#r #date #time #statistics #time-series
#r #Дата #время #Статистика #временные ряды
Вопрос:
Я пытаюсь преобразовать данные о потоковом потоке за месяц на нескольких станциях в объекты временных рядов в R, используя функцию ts со следующим кодом:
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)))})
при вводе df_month означает, что это список из 31 фрейма данных. Это структура одного из них:
> 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")
Код, похоже, работает правильно, что приводит к следующему результату:
> 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
>
Однако в среде кода появляется, что данные временных рядов (ts объектов) идут с 1953 по 2021 год, а не до 2020 года.
> str(ts_MonthlyMean[[1]])
Time-Series [1:809] from 1953 to 2021: 25.1 32.2 26.2 11.6 13.6 ...
Любая причина, по которой это происходит, и как я могу ее решить?
В то же время у меня возникают проблемы с применением сезонного наклона Sen к данным, вызывающим следующую ошибку:
> sea.sens.slope(ts_MonthlyMean[[1]])
Error in d[, i] <- .d(dat) :
number of items to replace is not a multiple of replacement length
Комментарии:
1. почему вы используете lapply? в вашем коде вам это не нужно
2. Я использую его для преобразования в объекты ts всех фреймов данных в списке с именем df_MonthyMean
3. список под названием
df_..., возможно, вводит в заблуждение ваших товарищей по команде. Я бы посоветовал вам назвать этоl_...
Ответ №1:
Проблема в том, что sea.sens.slop работает только с полными периодами.
Это работает, как и ожидалось:
trend::sea.sens.slope(window(ts_MonthlyMean[[1]], end = c(2020,5)))
#> [1] 0.01801948
Ваши данные состоят из 68 лет и 5 месяцев. Вы можете использовать sea.sens.slope только на 68 лет. Вот почему я взял window данные.
Причина, по которой вы видите 2021 в:
str(ts_MonthlyMean[[1]])
#> Time-Series [1:809] from 1953 to 2021: 25.1 32.2 26.2 11.6 13.6 ...
это просто потому start , end что точка и по умолчанию округляется в str :
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
Если вы хотите, чтобы оно не было округлено:
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