【发布时间】：2021-02-11 09:30:26
【问题描述】：
我正在尝试使用带有以下代码的函数 ts 将多个站点的月流量数据转换为 R 中的时间序列对象：
ts_MonthlyMean <- lapply(df_MonthyMean, function(x){ts(x$MonthlyMeanStreamflow,
frequency=12,
start=c(x[1,1],x[1,2]),
end=c(tail(x$year,1),tail(x$month,1)))})
输入，df_Monthly 表示它是一个包含 31 个数据帧的列表。这是其中一个的结构：
> str(df_MonthyMean[[1]])
'data.frame': 809 obs. of 3 variables:
$ year : int 1953 1953 1953 1953 1953 1953 1953 1954 1954 1954 ...
$ month : int 6 7 8 9 10 11 12 1 2 3 ...
$ MonthlyMeanStreamflow: num 25.1 32.2 26.2 11.6 13.6 ...
> dput(round(df_MonthyMean[[1]],1))
structure(list(year = c(1953, 1953, 1953, 1953, 1953, 1953, 1953,
1954, 1954, 1954, 1954, 1954, 1954, 1954, 1954, 1954, 1954, 1954,
1954, 1955, 1955, 1955, 1955, 1955, 1955, 1955, 1955, 1955, 1955,
1955, 1955, 1956, 1956, 1956, 1956, 1956, 1956, 1956, 1956, 1956,
1956, 1956, 1956, 1957, 1957, 1957, 1957, 1957, 1957, 1957, 1957,
1957, 1957, 1957, 1957, 1958, 1958, 1958, 1958, 1958, 1958, 1958,
1958, 1958, 1958, 1958, 1958, 1959, 1959, 1959, 1959, 1959, 1959,
1959, 1959, 1959, 1959, 1959, 1959, 1960, 1960, 1960, 1960, 1960,
1960, 1960, 1960, 1960, 1960, 1960, 1960, 1961, 1961, 1961, 1961,
1961, 1961, 1961, 1961, 1961, 1961, 1961, 1961, 1962, 1962, 1962,
1962, 1962, 1962, 1962, 1962, 1962, 1962, 1962, 1962, 1963, 1963,
1963, 1963, 1963, 1963, 1963, 1963, 1963, 1963, 1963, 1963, 1964,
1964, 1964, 1964, 1964, 1964, 1964, 1964, 1964, 1964, 1964, 1964,
1965, 1965, 1965, 1965, 1965, 1965, 1965, 1965, 1965, 1965, 1965,
1965, 1966, 1966, 1966, 1966, 1966, 1966, 1966, 1966, 1966, 1966,
1966, 1966, 1967, 1967, 1967, 1967, 1967, 1967, 1967, 1967, 1967,
1967, 1967, 1967, 1968, 1968, 1968, 1968, 1968, 1968, 1968, 1968,
1968, 1968, 1968, 1968, 1969, 1969, 1969, 1969, 1969, 1969, 1969,
1969, 1969, 1969, 1969, 1969, 1970, 1970, 1970, 1970, 1970, 1970,
1970, 1970, 1970, 1970, 1970, 1970, 1971, 1971, 1971, 1971, 1971,
1971, 1971, 1971, 1971, 1971, 1971, 1971, 1972, 1972, 1972, 1972,
1972, 1972, 1972, 1972, 1972, 1972, 1972, 1972, 1973, 1973, 1973,
1973, 1973, 1973, 1973, 1973, 1973, 1973, 1973, 1973, 1974, 1974,
1974, 1974, 1974, 1974, 1974, 1974, 1974, 1974, 1974, 1974, 1975,
1975, 1975, 1975, 1975, 1975, 1975, 1975, 1975, 1975, 1975, 1975,
1976, 1976, 1976, 1976, 1976, 1976, 1976, 1976, 1976, 1976, 1976,
1976, 1977, 1977, 1977, 1977, 1977, 1977, 1977, 1977, 1977, 1977,
1977, 1977, 1978, 1978, 1978, 1978, 1978, 1978, 1978, 1978, 1978,
1978, 1978, 1978, 1979, 1979, 1979, 1979, 1979, 1979, 1979, 1979,
1979, 1979, 1979, 1979, 1980, 1980, 1980, 1980, 1980, 1980, 1980,
1980, 1980, 1980, 1980, 1980, 1981, 1981, 1981, 1981, 1981, 1981,
1981, 1981, 1981, 1981, 1981, 1981, 1982, 1982, 1982, 1982, 1982,
1982, 1982, 1982, 1982, 1982, 1982, 1982, 1983, 1983, 1983, 1983,
1983, 1983, 1983, 1983, 1983, 1983, 1983, 1983, 1984, 1984, 1984,
1984, 1984, 1984, 1984, 1984, 1984, 1984, 1984, 1984, 1985, 1985,
1985, 1985, 1985, 1985, 1985, 1985, 1985, 1985, 1985, 1985, 1986,
1986, 1986, 1986, 1986, 1986, 1986, 1986, 1986, 1986, 1986, 1986,
1987, 1987, 1987, 1987, 1987, 1987, 1987, 1987, 1987, 1987, 1987,
1987, 1988, 1988, 1988, 1988, 1988, 1988, 1988, 1988, 1988, 1988,
1988, 1988, 1989, 1989, 1989, 1989, 1989, 1989, 1989, 1989, 1989,
1989, 1989, 1989, 1990, 1990, 1990, 1990, 1990, 1990, 1990, 1990,
1990, 1990, 1990, 1990, 1991, 1991, 1991, 1991, 1991, 1991, 1991,
1991, 1991, 1991, 1991, 1991, 1992, 1992, 1992, 1992, 1992, 1992,
1992, 1992, 1992, 1992, 1992, 1992, 1993, 1993, 1993, 1993, 1993,
1993, 1993, 1993, 1993, 1993, 1993, 1993, 1994, 1994, 1994, 1994,
1994, 1994, 1994, 1994, 1994, 1994, 1994, 1994, 1995, 1995, 1995,
1995, 1995, 1995, 1995, 1995, 1995, 1995, 1995, 1995, 1996, 1996,
1996, 1996, 1996, 1996, 1996, 1996, 1996, 1996, 1996, 1996, 1997,
1997, 1997, 1997, 1997, 1997, 1997, 1997, 1997, 1997, 1997, 1997,
1998, 1998, 1998, 1998, 1998, 1998, 1998, 1998, 1998, 1998, 1998,
1998, 1999, 1999, 1999, 1999, 1999, 1999, 1999, 1999, 1999, 1999,
1999, 1999, 2000, 2000, 2000, 2000, 2000, 2000, 2000, 2000, 2000,
2000, 2000, 2000, 2001, 2001, 2001, 2001, 2001, 2001, 2001, 2001,
2001, 2001, 2001, 2001, 2002, 2002, 2002, 2002, 2002, 2002, 2002,
2002, 2002, 2002, 2002, 2002, 2003, 2003, 2003, 2003, 2003, 2003,
2003, 2003, 2003, 2003, 2003, 2003, 2004, 2004, 2004, 2004, 2004,
2004, 2004, 2004, 2004, 2004, 2004, 2004, 2005, 2005, 2005, 2005,
2005, 2005, 2005, 2005, 2005, 2005, 2005, 2005, 2006, 2006, 2006,
2006, 2006, 2006, 2006, 2006, 2006, 2006, 2006, 2006, 2007, 2007,
2007, 2007, 2007, 2007, 2007, 2007, 2007, 2007, 2007, 2007, 2008,
2008, 2008, 2008, 2008, 2008, 2008, 2008, 2008, 2008, 2008, 2008,
2009, 2009, 2009, 2009, 2009, 2009, 2009, 2009, 2009, 2009, 2009,
2009, 2010, 2010, 2010, 2010, 2010, 2010, 2010, 2010, 2010, 2010,
2010, 2010, 2011, 2011, 2011, 2011, 2011, 2011, 2011, 2011, 2011,
2011, 2011, 2011, 2012, 2012, 2012, 2012, 2012, 2012, 2012, 2012,
2012, 2012, 2012, 2012, 2013, 2013, 2013, 2013, 2013, 2013, 2013,
2013, 2013, 2013, 2013, 2013, 2014, 2014, 2014, 2014, 2014, 2014,
2014, 2014, 2014, 2014, 2014, 2014, 2015, 2015, 2015, 2015, 2015,
2015, 2015, 2015, 2015, 2015, 2015, 2015, 2016, 2016, 2016, 2016,
2016, 2016, 2016, 2016, 2016, 2016, 2016, 2016, 2017, 2017, 2017,
2017, 2017, 2017, 2017, 2017, 2017, 2017, 2017, 2017, 2018, 2018,
2018, 2018, 2018, 2018, 2018, 2018, 2018, 2018, 2018, 2018, 2019,
2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019,
2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020),
month = c(6, 7, 8, 9, 10, 11, 12, 1, 2, 3, 4, 5, 6, 7, 8,
9, 10, 11, 12, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 1,
2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 1, 2, 3, 4, 5, 6, 7,
8, 9, 10, 11, 12, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12,
1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 1, 2, 3, 4, 5, 6,
7, 8, 9, 10, 11, 12, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12,
1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 1, 2, 3, 4, 5, 6,
7, 8, 9, 10, 11, 12, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12,
1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 1, 2, 3, 4, 5, 6,
7, 8, 9, 10, 11, 12, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12,
1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 1, 2, 3, 4, 5, 6,
7, 8, 9, 10, 11, 12, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12,
1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 1, 2, 3, 4, 5, 6,
7, 8, 9, 10, 11, 12, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12,
1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 1, 2, 3, 4, 5, 6,
7, 8, 9, 10, 11, 12, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12,
1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 1, 2, 3, 4, 5, 6,
7, 8, 9, 10, 11, 12, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12,
1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 1, 2, 3, 4, 5, 6,
7, 8, 9, 10, 11, 12, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12,
1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 1, 2, 3, 4, 5, 6,
7, 8, 9, 10, 11, 12, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12,
1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 1, 2, 3, 4, 5, 6,
7, 8, 9, 10, 11, 12, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12,
1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 1, 2, 3, 4, 5, 6,
7, 8, 9, 10, 11, 12, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12,
1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 1, 2, 3, 4, 5, 6,
7, 8, 9, 10, 11, 12, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12,
1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 1, 2, 3, 4, 5, 6,
7, 8, 9, 10, 11, 12, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12,
1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 1, 2, 3, 4, 5, 6,
7, 8, 9, 10, 11, 12, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12,
1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 1, 2, 3, 4, 5, 6,
7, 8, 9, 10, 11, 12, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12,
1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 1, 2, 3, 4, 5, 6,
7, 8, 9, 10, 11, 12, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12,
1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 1, 2, 3, 4, 5, 6,
7, 8, 9, 10, 11, 12, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12,
1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 1, 2, 3, 4, 5, 6,
7, 8, 9, 10, 11, 12, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12,
1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 1, 2, 3, 4, 5, 6,
7, 8, 9, 10, 11, 12, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12,
1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 1, 2, 3, 4, 5, 6,
7, 8, 9, 10, 11, 12, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12,
1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 1, 2, 3, 4, 5, 6,
7, 8, 9, 10), MonthlyMeanStreamflow = c(25.1, 32.2, 26.2,
11.6, 13.6, 22.7, 20, 26.5, 38.6, 322.6, 279.7, 68.3, 14.7,
36.5, 87.7, 34.7, 22.5, 29.5, 28.5, 36.6, 46, 67.9, 49.5,
25.1, 14.4, 46, 342.9, 55.8, 26.5, 30.5, 42.9, 42, 80.5,
273.4, 189, 65.1, 17.2, 20.9, 27.4, 9.4, 15.1, 29.1, 28.4,
77.9, 223.1, 257.8, 239.3, 148.1, 56.9, 44, 376.2, 103.7,
61.1, 124.1, 75.5, 47.9, 141.4, 760.8, 1872.3, 649.4, 85.1,
31.6, 53.9, 154, 206.5, 60.2, 51.2, 40.5, 48.5, 66.6, 66.5,
29.7, 19.2, 33.2, 251.5, 60.5, 48.9, 163.5, 109, 205.5, 182.2,
1000.3, 506.9, 131.3, 42.7, 16.5, 20.4, 21.6, 41.8, 36.4,
35.6, 37.7, 46.7, 154.9, 197.2, 40.5, 23.5, 23.3, 32.4, 36.1,
37.9, 124.9, 182.1, 172.7, 654.5, 427.5, 1793.3, 295.2, 56.3,
34.2, 18.2, 41.7, 59.5, 54.4, 46, 50.1, 296.9, 321.1, 289.2,
69.2, 28.8, 32.1, 143.2, 384.6, 165.1, 128, 60.4, 36.6, 36.6,
90.6, 407.2, 111.3, 37.5, 48.5, 117.9, 296.8, 92.6, 50.1,
57.8, 322.2, 344, 549.7, 1282.8, 380.5, 68.1, 122.4, 139.9,
47.2, 34.4, 96.9, 472.7, 391.7, 167.2, 1383.9, 1208.9, 209.6,
39.7, 45.4, 90.8, 87.1, 51.6, 27.3, 45.6, 38.1, 46.6, 70.1,
62, 25.4, 22.3, 84.2, 378, 203.1, 52.5, 49.1, 61.1, 132.4,
537.7, 798.6, 1290.8, 473.8, 77.9, 41.9, 128.3, 36.9, 25.8,
39.2, 33.1, 153.5, 127.2, 325, 876.8, 222.7, 46.2, 27.7,
64.4, 134.7, 37.1, 55.7, 54.5, 50.3, 52.7, 219.4, 315.2,
128.7, 25.3, 24.8, 36.6, 85, 58.5, 26.5, 28.1, 44, 56.5,
78.7, 45.6, 23.1, 15.6, 28.8, 122, 86.4, 564.2, 200.7, 203.1,
151.8, 75.4, 158.5, 43.7, 24.8, 32.6, 24.5, 25.7, 65.2, 1210.7,
299.5, 174.6, 222.9, 276.7, 674.2, 2058.2, 1933, 244.3, 102.8,
54.8, 20.7, 22.3, 38, 36.9, 41.5, 34.7, 131.9, 110, 30, 9.8,
22.3, 75.5, 35, 95.4, 143.6, 48.5, 52.2, 87.2, 819.2, 1052.4,
518.3, 72.7, 47.8, 24.3, 120.6, 23.2, 25.1, 27.7, 32.2, 169.4,
232.8, 507.4, 214.1, 31.4, 37.3, 39.1, 24.2, 18.8, 24.7,
29.9, 32.7, 40.8, 64, 150.1, 50.4, 18, 42.7, 80.6, 48.2,
34.3, 33.4, 31.4, 40.5, 176.5, 1623.7, 1001.5, 222.7, 35.1,
27, 42.5, 23.7, 26.4, 282.7, 915.1, 391.6, 525.6, 1020.9,
2252.7, 800.2, 239.3, 57.4, 62, 21.5, 23, 32, 24.5, 92.9,
1036, 812, 1644.8, 890.7, 136.9, 61.8, 79.4, 76.2, 27.2,
40.7, 40.4, 28.3, 40.2, 194.2, 447.2, 120.8, 36.5, 51.8,
77.1, 61.5, 69.6, 38.3, 43.2, 57.2, 195.2, 664.1, 759, 337.8,
60.5, 30.7, 100.3, 75.1, 18.8, 48.7, 195.2, 173, 374.9, 1102.8,
1707.8, 1262, 230.1, 55.4, 97.4, 171.7, 851.3, 96.1, 196.7,
256.2, 171.8, 322.4, 260.1, 107.5, 22.6, 25.3, 68, 80.8,
268.8, 107, 602.6, 503.5, 611, 1863.1, 1336.3, 552.2, 108.8,
61.2, 123.4, 75.6, 100.6, 73.2, 82.7, 50.5, 329.9, 759.4,
538.3, 82, 39.3, 54.4, 47.5, 48.8, 76.9, 368, 227.4, 96.8,
232.2, 741.5, 1341.7, 411.7, 69.9, 39.7, 76.4, 39.4, 39.9,
97.4, 37, 35.1, 331.1, 457.2, 701.1, 328.1, 60.1, 54.6, 311.3,
366.2, 60.9, 51, 47.3, 71.3, 96.9, 389.8, 126.3, 42.7, 21.6,
14.1, 48.7, 29.5, 33.1, 32.2, 35.4, 45.8, 49.9, 108.4, 84.1,
44, 23.8, 49.8, 60.7, 49.8, 61.7, 59.2, 201.1, 308.5, 286,
1004.2, 1432.8, 394, 75.8, 50.5, 82.2, 131.3, 36.9, 56.7,
130.3, 135.2, 400.3, 864.7, 1120.7, 406.4, 226.6, 57.8, 202.7,
79.7, 46.2, 52.8, 240, 1570.8, 984.8, 1577, 1926.7, 687.2,
157.3, 62.2, 61.7, 61, 45.9, 49.1, 50.1, 41.5, 57.7, 458.3,
242.7, 79.1, 27.3, 17.2, 40.9, 182.6, 50.7, 505.3, 346.1,
249, 986.1, 1164.9, 429.3, 227.9, 69, 30.1, 54.9, 54.2, 30.4,
33.1, 23.8, 23, 39.5, 30.1, 32, 22.5, 25.2, 44.6, 66.8, 85.8,
58.5, 101, 79.2, 108.1, 194.7, 677.3, 342.1, 109.5, 34.7,
29.8, 39.7, 42.7, 32.1, 36.3, 37.7, 54.7, 124.5, 802, 1032.1,
465.5, 66.5, 50.6, 38.2, 31.2, 40, 37.1, 43, 38.6, 39.3,
31.2, 91.1, 33.8, 30.9, 67.3, 509, 120.3, 41.3, 38.2, 40.4,
40.1, 35.2, 44.9, 43.5, 31.2, 34.9, 34.2, 44.4, 27.5, 219,
332.3, 100.6, 52.6, 115.6, 426.8, 658.7, 152.7, 32, 33.4,
69.5, 35.3, 29.7, 32.3, 38.3, 36.2, 35.1, 35.2, 27, 23.1,
21.3, 50.8, 52.6, 74.4, 28.6, 47.4, 40.3, 61.3, 166, 629.8,
413.9, 102.5, 31.1, 29.2, 44.4, 121.1, 30.4, 67.4, 41, 42.5,
60.7, 564.9, 375.6, 65.6, 25, 37.7, 30.7, 29.7, 30, 47.2,
127.6, 358.6, 1124.4, 591.4, 766, 229.9, 50.9, 29.3, 53,
38.9, 30.3, 34, 32.7, 30.9, 30.4, 41.3, 53.1, 24.6, 24.9,
44.5, 599.1, 149.7, 79.6, 43.5, 36.4, 41, 95.2, 321.3, 145.9,
53.8, 32.4, 32.7, 203.4, 70.9, 54.2, 48.1, 152.8, 393.6,
600.7, 991.5, 532, 156.2, 50.5, 67.8, 138.4, 296.8, 97.1,
39.7, 46.1, 169.8, 235.3, 697.6, 256.7, 103.5, 42.3, 36.5,
30.6, 34.4, 32.6, 35.7, 38, 128.3, 164.4, 661.3, 1280.2,
390.9, 52.6, 63.8, 129.6, 44.9, 29.3, 29, 36, 35.8, 35.4,
63.9, 45.2, 25.3, 26.5, 64.9, 163, 96, 39.7, 27.8, 36.3,
64.1, 108.8, 335, 153.5, 33.6, 25.2, 33.1, 63.2, 52.7, 23.7,
27.9, 31.5, 86.1, 153.4, 481, 174.5, 48.6, 25.7, 203.1, 193.5,
578.7, 88.5, 55.3, 67.8, 42.6, 44, 115.3, 41.8, 26.5, 23.6,
47, 194.9, 136.4, 131.5, 49.1, 66, 99.4, 327.8, 203, 72.3,
43.1, 28.1, 178.9, 145.1, 168.8, 149.3, 374.5, 126.5, 88.4,
557.6, 281, 85.1, 41.8, 31.4, 32.9, 44.5, 26.2, 36.6, 48.3,
328.5, 527.2, 934.2, 684.3, 205.6, 63.3, 27.4, 66.8, 188.7,
30.4, 31.4, 22.3, 24.4, 26.9, 32.5, 32.9, 23.5, 24.7, 19.5,
29.9, 42.5, 43.9, 61.1, 33.4, 29, 75.5, 525.9, 1537.6, 611.8,
154, 46.4, 28.4, 41, 35.9, 31.6, 40.4, 217.2, 152.1, 393.1,
1191.2, 383.4, 78.7, 29.1, 33.9, 29.9, 24.1, 25.3)), row.names = c(NA,
-809L), class = "data.frame")
代码似乎工作正常，产生以下结果：
> 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
>
但是，在代码环境中，时间序列数据（对象 ts）似乎是从 1953 年到 2021，而不是到 2020 年。
> str(ts_MonthlyMean[[1]])
Time-Series [1:809] from 1953 to 2021: 25.1 32.2 26.2 11.6 13.6 ...
发生这种情况的任何原因以及我该如何解决？
同时，我在将季节性森斜率应用于数据时遇到问题，出现以下错误：
> sea.sens.slope(ts_MonthlyMean[[1]])
Error in d[, i] <- .d(dat) :
number of items to replace is not a multiple of replacement length
【问题讨论】：
-
你为什么要使用 lapply？在你的代码中你不需要那个
-
我正在使用它来将列表中名为 df_MonthyMean 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。我建议你叫它l_...
标签： r date time statistics time-series