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Makes a frequency change of this series.

Usage

aggregate(
  s,
  nfreq = 1,
  conversion = c("Sum", "Average", "First", "Last", "Min", "Max"),
  complete = TRUE
)

Arguments

s

the input time series.

nfreq

the new frequency. Must be la divisor of the frequency of s.

conversion

Aggregation mode: sum ("Sum"), average ("Average"), first observation ("First"), last observation ("Last"), minimum ("Min"), maximum ("Max").

complete

Boolean indicating if the observation for a given period in the new series is set missing if some data in the original series are missing.

Value

A new time series of frequency nfreq.

Examples

s <- ABS$X0.2.09.10.M
# Annual sum
aggregate(s, nfreq = 1, conversion = "Sum") # first and last years removed
#> Time Series:
#> Start = 1983 
#> End = 2016 
#> Frequency = 1 
#>  [1]  6132.6  6688.3  7614.5  8136.0  8692.8  9266.3  9592.9  9875.2 10051.2
#> [10] 10497.0 10705.9 11105.1 11442.6 11632.3 11813.7 12232.4 12719.2 13271.0
#> [19] 13814.6 14569.2 15539.8 16432.0 16625.9 16930.4 17811.7 18357.7 18801.6
#> [28] 18579.0 18172.0 18326.8 18171.6 18160.9 18727.3 18811.1
aggregate(s, nfreq = 1, conversion = "Sum", complete = FALSE)
#> Time Series:
#> Start = 1982 
#> End = 2017 
#> Frequency = 1 
#>  [1]  4634.0  6132.6  6688.3  7614.5  8136.0  8692.8  9266.3  9592.9  9875.2
#> [10] 10051.2 10497.0 10705.9 11105.1 11442.6 11632.3 11813.7 12232.4 12719.2
#> [19] 13271.0 13814.6 14569.2 15539.8 16432.0 16625.9 16930.4 17811.7 18357.7
#> [28] 18801.6 18579.0 18172.0 18326.8 18171.6 18160.9 18727.3 18811.1 11172.0
# Quarterly mean
aggregate(s, nfreq = 4, conversion = "Average")
#>           Qtr1      Qtr2      Qtr3      Qtr4
#> 1982            468.8333  454.2000  621.6333
#> 1983  409.7667  492.1333  469.7333  672.5667
#> 1984  437.9667  528.5333  523.2000  739.7333
#> 1985  498.2000  616.6000  582.8000  840.5667
#> 1986  532.0333  633.1000  635.4000  911.4667
#> 1987  575.6667  686.2000  664.9333  970.8000
#> 1988  620.0667  726.1667  685.9000 1056.6333
#> 1989  634.1667  749.5667  732.2667 1081.6333
#> 1990  663.7333  797.3333  736.9333 1093.7333
#> 1991  672.6333  774.4333  768.8000 1134.5333
#> 1992  702.4667  845.3333  777.2000 1174.0000
#> 1993  719.7333  878.1333  781.6333 1189.1333
#> 1994  743.8667  884.1000  822.9000 1250.8333
#> 1995  748.7000  913.3333  870.4333 1281.7333
#> 1996  794.0667  922.2000  876.8667 1284.3000
#> 1997  808.7333  891.3333  915.4000 1322.4333
#> 1998  818.8000  929.9000  950.5333 1378.2333
#> 1999  861.2000  952.9333  985.4333 1440.1667
#> 2000  875.0000 1080.8333  974.3667 1493.4667
#> 2001  934.6667 1062.5333 1025.5667 1582.1000
#> 2002  971.0667 1136.4000 1067.2333 1681.7000
#> 2003 1030.8333 1232.4667 1160.4667 1756.1667
#> 2004 1101.3667 1313.8333 1263.5333 1798.6000
#> 2005 1194.8000 1290.7000 1259.5333 1796.9333
#> 2006 1145.3000 1348.4000 1290.1000 1859.6667
#> 2007 1215.3000 1386.4000 1377.0333 1958.5000
#> 2008 1296.6667 1406.4333 1411.3333 2004.8000
#> 2009 1298.1667 1509.6667 1437.3667 2022.0000
#> 2010 1320.3000 1456.1000 1442.3333 1974.2667
#> 2011 1283.9000 1451.1333 1390.5667 1931.7333
#> 2012 1281.1000 1488.1667 1383.4000 1956.2667
#> 2013 1302.7667 1439.4667 1362.4000 1952.5667
#> 2014 1269.7000 1438.7667 1370.7333 1974.4333
#> 2015 1297.4000 1450.6000 1424.3000 2070.1333
#> 2016 1375.5000 1498.9333 1385.2333 2010.7000
#> 2017 1297.0667 1510.7333