R中的Ts对象
我正在尝试使用函数ts和以下代码将几个站点的月径流数据转换为R中的时间序列对象：R中的Ts对象,r,date,time,statistics,time-series,R,Date,Time,Statistics,Time Series,我正在尝试使用函数ts和以下代码将几个站点的月径流数据转换为R中的时间序列对象： ts_MonthlyMean <- lapply(df_MonthyMean, function(x){ts(x$MonthlyMeanStreamflow, frequency=12,
ts_MonthlyMean <- lapply(df_MonthyMean, function(x){ts(x$MonthlyMeanStreamflow,
frequency=12,
start=c(x[1,1],x[1,2]),
end=c(tail(x$year,1),tail(x$month,1)))})
> round(ts_MonthlyMean[[1]],1)
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
1953 25.1 32.2 26.2 11.6 13.6 22.7 20.0
1954 26.5 38.6 322.6 279.7 68.3 14.7 36.5 87.7 34.7 22.5 29.5 28.5
1955 36.6 46.0 67.9 49.5 25.1 14.4 46.0 342.9 55.8 26.5 30.5 42.9
1956 42.0 80.5 273.4 189.0 65.1 17.2 20.9 27.4 9.4 15.1 29.1 28.4
1957 77.9 223.1 257.8 239.3 148.1 56.9 44.0 376.2 103.7 61.1 124.1 75.5
1958 47.9 141.4 760.8 1872.3 649.4 85.1 31.6 53.9 154.0 206.5 60.2 51.2
1959 40.5 48.5 66.6 66.5 29.7 19.2 33.2 251.5 60.5 48.9 163.5 109.0
1960 205.5 182.2 1000.3 506.9 131.3 42.7 16.5 20.4 21.6 41.8 36.4 35.6
1961 37.7 46.7 154.9 197.2 40.5 23.5 23.3 32.4 36.1 37.9 124.9 182.1
1962 172.7 654.5 427.5 1793.3 295.2 56.3 34.2 18.2 41.7 59.5 54.4 46.0
1963 50.1 296.9 321.1 289.2 69.2 28.8 32.1 143.2 384.6 165.1 128.0 60.4
1964 36.6 36.6 90.6 407.2 111.3 37.5 48.5 117.9 296.8 92.6 50.1 57.8
1965 322.2 344.0 549.7 1282.8 380.5 68.1 122.4 139.9 47.2 34.4 96.9 472.7
1966 391.7 167.2 1383.9 1208.9 209.6 39.7 45.4 90.8 87.1 51.6 27.3 45.6
1967 38.1 46.6 70.1 62.0 25.4 22.3 84.2 378.0 203.1 52.5 49.1 61.1
1968 132.4 537.7 798.6 1290.8 473.8 77.9 41.9 128.3 36.9 25.8 39.2 33.1
1969 153.5 127.2 325.0 876.8 222.7 46.2 27.7 64.4 134.7 37.1 55.7 54.5
1970 50.3 52.7 219.4 315.2 128.7 25.3 24.8 36.6 85.0 58.5 26.5 28.1
1971 44.0 56.5 78.7 45.6 23.1 15.6 28.8 122.0 86.4 564.2 200.7 203.1
1972 151.8 75.4 158.5 43.7 24.8 32.6 24.5 25.7 65.2 1210.7 299.5 174.6
1973 222.9 276.7 674.2 2058.2 1933.0 244.3 102.8 54.8 20.7 22.3 38.0 36.9
1974 41.5 34.7 131.9 110.0 30.0 9.8 22.3 75.5 35.0 95.4 143.6 48.5
1975 52.2 87.2 819.2 1052.4 518.3 72.7 47.8 24.3 120.6 23.2 25.1 27.7
1976 32.2 169.4 232.8 507.4 214.1 31.4 37.3 39.1 24.2 18.8 24.7 29.9
1977 32.7 40.8 64.0 150.1 50.4 18.0 42.7 80.6 48.2 34.3 33.4 31.4
1978 40.5 176.5 1623.7 1001.5 222.7 35.1 27.0 42.5 23.7 26.4 282.7 915.1
1979 391.6 525.6 1020.9 2252.7 800.2 239.3 57.4 62.0 21.5 23.0 32.0 24.5
1980 92.9 1036.0 812.0 1644.8 890.7 136.9 61.8 79.4 76.2 27.2 40.7 40.4
1981 28.3 40.2 194.2 447.2 120.8 36.5 51.8 77.1 61.5 69.6 38.3 43.2
1982 57.2 195.2 664.1 759.0 337.8 60.5 30.7 100.3 75.1 18.8 48.7 195.2
1983 173.0 374.9 1102.8 1707.8 1262.0 230.1 55.4 97.4 171.7 851.3 96.1 196.7
1984 256.2 171.8 322.4 260.1 107.5 22.6 25.3 68.0 80.8 268.8 107.0 602.6
1985 503.5 611.0 1863.1 1336.3 552.2 108.8 61.2 123.4 75.6 100.6 73.2 82.7
1986 50.5 329.9 759.4 538.3 82.0 39.3 54.4 47.5 48.8 76.9 368.0 227.4
1987 96.8 232.2 741.5 1341.7 411.7 69.9 39.7 76.4 39.4 39.9 97.4 37.0
1988 35.1 331.1 457.2 701.1 328.1 60.1 54.6 311.3 366.2 60.9 51.0 47.3
1989 71.3 96.9 389.8 126.3 42.7 21.6 14.1 48.7 29.5 33.1 32.2 35.4
1990 45.8 49.9 108.4 84.1 44.0 23.8 49.8 60.7 49.8 61.7 59.2 201.1
1991 308.5 286.0 1004.2 1432.8 394.0 75.8 50.5 82.2 131.3 36.9 56.7 130.3
1992 135.2 400.3 864.7 1120.7 406.4 226.6 57.8 202.7 79.7 46.2 52.8 240.0
1993 1570.8 984.8 1577.0 1926.7 687.2 157.3 62.2 61.7 61.0 45.9 49.1 50.1
1994 41.5 57.7 458.3 242.7 79.1 27.3 17.2 40.9 182.6 50.7 505.3 346.1
1995 249.0 986.1 1164.9 429.3 227.9 69.0 30.1 54.9 54.2 30.4 33.1 23.8
1996 23.0 39.5 30.1 32.0 22.5 25.2 44.6 66.8 85.8 58.5 101.0 79.2
1997 108.1 194.7 677.3 342.1 109.5 34.7 29.8 39.7 42.7 32.1 36.3 37.7
1998 54.7 124.5 802.0 1032.1 465.5 66.5 50.6 38.2 31.2 40.0 37.1 43.0
1999 38.6 39.3 31.2 91.1 33.8 30.9 67.3 509.0 120.3 41.3 38.2 40.4
2000 40.1 35.2 44.9 43.5 31.2 34.9 34.2 44.4 27.5 219.0 332.3 100.6
2001 52.6 115.6 426.8 658.7 152.7 32.0 33.4 69.5 35.3 29.7 32.3 38.3
2002 36.2 35.1 35.2 27.0 23.1 21.3 50.8 52.6 74.4 28.6 47.4 40.3
2003 61.3 166.0 629.8 413.9 102.5 31.1 29.2 44.4 121.1 30.4 67.4 41.0
2004 42.5 60.7 564.9 375.6 65.6 25.0 37.7 30.7 29.7 30.0 47.2 127.6
2005 358.6 1124.4 591.4 766.0 229.9 50.9 29.3 53.0 38.9 30.3 34.0 32.7
2006 30.9 30.4 41.3 53.1 24.6 24.9 44.5 599.1 149.7 79.6 43.5 36.4
2007 41.0 95.2 321.3 145.9 53.8 32.4 32.7 203.4 70.9 54.2 48.1 152.8
2008 393.6 600.7 991.5 532.0 156.2 50.5 67.8 138.4 296.8 97.1 39.7 46.1
2009 169.8 235.3 697.6 256.7 103.5 42.3 36.5 30.6 34.4 32.6 35.7 38.0
2010 128.3 164.4 661.3 1280.2 390.9 52.6 63.8 129.6 44.9 29.3 29.0 36.0
2011 35.8 35.4 63.9 45.2 25.3 26.5 64.9 163.0 96.0 39.7 27.8 36.3
2012 64.1 108.8 335.0 153.5 33.6 25.2 33.1 63.2 52.7 23.7 27.9 31.5
2013 86.1 153.4 481.0 174.5 48.6 25.7 203.1 193.5 578.7 88.5 55.3 67.8
2014 42.6 44.0 115.3 41.8 26.5 23.6 47.0 194.9 136.4 131.5 49.1 66.0
2015 99.4 327.8 203.0 72.3 43.1 28.1 178.9 145.1 168.8 149.3 374.5 126.5
2016 88.4 557.6 281.0 85.1 41.8 31.4 32.9 44.5 26.2 36.6 48.3 328.5
2017 527.2 934.2 684.3 205.6 63.3 27.4 66.8 188.7 30.4 31.4 22.3 24.4
2018 26.9 32.5 32.9 23.5 24.7 19.5 29.9 42.5 43.9 61.1 33.4 29.0
2019 75.5 525.9 1537.6 611.8 154.0 46.4 28.4 41.0 35.9 31.6 40.4 217.2
2020 152.1 393.1 1191.2 383.4 78.7 29.1 33.9 29.9 24.1 25.3
>
> str(ts_MonthlyMean[[1]])
Time-Series [1:809] from 1953 to 2021: 25.1 32.2 26.2 11.6 13.6 ...
> sea.sens.slope(ts_MonthlyMean[[1]])
Error in d[, i] <- .d(dat) :
number of items to replace is not a multiple of replacement length
>sea.sens.slope（ts_MonthlyMean[[1]]
d[，i]中的错误问题是sea.sens.slop
仅对完整周期有效
这与预期的效果一样：
trend::sea.sens.slope(window(ts_MonthlyMean[[1]], end = c(2020,5)))
#> [1] 0.01801948
您的数据由68年零5个月组成。您只能在68年内使用sea.sens.slope
。这就是为什么我选择了一个窗口
您在中看到2021的原因：
str(ts_MonthlyMean[[1]])
#> Time-Series [1:809] from 1953 to 2021: 25.1 32.2 26.2 11.6 13.6 ...
这只是因为开始
和结束
点在str
中默认为四舍五入：
tsp(ts_MonthlyMean[[1]])
#> [1] 1953.417 2020.750 12.000
oo <- options(digits = 3) # change options the same way str does
tsp(ts_MonthlyMean[[1]])
#> [1] 1953 2021 12
options(oo) # reset options
tsp（ts_MonthlyMean[[1]]
#> [1] 1953.417 2020.750 12.000
oo[1]1953 2021 12
选项（oo）#重置选项
如果希望看到它不是四舍五入的：
str(ts_MonthlyMean[[1]])
#> Time-Series [1:809] from 1953 to 2021: 25.1 32.2 26.2 11.6 13.6 ...
# change str options
stro <- getOption("str")
stro$digits.d <- 7
oo <- options(str = stro)
str(ts_MonthlyMean[[1]])
#> Time-Series [1:809] from 1953.417 to 2020.75: 25.1 32.2 26.2 11.6 13.6 22.7 20 26.5 38.6 322.6 ...
options(oo) # reset options
str（ts_monthlyman[[1]]
#>1953年至2021年的时间序列[1:809]：25.132.226.211.613.6。。。
#更改str选项
stro你为什么要用Lappy？在您的代码中，您不需要这样做。我使用它将列表中名为df_MonthyMeana的所有数据帧转换为ts对象一个名为df_…
的列表可能会误导您的队友。我建议你把它叫做l\u…
str(ts_MonthlyMean[[1]])
#> Time-Series [1:809] from 1953 to 2021: 25.1 32.2 26.2 11.6 13.6 ...
# change str options
stro <- getOption("str")
stro$digits.d <- 7
oo <- options(str = stro)
str(ts_MonthlyMean[[1]])
#> Time-Series [1:809] from 1953.417 to 2020.75: 25.1 32.2 26.2 11.6 13.6 22.7 20 26.5 38.6 322.6 ...
options(oo) # reset options