R - XTS : Get the first dates and values for each month from a daily time series with missing rows

Question

我在 R 中有一个作为 myxts xts 对象的每日时间序列，日期格式为 d/m/y。现在，我想将原始时间序列减少为仅采用序列中每个月的第一个日期和值的时间序列。

myxts[.indexmday(myxts) == 1] 返回一个包含 d/m/y 且 d = 1 的系列。

我的问题是我每个月需要 1 个数据，而我的问题是我的原始系列有一些月份没有日期和第一个日历日期的数据。

如果没有这样的日期，我如何对 R 说，然后取该特定月份的第二天，如果后者不可用，则取第三天，依此类推。 ..

例如，在下面的示例数据中，没有 2014 年 2 月 1 日。也没有 1 月 1 日。

dates <- c('14/02/2014', '13/02/2014', '12/02/2014', '11/02/2014', '10/02/2014', '07/02/2014', '06/02/2014', '05/02/2014', '04/02/2014', '03/02/2014', '31/01/2014', '30/01/2014', '29/01/2014', '28/01/2014', '27/01/2014', '24/01/2014', '23/01/2014', '22/01/2014', '21/01/2014', '20/01/2014', '17/01/2014', '16/01/2014', '15/01/2014', '14/01/2014', '13/01/2014', '10/01/2014', '09/01/2014', '08/01/2014', '07/01/2014', '06/01/2014', '03/01/2014', '02/01/2014', '31/12/2013', '30/12/2013', '27/12/2013', '26/12/2013', '24/12/2013', '23/12/2013', '20/12/2013', '19/12/2013', '18/12/2013', '17/12/2013', '16/12/2013', '13/12/2013', '12/12/2013', '11/12/2013', '10/12/2013', '09/12/2013', '06/12/2013', '05/12/2013', '04/12/2013', '03/12/2013', '02/12/2013', '29/11/2013', '28/11/2013', '27/11/2013', '26/11/2013', '25/11/2013', '22/11/2013', '21/11/2013', '20/11/2013', '19/11/2013', '18/11/2013', '15/11/2013', '14/11/2013', '13/11/2013', '12/11/2013', '11/11/2013', '08/11/2013', '07/11/2013', '06/11/2013', '05/11/2013', '04/11/2013', '01/11/2013', '31/10/2013', '30/10/2013', '29/10/2013', '28/10/2013', '25/10/2013', '24/10/2013', '23/10/2013', '22/10/2013', '21/10/2013', '18/10/2013', '17/10/2013', '16/10/2013', '15/10/2013', '14/10/2013', '11/10/2013', '10/10/2013', '09/10/2013', '08/10/2013', '07/10/2013', '04/10/2013', '03/10/2013', '02/10/2013', '01/10/2013', '30/09/2013', '27/09/2013', '26/09/2013', '25/09/2013', '24/09/2013', '23/09/2013', '20/09/2013', '19/09/2013', '18/09/2013', '17/09/2013', '16/09/2013', '13/09/2013', '12/09/2013', '11/09/2013', '10/09/2013', '09/09/2013', '06/09/2013', '05/09/2013', '04/09/2013', '03/09/2013', '02/09/2013', '30/08/2013', '29/08/2013', '28/08/2013', '27/08/2013', '26/08/2013', '23/08/2013', '22/08/2013', '21/08/2013', '20/08/2013', '19/08/2013', '16/08/2013', '15/08/2013', '14/08/2013', '13/08/2013', '12/08/2013', '09/08/2013', '08/08/2013', '07/08/2013', '06/08/2013', '05/08/2013', '02/08/2013', '01/08/2013', '31/07/2013', '30/07/2013', '29/07/2013', '26/07/2013', '25/07/2013', '24/07/2013', '23/07/2013', '22/07/2013', '19/07/2013', '18/07/2013', '17/07/2013', '16/07/2013', '15/07/2013', '12/07/2013', '11/07/2013', '10/07/2013', '09/07/2013', '08/07/2013', '05/07/2013', '04/07/2013', '03/07/2013', '02/07/2013', '01/07/2013', '28/06/2013') 
values <- c(920.25, 918.5, 921.5, 921.5, 921, 919, 906.25, 899, 906.25, 903, 917, 924, 923.75, 917.5, 914.5, 921.75, 922.5, 919, 919, 907.75, 916.25, 907.5, 913.75, 900.25, 907, 907.25, 907.25, 912.25, 910.5, 910.25, 910.25, 923.5, 944.25, 945.5, 955.75, 950, 944.25, 945.75, 945.25, 935.25, 929, 919.5, 931.5, 917.75, 932, 932, 928, 934.25, 940.75, 943.75, 947.25, 945.75, 942.75, 943, 942, 941, 944.5, 934.75, 937.75, 923.25, 911, 910.75, 910.25, 911.25, 908.75, 901.25, 903.25, 903.25, 893, 888.5, 905, 903, 904, 915, 932.5, 937.25, 930.5, 925.75, 909.75, 911.5, 920, 936.5, 941.5, 939.25, 931.5, 945, 940.25, 931.5, 933.5, 925.25, 925.25, 935.5, 926.25, 922.5, 926.5, 922.5, 905.25, 913, 927.25, 920.5, 919, 906, 913, 925.25, 934.5, 924.25, 925.75, 944.25, 949.75, 943.5, 943.5, 938.75, 960, 971, 963.75, 960.75, 960.25, 958.5, 969.5, 978.75, 970, 961.75, 940.25, 941.75, 936.25, 938.75, 938.75, 942.25, 941.25, 937.25, 926, 927.75, 903.75, 903.75, 896, 906, 909.25, 920.25, 923.75, 927, 910.5, 908, 913.75, 910.5, 913.75, 914.25, 919.75, 918.5, 925.75, 928.75, 923.75, 919.75, 916.5, 916.25, 913.75, 913.75, 909.25, 913.25, 911.75, 902.25, 903.25, 879.75, 883.25, 883.25)
myxts <- as.xts(values, order.by = as.Date(dates, format = '%d/%m/%Y'))

Answer 1

有一个未导出的 startof 函数在这里可能有用

myxts[xts:::startof(myxts, "months")]
             [,1]
2013-06-28 883.25
2013-07-01 883.25
2013-08-01 927.00
2013-09-02 958.50
2013-10-01 905.25
2013-11-01 915.00
2013-12-02 942.75
2014-01-02 923.50
2014-02-03 903.00