Time of first or last non-NA value
availStart.RdTime of first or last non-NA value.
Arguments
- x
a time series or similar object
- any
-
logical flag for multivariate objects. The default
TRUErequests the first/last index containing any non-NA value.FALSErequires that all values at the first/last index must be non-NA.
Details
The time is given as a cycle-season pair.
Argument any is meaningful only for multivariate objects. Its
name is short for
"the first/last index for which any of the values (ie at least one) is non-NA".
any = FALSE is taken to mean that the index is the first/last
for which all values are non-NA.
The functions can be used together with windows to trim
NA's from the beginning and/or end of the data. As an
alternative we provide also methods for periodic time series methods
for zoo:na.trim, see the examples below.
Examples
tipi <- pcts(dataFranses1996[ , "USTotalIPI"])
start(tipi)
#> [1] 1955 1
end(tipi)
#> [1] 1991 4
head(tipi)
#> An object of class "PeriodicTS"
#> Slot "cycle": Start: 1955 Quarter_1
#> Cycle: QuarterYearCycle
#> Number of seasons: 4
#>
#> Q1 Q2 Q3 Q4
#> Y1955 NA NA NA NA
#> Y1956 NA NA
tail(tipi)
#> An object of class "PeriodicTS"
#> Slot "cycle": Start: 1990 Quarter_3
#> Cycle: QuarterYearCycle
#> Number of seasons: 4
#>
#> Q1 Q2 Q3 Q4
#> Y1990 118.3 114.5
#> Y1991 111.4 112.4 116.0 113.9
tipi <- window(tipi, start = availStart(tipi), end = availEnd(tipi))
start(tipi)
#> [1] 1960 1
end(tipi)
#> [1] 1991 4
plot(tipi)
pcfr <- pcts(dataFranses1996)
pcfr2to4 <- pcfr[2:4]
head(pcfr2to4)
#> An object of class "PeriodicMTS"
#> Slot "cycle": Object from built-in class 'QuarterYearCycle'
#> Cycle start: Quarter_1
#>
#> USTotalIPI CanadaUnemployment GermanyGNP
#> Y1955_1 NA NA NA
#> Y1955_2 NA NA NA
#> Y1955_3 NA NA NA
#> Y1955_4 NA NA NA
#> Y1956_1 NA NA NA
#> Y1956_2 NA NA NA
tail(pcfr2to4)
#> An object of class "PeriodicMTS"
#> Slot "cycle": Object from built-in class 'QuarterYearCycle'
#> Cycle start: Quarter_1
#>
#> USTotalIPI CanadaUnemployment GermanyGNP
#> Y1990_3 118.3 NA 468.9
#> Y1990_4 114.5 NA 482.9
#> Y1991_1 111.4 NA NA
#> Y1991_2 112.4 NA NA
#> Y1991_3 116.0 NA NA
#> Y1991_4 113.9 NA NA
## time of first and last data, can be NA's
start(pcfr2to4) # 1955 Q1
#> [1] 1955 1
end(pcfr2to4) # 1991 Q4
#> [1] 1991 4
## time of first nonNA:
availStart(pcfr[[2]]) # 1960 Q1
#> [1] 1960 1
availStart(pcfr2to4) # 1960 Q1
#> [1] 1960 1
## time of last nonNA:
availEnd(pcfr[[2]]) # 1991 Q4
#> [1] 1991 4
availEnd(pcfr[[3]]) # 1987 Q4
#> [1] 1987 4
availEnd(pcfr[[4]]) # 1990 Q4
#> [1] 1990 4
## but at least one of them is available for 1991 Q4, so:
availEnd(pcfr2to4) # 1991 Q4
#> [1] 1991 4
## this requests the time of the last full record:
availEnd(pcfr2to4, any = FALSE) # 1987 Q4
#> [1] 1987 4
pcfr2to4a <- window(pcfr2to4, start = availStart(pcfr2to4), end = availEnd(pcfr2to4))
head(pcfr2to4a)
#> An object of class "PeriodicMTS"
#> Slot "cycle": Object from built-in class 'QuarterYearCycle'
#> Cycle start: Quarter_1
#>
#> USTotalIPI CanadaUnemployment GermanyGNP
#> Y1960_1 40.2 583 167.0
#> Y1960_2 39.8 427 176.2
#> Y1960_3 38.5 334 198.0
#> Y1960_4 38.0 439 190.5
#> Y1961_1 37.1 703 180.1
#> Y1961_2 39.2 480 184.9
tail(pcfr2to4a, 20)
#> An object of class "PeriodicMTS"
#> Slot "cycle": Object from built-in class 'QuarterYearCycle'
#> Cycle start: Quarter_1
#>
#> USTotalIPI CanadaUnemployment GermanyGNP
#> Y1987_1 101.5 1358 388.3
#> Y1987_2 103.8 1197 402.4
#> Y1987_3 107.3 1097 417.5
#> Y1987_4 107.2 1016 431.6
#> Y1988_1 107.6 NA 405.3
#> Y1988_2 110.0 NA 416.6
#> Y1988_3 113.5 NA 432.2
#> Y1988_4 112.6 NA 446.4
#> Y1989_1 113.6 NA 424.1
#> Y1989_2 114.5 NA 436.3
#> Y1989_3 115.6 NA 445.0
#> Y1989_4 114.3 NA 460.6
#> Y1990_1 114.3 NA 442.0
#> Y1990_2 115.5 NA 452.1
#> Y1990_3 118.3 NA 468.9
#> Y1990_4 114.5 NA 482.9
#> Y1991_1 111.4 NA NA
#> Y1991_2 112.4 NA NA
#> Y1991_3 116.0 NA NA
#> Y1991_4 113.9 NA NA
## trim NA's from both ends, up to the firsxst/last full record:
pcfr2to4b <- window(pcfr2to4, start = availStart(pcfr2to4, FALSE),
end = availEnd(pcfr2to4, FALSE))
## TODO: need a better example here since the first non-NA value for all
## ts in pcfr2to4 is at the same
## alternatively, use na.trim(), the default for is.na is "any"
pcpres <- window(pcts(presidents), end = c(1972, 4))
availStart(pcpres) # 1945 2
#> [1] 1945 2
availEnd(pcpres) # 1972 2
#> [1] 1972 2
both <- na.trim(pcpres) # same as "both"
identical(na.trim(pcpres), both) # TRUE
#> [1] TRUE
head(both, 7)
#> An object of class "PeriodicTS"
#> Slot "cycle": Start: 1945 Quarter_2
#> Cycle: QuarterYearCycle
#> Number of seasons: 4
#>
#> Q1 Q2 Q3 Q4
#> Y1945 87 82 75
#> Y1946 63 50 43 32
tail(both)
#> An object of class "PeriodicTS"
#> Slot "cycle": Start: 1971 Quarter_1
#> Cycle: QuarterYearCycle
#> Number of seasons: 4
#>
#> Q1 Q2 Q3 Q4
#> Y1971 51 48 54 49
#> Y1972 49 61
head(na.trim(pcpres, "left"), 7)
#> An object of class "PeriodicTS"
#> Slot "cycle": Start: 1945 Quarter_2
#> Cycle: QuarterYearCycle
#> Number of seasons: 4
#>
#> Q1 Q2 Q3 Q4
#> Y1945 87 82 75
#> Y1946 63 50 43 32
tail(na.trim(pcpres, "right"))
#> An object of class "PeriodicTS"
#> Slot "cycle": Start: 1971 Quarter_1
#> Cycle: QuarterYearCycle
#> Number of seasons: 4
#>
#> Q1 Q2 Q3 Q4
#> Y1971 51 48 54 49
#> Y1972 49 61
cguk <- pcfr[c("CanadaUnemployment", "GermanyGNP", "UKTotalInvestment")]
availStart(cguk)
#> [1] 1955 1
availStart(cguk, TRUE) # same
#> [1] 1955 1
availStart(cguk, FALSE)
#> [1] 1960 1
availEnd(cguk)
#> [1] 1990 4
availEnd(cguk, TRUE) # same
#> [1] 1990 4
availEnd(cguk, FALSE)
#> [1] 1987 4
na.trim(cguk)
#> An object of class "PeriodicMTS"
#> Slot "cycle": Object from built-in class 'QuarterYearCycle'
#> Cycle start: Quarter_1
#>
#> CanadaUnemployment GermanyGNP UKTotalInvestment
#> Y1960_1 583 167.0 7480
#> Y1960_2 427 176.2 7466
#> Y1960_3 334 198.0 7745
#> Y1960_4 439 190.5 8386
#> Y1961_1 703 180.1 8211
#> Y1961_2 480 184.9 8325
#> Y1961_3 325 202.9 8744
#> Y1961_4 358 195.8 8832
#> Y1962_1 561 185.4 8467
#> Y1962_2 373 195.0 8485
#> Y1962_3 282 214.5 8698
#> Y1962_4 346 204.9 8696
#> Y1963_1 546 183.0 7704
#> Y1963_2 372 199.9 8497
#> Y1963_3 272 223.5 8984
#> Y1963_4 305 215.4 9639
#> Y1964_1 463 201.6 9855
#> Y1964_2 326 214.4 9738
#> Y1964_3 243 232.8 10211
#> Y1964_4 266 226.9 10785
#> Y1965_1 397 213.4 10866
#> Y1965_2 298 226.7 10161
#> Y1965_3 210 243.4 10442
#> Y1965_4 214 239.2 11220
#> Y1966_1 303 224.3 11115
#> Y1966_2 237 235.5 10378
#> Y1966_3 236 250.0 10876
#> Y1966_4 230 240.3 11411
#> Y1967_1 340 220.3 11866
#> Y1967_2 294 232.6 11713
#> Y1967_3 260 248.3 11893
#> Y1967_4 290 248.2 12137
#> Y1968_1 411 226.9 12809
#> Y1968_2 365 243.0 11782
#> Y1968_3 332 267.1 12359
#> Y1968_4 324 267.2 13640
#> Y1969_1 405 244.0 12375
#> Y1969_2 368 262.3 11887
#> Y1969_3 329 287.1 12698
#> Y1969_4 346 286.2 13333
#> Y1970_1 455 257.4 12533
#> Y1970_2 490 280.1 12454
#> Y1970_3 479 298.2 12804
#> Y1970_4 479 298.3 13773
#> Y1971_1 601 274.5 12963
#> Y1971_2 545 286.2 12708
#> Y1971_3 494 304.8 13079
#> Y1971_4 502 302.5 13767
#> Y1972_1 591 286.0 13217
#> Y1972_2 540 296.9 12650
#> Y1972_3 536 315.2 12904
#> Y1972_4 545 318.9 13630
#> Y1973_1 599 304.5 14648
#> Y1973_2 501 312.1 13247
#> Y1973_3 476 328.7 13549
#> Y1973_4 483 328.8 14347
#> Y1974_1 564 309.4 14539
#> Y1974_2 494 314.5 12668
#> Y1974_3 488 328.4 13201
#> Y1974_4 512 324.2 14057
#> Y1975_1 738 298.2 13904
#> Y1975_2 685 308.2 12681
#> Y1975_3 670 322.1 13394
#> Y1975_4 666 329.5 13404
#> Y1976_1 776 316.1 14295
#> Y1976_2 717 328.1 12795
#> Y1976_3 709 335.2 13402
#> Y1976_4 703 348.8 13785
#> Y1977_1 909 328.6 13328
#> Y1977_2 838 334.1 12736
#> Y1977_3 826 341.8 13125
#> Y1977_4 824 358.9 14118
#> Y1978_1 999 336.5 14233
#> Y1978_2 930 346.4 13139
#> Y1978_3 878 354.8 13574
#> Y1978_4 827 370.2 13968
#> Y1979_1 966 348.3 14000
#> Y1979_2 856 361.5 13216
#> Y1979_3 759 369.8 14089
#> Y1979_4 762 384.0 15145
#> Y1980_1 953 365.3 14194
#> Y1980_2 907 365.4 12661
#> Y1980_3 816 372.8 13032
#> Y1980_4 784 381.7 13529
#> Y1981_1 951 360.9 12193
#> Y1981_2 864 364.9 11439
#> Y1981_3 840 374.8 12005
#> Y1981_4 937 384.7 12661
#> Y1982_1 1152 357.5 12423
#> Y1982_2 1266 364.8 11839
#> Y1982_3 1384 369.2 12869
#> Y1982_4 1455 379.5 13784
#> Y1983_1 1630 359.4 13485
#> Y1983_2 1515 370.4 12236
#> Y1983_3 1353 376.0 13130
#> Y1983_4 1295 393.1 14625
#> Y1984_1 1497 375.8 14552
#> Y1984_2 1430 375.6 13431
#> Y1984_3 1345 391.2 14504
#> Y1984_4 1325 405.5 15588
#> Y1985_1 1495 375.8 16000
#> Y1985_2 1353 387.2 13542
#> Y1985_3 1236 402.7 14922
#> Y1985_4 1228 412.4 15819
#> Y1986_1 1356 381.0 15393
#> Y1986_2 1245 400.1 13685
#> Y1986_3 1186 411.4 15579
#> Y1986_4 1156 422.2 16636
#> Y1987_1 1358 388.3 15956
#> Y1987_2 1197 402.4 14973
#> Y1987_3 1097 417.5 16859
#> Y1987_4 1016 431.6 18585
head( na.trim(cguk, sides = "left") )
#> An object of class "PeriodicMTS"
#> Slot "cycle": Object from built-in class 'QuarterYearCycle'
#> Cycle start: Quarter_1
#>
#> CanadaUnemployment GermanyGNP UKTotalInvestment
#> Y1960_1 583 167.0 7480
#> Y1960_2 427 176.2 7466
#> Y1960_3 334 198.0 7745
#> Y1960_4 439 190.5 8386
#> Y1961_1 703 180.1 8211
#> Y1961_2 480 184.9 8325
tail( na.trim(cguk, sides = "right") )
#> An object of class "PeriodicMTS"
#> Slot "cycle": Object from built-in class 'QuarterYearCycle'
#> Cycle start: Quarter_1
#>
#> CanadaUnemployment GermanyGNP UKTotalInvestment
#> Y1986_3 1186 411.4 15579
#> Y1986_4 1156 422.2 16636
#> Y1987_1 1358 388.3 15956
#> Y1987_2 1197 402.4 14973
#> Y1987_3 1097 417.5 16859
#> Y1987_4 1016 431.6 18585
head( na.trim(cguk, sides = "left", is.na = "all") )
#> An object of class "PeriodicMTS"
#> Slot "cycle": Object from built-in class 'QuarterYearCycle'
#> Cycle start: Quarter_1
#>
#> CanadaUnemployment GermanyGNP UKTotalInvestment
#> Y1955_1 NA NA 5654
#> Y1955_2 NA NA 5720
#> Y1955_3 NA NA 5925
#> Y1955_4 NA NA 6469
#> Y1956_1 NA NA 6019
#> Y1956_2 NA NA 6019
tail( na.trim(cguk, sides = "right", is.na = "all") )
#> An object of class "PeriodicMTS"
#> Slot "cycle": Object from built-in class 'QuarterYearCycle'
#> Cycle start: Quarter_1
#>
#> CanadaUnemployment GermanyGNP UKTotalInvestment
#> Y1989_3 NA 445.0 NA
#> Y1989_4 NA 460.6 NA
#> Y1990_1 NA 442.0 NA
#> Y1990_2 NA 452.1 NA
#> Y1990_3 NA 468.9 NA
#> Y1990_4 NA 482.9 NA