我正在尝试构建一个面板数据数据框，它由应该相互分配的周期性和“连续”每日数据组成，以便新数据框的每一行都具有周期和值对于周期性数据以及该期间内某一天的值和日期，数据看起来类似于:
> dailycds
Date CDS
1 30-06-2015 194
2 01-07-2015 195
3 02-07-2015 198
4 03-07-2015 198
5 04-07-2015 199
6 30-06-2016 165
7 01-07-2016 172
8 02-07-2016 213
9 03-07-2016 123
10 04-07-2016 321
> periodicassets
Period Assets
1 201506 1314
2 201606 2134
最终，我希望它看起来像这样:
> df
Period Date Assets CDS
1 201506 30-06-2015 1314 194
2 201506 01-07-2015 1314 195
3 201506 02-07-2015 1314 198
4 201506 03-07-2015 1314 198
5 201606 30-06-2016 2134 165
6 201606 01-07-2016 2134 172
7 201606 02-07-2016 2134 213
8 201606 03-07-2016 2134 123
基本上，我们的想法是从日常数据中获取特定范围的行，并将它们分配(并合并)到周期性数据中。不幸的是，我不能简单地通过提取日期的 mm-yyyy 部分来做到这一点，因为 201506 期间也包含 7 月到第三天的数据，而第四个与任何期间无关，应该被删除，因为每个期间都应该仅包含特定天数(在本例中为 4)。
这是上面示例数据的代码:
dailycds = data.frame(Date = c("30-06-2015", "01-07-2015", "02-07-2015","03-07-2015","04-07-2015","30-06-2016", "01-07-2016", "02-07-2016","03-07-2016","04-07-2016"),
CDS = c(194, 195, 198,198,199,165,172,213,123,321))
dailycds
periodicassets = data.frame(Period = c("201506", "201606"),
Assets = c("1314","2134"))
periodicassets
df = data.frame(Period = c("201506", "201506", "201506", "201506", "201606", "201606", "201606", "201606"),
Date = c("30-06-2015", "01-07-2015", "02-07-2015","03-07-2015", "30-06-2016", "01-07-2016", "02-07-2016", "03-07-2016"),
Assets = c("1314", "1314", "1314", "1314", "2134", "2134", "2134", "2134"),
CDS = c(194, 195, 198, 198, 165, 172, 213, 123))
背景和其他并发症
因此，正如给定解决方案中所建议的那样，我之前的示例非常具体并且可能过于简化。因此，为了更接近我的问题，这里有一些额外的上下文: 最终，定期数据指的是银行 Assets 的月末持有量，我想在月末前 3 天和月末后 6 天左右的时间(例如)分配每日 CDS 数据。因此，在面板中当然有多家银行，对于每家银行，(相同的)CDS 数据都必须分配给其持有量。 (例如，如果我有 2 家银行，我需要在月底前 3 天和月底后 6 天，我有 (3+1+6)*2 天。)正如评论中所指出的，我总是指商业/工作我的问题是几天，因为我的时间序列不包含任何假期等。
因此，为了公正地解决这个问题，这里是原文中只有一个句点的片段:
> periodicassets
BankName Period value
2 BPCE 201412 112189.50
4 Credit Agricole 201412 81618.76
Date CDS
<dttm> <chr>
1 2015-01-12 46.869
2 2015-01-09 48.121000000000002
3 2015-01-08 48.625999999999998
4 2015-01-07 48.801000000000002
5 2015-01-06 48.633000000000003
6 2015-01-05 46.670999999999999
7 2015-01-02 45.158000000000001
8 2015-01-01 47.32
9 2014-12-31 47.658000000000001
10 2014-12-30 45.843000000000004
11 2014-12-29 47.588999999999999
12 2014-12-26 47.625999999999998
13 2014-12-25 47.697000000000003
14 2014-12-24 47.414999999999999
15 2014-12-23 48.075000000000003
16 2014-12-22 48.085999999999999
17 2014-12-19 47.496000000000002
18 2014-12-18 46.534999999999997
19 2014-12-17 48.149000000000001
可在此处访问:periodic assets , dailycds
在浏览论坛时，我发现了类似的问题，例如: create an index for aggregating daily data to match periodic data和 create an index for aggregating daily data to match periodic data ，然而，当第一个尝试聚合数据时，第二个已经拥有我想要的格式(在对象 xtime 中)。
最佳答案
这道题的关键问题是Period是如何映射到Date的。从OP的描述中我了解到每个期间包括实际月份的最后一天加上下个月的前三天，总共4天。
这可以通过一些日期算法和右连接来解决:
library(data.table)
result <-
# coerce to data.table
setDT(dailycds)[
# compute period by subtracting 3 days of date
, Period := format(as.IDate(Date, "%d-%m-%Y") - 3L, "%Y%m")][
# right join, dropping all rows from dailycds without matching period
periodicassets, on = "Period"][
# change column order to be in line with expected result df
, setcolorder(.SD, names(df))]
result
Period Date Assets CDS 1: 201506 30-06-2015 1314 194 2: 201506 01-07-2015 1314 195 3: 201506 02-07-2015 1314 198 4: 201506 03-07-2015 1314 198 5: 201606 30-06-2016 2134 165 6: 201606 01-07-2016 2134 172 7: 201606 02-07-2016 2134 213 8: 201606 03-07-2016 2134 123
请求的每个周期只有4行，结果符合预期结果 df:
all.equal(df, as.data.frame(result[, lapply(.SD, forcats::fct_drop)]))
[1] TRUE
必须删除未使用的级别才能通过 all.equal() 的严格检查
警告
代码已经过测试，可以与提供的示例数据一起使用。对于连续的每日数据和周期性数据，可能需要添加代码以删除不属于 4 天周期的日期。
编辑:更真实的示例数据
OP 更新了他的问题，并通过 dropbox 提供了更真实的样本数据。现在，dailycds 包含每日数据(周末除外)。正如上面的警告 中所述，这需要过滤相关日期的dailycds。
OP 不清楚如何定义月初和之后要考虑的天数。在这里，我们假设月底前 3 天和月底后 6 天指的是日历天而不是工作日。
# define day range of interest relativ to turn of the month
days_before <- 3L
days_after <- 6L
stopifnot(days_before + days_after < 28)
# read data from dropbox links, note ?dl=1
dailycds <- readRDS(url("https://www.dropbox.com/s/r7v5dq6la0mnn71/dailycds.RDS?dl=1"))
periodicassets <-
readRDS(url("https://www.dropbox.com/s/gdflcngwp8nm552/periodicassets.RDS?dl=1"))
library(data.table)
# coerce to data.table
setDT(dailycds)[
# filter calendar dates
mday(Date) <= days_after | mday(Date) > lubridate::days_in_month(Date) - days_before][
# compute period by shifting dates from next month into actual month
# coersion to IDate is required because Date is of class POSIXct
, Period := format(as.IDate(Date) - days_after, "%Y%m")][
# right join, dropping all rows from dailycds without matching period
setDT(periodicassets), on = "Period"][]
Date CDS Period BankName value 1: 2015-01-06 48.633000000000003 201412 BPCE 112189.50 2: 2015-01-05 46.670999999999999 201412 BPCE 112189.50 3: 2015-01-02 45.158000000000001 201412 BPCE 112189.50 4: 2015-01-01 47.32 201412 BPCE 112189.50 5: 2014-12-31 47.658000000000001 201412 BPCE 112189.50 6: 2014-12-30 45.843000000000004 201412 BPCE 112189.50 7: 2014-12-29 47.588999999999999 201412 BPCE 112189.50 8: 2015-02-06 47.265000000000001 201501 BPCE 103142.06 9: 2015-02-05 47.073999999999998 201501 BPCE 103142.06 10: 2015-02-04 46.634999999999998 201501 BPCE 103142.06 11: 2015-02-03 46.405000000000001 201501 BPCE 103142.06 12: 2015-02-02 47.567 201501 BPCE 103142.06 13: 2015-01-30 47.396000000000001 201501 BPCE 103142.06 14: 2015-01-29 48.448999999999998 201501 BPCE 103142.06 15: 2015-01-06 48.633000000000003 201412 Credit Agricole 81618.76 16: 2015-01-05 46.670999999999999 201412 Credit Agricole 81618.76 ... 26: 2015-02-02 47.567 201501 Credit Agricole 73987.36 27: 2015-01-30 47.396000000000001 201501 Credit Agricole 73987.36 28: 2015-01-29 48.448999999999998 201501 Credit Agricole 73987.36 Date CDS Period BankName value
编辑 2:使用工作日而不是日历日期。
The OP has clarified 他使用工作日而不是日历日。规范的这一看似微小的变化对要包含的日期的选择方式产生了严重影响。
现在，总是选择每个月的前 6 个条目以及该月最后一个交易日 (ultimo) 之前的最后 3 个条目和 ultimo 本身，这导致 3 + 1 + 6 = 10工作日可供选择。
# define range of business days relative to the last trading day (ultimo)
days_before <- 3L
days_after <- 6L
stopifnot(days_before + days_after < 28)
library(data.table)
# read data from dropbox links, note ?dl=1
dailycds <- readRDS(url("https://www.dropbox.com/s/r7v5dq6la0mnn71/dailycds.RDS?dl=1"))
periodicassets <- readRDS(url("https://www.dropbox.com/s/gdflcngwp8nm552/periodicassets.RDS?dl=1"))
# coerce to data.table
setDT(dailycds)[
# filter business dates:
# for each month pick the first days_after business days into the month
# and the last days_before biz days before and including ultimo
dailycds[, c(head(.I, days_after), tail(.I, days_before + 1L)),
by = .(year(Date), month(Date))]$V1][
# compute period by shifting dates from next month into actual month
# coersion to IDate is required because Date is of class POSIXct
, Period := format(as.IDate(Date) - days_after, "%Y%m")][
# right join, dropping all rows from dailycds without matching period
setDT(periodicassets), on = "Period"][]
Date CDS Period BankName value 1: 2015-01-06 48.633000000000003 201412 BPCE 112189.50 2: 2015-01-05 46.670999999999999 201412 BPCE 112189.50 3: 2015-01-02 45.158000000000001 201412 BPCE 112189.50 4: 2015-01-01 47.32 201412 BPCE 112189.50 5: 2014-12-31 47.658000000000001 201412 BPCE 112189.50 6: 2014-12-30 45.843000000000004 201412 BPCE 112189.50 7: 2014-12-29 47.588999999999999 201412 BPCE 112189.50 8: 2014-12-26 47.625999999999998 201412 BPCE 112189.50 9: 2014-12-25 47.697000000000003 201412 BPCE 112189.50 10: 2014-12-24 47.414999999999999 201412 BPCE 112189.50 11: 2015-02-05 47.073999999999998 201501 BPCE 103142.06 12: 2015-02-04 46.634999999999998 201501 BPCE 103142.06 13: 2015-02-03 46.405000000000001 201501 BPCE 103142.06 14: 2015-02-02 47.567 201501 BPCE 103142.06 15: 2015-01-30 47.396000000000001 201501 BPCE 103142.06 16: 2015-01-29 48.448999999999998 201501 BPCE 103142.06 17: 2015-01-28 49.442 201501 BPCE 103142.06 18: 2015-01-27 49.502000000000002 201501 BPCE 103142.06 19: 2015-01-26 49.73 201501 BPCE 103142.06 20: 2015-01-23 50.917000000000002 201501 BPCE 103142.06 21: 2015-01-06 48.633000000000003 201412 Credit Agricole 81618.76 22: 2015-01-05 46.670999999999999 201412 Credit Agricole 81618.76 ... 39: 2015-01-26 49.73 201501 Credit Agricole 73987.36 40: 2015-01-23 50.917000000000002 201501 Credit Agricole 73987.36 Date CDS Period BankName value
请注意，结果数据集包含(3 + 1 + 6) * 2 months * 2 banks = 40 rows。
来自 dropbox 的数据
如果保管箱链接断开:
dailycds <-
structure(list(Date = structure(c(1424649600, 1424390400, 1424304000,
1424217600, 1424131200, 1424044800, 1423785600, 1423699200, 1423612800,
1423526400, 1423440000, 1423180800, 1423094400, 1423008000, 1422921600,
1422835200, 1422576000, 1422489600, 1422403200, 1422316800, 1422230400,
1421971200, 1421884800, 1421798400, 1421712000, 1421625600, 1421366400,
1421280000, 1421193600, 1421107200, 1421020800, 1420761600, 1420675200,
1420588800, 1420502400, 1420416000, 1420156800, 1420070400, 1419984000,
1419897600, 1419811200, 1419552000, 1419465600, 1419379200, 1419292800,
1419206400, 1418947200, 1418860800, 1418774400, 1418688000, 1418601600,
1418342400, 1418256000, 1418169600, 1418083200, 1417996800, 1417737600,
1417651200, 1417564800, 1417478400, 1417392000, 1417132800, 1417046400,
1416960000, 1416873600, 1416787200, 1416528000, 1416441600, 1416355200,
1416268800, 1416182400, 1415923200, 1415836800, 1415750400, 1415664000,
1415577600, 1415318400, 1415232000, 1415145600, 1415059200, 1414972800
), class = c("POSIXct", "POSIXt"), tzone = "UTC"), CDS = c("44.259",
"44.555999999999997", "45.076999999999998", "44.951000000000001",
"45.762", "45.573", "45.634999999999998", "45.956000000000003",
"47.064", "47.51", "48.576999999999998", "47.265000000000001",
"47.073999999999998", "46.634999999999998", "46.405000000000001",
"47.567", "47.396000000000001", "48.448999999999998", "49.442",
"49.502000000000002", "49.73", "50.917000000000002", "51.37",
"52.536999999999999", "49.188000000000002", "47.893999999999998",
"46.728000000000002", "46.634999999999998", "46.366999999999997",
"47.012999999999998", "46.869", "48.121000000000002", "48.625999999999998",
"48.801000000000002", "48.633000000000003", "46.670999999999999",
"45.158000000000001", "47.32", "47.658000000000001", "45.843000000000004",
"47.588999999999999", "47.625999999999998", "47.697000000000003",
"47.414999999999999", "48.075000000000003", "48.085999999999999",
"47.496000000000002", "46.534999999999997", "48.149000000000001",
"49.421999999999997", "48.223999999999997", "47.100999999999999",
"47.484999999999999", "47.491999999999997", "47.052", "46.697000000000003",
"44.670999999999999", "47.706000000000003", "46.835000000000001",
"48.66", "46.841999999999999", "48.069000000000003", "49.49",
"50.155000000000001", "50.155000000000001", "50.49", "52.024000000000001",
"50.33", "50", "50.67", "53.15", "52.994999999999997", "55.31",
"50.82", "50.49", "50.832999999999998", "52.241", "51.97", "52.8",
"50.667000000000002", "51.134999999999998")), .Names = c("Date",
"CDS"), row.names = c(NA, -81L), class = c("tbl_df", "tbl", "data.frame"))
periodicassets <-
structure(list(BankName = c(" BPCE", " BPCE", " Credit Agricole",
" Credit Agricole"), Period = c("201412", "201501", "201412",
"201501"), value = c(112189.50293406, 103142.064337463, 81618.762099507,
73987.36251389)), .Names = c("BankName", "Period", "value"), row.names = c(10L,
11L, 18L, 19L), class = "data.frame")
关于r - 将每日和定期数据合并到一个数据框中，我们在Stack Overflow上找到一个类似的问题： https://stackoverflow.com/questions/45966897/