, 26L, 27L, 28L, 29L,
> 30L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 13L,
> 14L, 15L, 16L, 17L, 18L, 19L, 20L, 21L, 22L, 23L, 24L, 25L, 26L,
> 27L, 28L, 29L, 30L, 31L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L,
> 10L, 11L, 12L, 13L, 14L, 15L, 16L, 17L, 18L, 19L, 20L, 21L, 22L,
> 23L, 24L, 25L, 26L, 27L, 28L, 29L, 30L, 1L, 2L, 3L, 4L, 5L, 6L,
> 7L, 8L, 9L, 10L, 11L, 12L, 13L, 14L, 15L, 16L, 17L, 18L, 19L,
> 20L, 21L, 22L, 23L, 24L, 25L, 26L, 27L, 28L, 29L, 30L, 31L, 1L,
> 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 13L, 14L, 15L,
> 16L, 17L, 18L, 19L, 20L, 21L, 22L, 23L, 24L, 25L, 26L, 27L, 28L,
> 29L, 30L, 31L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L,
> 12L, 13L, 14L, 15L, 16L, 17L, 18L, 19L, 20L, 21L, 22L, 23L, 24L,
> 25L, 26L, 27L, 28L, 29L, 30L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L,
> 9L, 10L, 11L, 12L, 13L, 14L, 15L, 16L, 17L, 18L, 19L, 20L, 21L,
> 22L, 23L, 24L, 25L, 26L, 27L, 28L, 29L, 30L, 31L, 1L, 2L, 3L,
> 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 13L, 14L, 15L, 16L, 17L,
> 18L, 19L, 20L, 21L, 22L, 23L, 24L, 25L, 26L, 27L, 28L, 29L, 30L,
> 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 13L, 14L,
> 15L, 16L, 17L, 18L, 19L, 20L, 21L, 22L, 23L, 24L, 25L, 26L, 27L,
> 28L, 29L, 30L, 31L), Amount = c(55.81428571, 49.01818182, 38.07727273,
> 17.77272727, 6.913636364, 5.163636364, 2.995454545, 4.540909091,
> 3.09047619, 3.959090909, 4.080952381, 0.513636364, 0.727272727,
> 0.940909091, 0.036363636, 0.672727273, 0.338095238, 1.163636364,
> 1.745454545, 1.004545455, 0.386363636, 0.359090909, 1.463636364,
> 0.640909091, 0.054545455, 3.768181818, 0.027272727, 0.027272727,
> 1.904545455, 8.261904762, 12.21363636, 12.48181818, 0.059090909,
> 2.468181818, 0.659090909, 0.157142857, 0.6, 0.477272727, 0.818181818,
> 0.05, 0.118181818, 0.527272727, 0.331818182, 0.004545455, 1.9,
> 2.018181818, 0.795454545, 2.118181818, 0.345454545, 0.045454545,
> 6.481818182, 13.45454545, 21.48636364, 23.98181818, 3.877272727,
> 3.945454545, 2.423809524, 0.672727273, 4.565, 11.625, 25.74761905,
> 14.51428571, 2.471428571, 0.085714286, 1.523809524, 0.652380952,
> 1.085714286, 0.485714286, 0.576190476, 0.1, 0.038095238, 1.361904762,
> 38.17619048, 102.1761905, 54.28095238, 20.2, 17.28095238, 25.53809524,
> 7.419047619, 0.576190476, 0.061904762, 0.061904762, 1.533333333,
> 0.028571429, 0.023809524, 0.057142857, 0.638095238, 0.70952381,
> 0.171428571, 0.014285714, 0.014285714, 0.557142857, 0.019047619,
> 1.090909091, 0.027272727, 0, 1.254545455, 0, 0.031818182, 0.418181818,
> 0.086363636, 0.186363636, 0.227272727, 0.009090909, 0.009090909,
> 0.981818182, 0.4, 0, 0, 0, 0, 0, 0, 0, 1.55, 0.327272727, 0.009090909,
> 1.413636364, 5.023809524, 11.11, 12.68181818, 12.63636364, 5.877272727,
> 1.018181818, 2.495454545, 3.25, 3.527272727, 1.177272727, 0.6,
> 1.836363636, 8.45, 6.745454545, 7.677272727, 1.177272727, 7.961904762,
> 6.786363636, 5.763636364, 4.047619048, 3.961904762, 7.933333333,
> 3.233333333, 1.114285714, 2.033333333, 6.257142857, 6.386363636,
> 2.327272727, 16.09090909, 13.87727273, 4.277272727, 1.036363636,
> 1.454545455, 1.086363636, 6.495454545, 4.136363636, 2.663636364,
> 10.49545455, 6.422727273, 2.881818182, 6.695454545, 1.631818182,
> 0.277272727, 3.686363636, 2.718181818, 1.281818182, 4.381818182,
> 0.759090909, 7.236363636, 8.195454545, 3.572727273, 3.395454545,
> 1.072727273, 3.827272727, 2.486363636, 0.481818182, 1.163636364,
> 1.5, 2.731818182, 2.568181818, 11.87727273, 12.60909091, 3.747826087,
> 2.204347826, 2.634782609, 1, 2.582608696, 1.213043478, 0.043478261,
> 0.930434783, 3.443478261, 0.743478261, 2.252173913, 2.117391304,
> 7.2, 9.730434783, 1.047826087, 0.213043478, 4.613043478, 6.77826087,
> 7.97826087, 19.52173913, 14.10434783, 3.652173913, 1.039130435,
> 3.873913043, 5.543478261, 2.717391304, 4.295652174, 5.417391304,
> 6.234782609, 7.556521739, 2.356521739, 7.413043478, 6.647826087,
> 5.139130435, 8.856521739, 8.4, 4.113043478, 10.20454545, 8.443478261,
> 3.886956522, 7.856521739, 6.047826087, 5.154166667, 4.241666667,
> 1.041666667, 0.820833333, 3.220833333, 1.0375, 4.545833333, 2.825,
> 4.769565217, 11.84782609, 5.773913043, 10.46521739, 7.77826087,
> 1.134782609, 1.17826087, 4.504347826, 7.247826087, 9.956521739,
> 13.77826087, 9.265217391, 0.534782609, 1.217391304, 1.72173913,
> 0.904347826, 0.426086957, 5.965217391, 2.191304348, 1.569565217,
> 7.360869565, 1.17826087, 6.417391304, 10.84782609, 9.508695652,
> 8.691304348, 15.5, 2.447826087, 2.739130435, 5.082608696, 10.53913043,
> 11.5826087, 2.143478261, 4.67826087, 4.969565217, 1.030434783,
> 3.073913043, 3.77826087, 0.952173913, 2.952173913, 3.669565217,
> 2.282608696, 2.740909091, 8.3, 3.67826087, 6.456521739, 8.952173913,
> 1.469565217, 2.147826087, 10.14782609, 9.008695652, 10.81304348,
> 6.460869565, 11.65652174, 9.195652174, 7.973913043, 15.65652174,
> 18.86956522, 6.930434783, 11.56086957, 12.99545455, 13.13913043,
> 19.23043478, 8.995652174, 5.556521739, 27.07391304, 20.94347826,
> 4.826086957, 5.586956522, 4.126086957, 7.1, 9.404545455, 6.47826087,
> 6.426086957, 12.35217391, 8.652173913, 4.752173913, 4.926086957,
> 2.304347826, 7.930434783, 9.782608696, 1.831818182, 1.877272727,
> 3.19047619, 9.940909091, 4.222727273, 21, 16.38636364, 10.56363636,
> 8.422727273, 7.533333333, 47.19090909, 101.9181818, 63.34090909,
> 24.98181818, 12.72272727, 7.4, 4.672727273, 8.104545455, 8.4,
> 7.473913043, 3.226086957, 15.87391304, 7.834782609, 6.773913043,
> 15.12608696, 36.89047619, 65.66521739, 53.70434783, 42.76956522,
> 48.49130435, 29.59130435, 28.59130435, 22.25652174, 15.08695652,
> 26.23913043, 21.17391304, 30.31818182, 28.59130435, 9.817391304,
> 14.02608696, 6.708695652, 11.74782609, 17.5375, 25.8826087, 12.55217391,
> 13.10869565, 5.02173913, 11.50869565, 5.195652174, 3.409090909,
> 0.340909091, 6.231818182, 0.004545455, 0.03)), .Names = c("Year",
> "Month", "Day", "Amount"), row.names = 8402:8766, class = "data.frame")
>
>
> Thank you very much for your help.
> -- 
> *Roslinazairimah Zakaria*
> *Tel: +609-5492370 <+60%209-549%202370>; Fax. No.+609-5492766
> <+60%209-549%202766>*
>
> *Email: roslinazairimah at ump.edu.my
> <roslinazairimah at ump.edu.my>; roslinaump at gmail.com <roslinaump at gmail.com>*
> Faculty of Industrial Sciences & Technology
> University Malaysia Pahang
> Lebuhraya Tun Razak, 26300 Gambang, Pahang, Malaysia
>
> 	[[alternative HTML version deleted]]
>
> ______________________________________________
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>

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Jeff Newmiller                        The     .....       .....  Go Live...
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