在下面的示例xts数据中，我显示了一小部分日内数据。
my.temp <- structure(c(1134.65, 45665.2, 63639.8, 262817.8, 71898.4, 128737,
641741.8, 6090, 7561.5, 3954.73, 15733.2, 274.88, 824.64, 1099.52, 1924.16,
348715, 196425, 113975, 215825, 38340, 21138, 12020, 20200, 20200, 20200,
20200, 20200, 20200, 23000, 12020, 12020, 12020, 12020, 12020, 12020, 12020,
20200, 20200, 20200, 20200, 12020, 20000), .Dim = c(21L, 2L),
.Dimnames = list(NULL, c("VALUE", "USAP")), index = structure(c(1378130401,
1378130404, 1378130404, 1378130404, 1378130404, 1378130404, 1378130404,
1378130404, 1378130406, 1378130409, 1378130411, 1378130415, 1378130415,
1378130415, 1378130415, 1378130451, 1378130452, 1378130452, 1378130452,
1378130455, 1378130501), tzone = "", tclass = c("POSIXct", "POSIXt")),
.indexCLASS = c("POSIXct", "POSIXt"), tclass = c("POSIXct", "POSIXt"),
.indexTZ = "", tzone = "", class = c("xts", "zoo"))
我试图显示每个USAP的总价值。如果我想在整个期间显示结果，我已成功使用aggregate功能。如何显示每天USAP的总数？
答案 0 :(得分：0)
这是一种方法。可能有更简洁的方法，但这是我现在能想到的最好方法。
# first, split your object into groups by USAP
temp.split <- split(my.temp, my.temp$USAP)
# define a simple helper function
f <- function(x) apply.daily(x, function(d) c(sum(d$VALUE),d$USAP[1]))
# then call apply.daily on each group;
# finally, rbind the results back together
do.call(rbind, lapply(temp.split, f))
# VALUE USAP
# 2013-09-02 09:00:04 6090.00 23000
# 2013-09-02 09:00:52 2089440.00 20200
# 2013-09-02 09:00:55 70847.28 12020
# 2013-09-02 09:01:41 21138.00 20000
答案 1 :(得分：0)
aggregate(VALUE ~ USAP + as.Date(index(my.temp)), data = my.temp, sum)
# USAP as.Date(index(my.temp)) VALUE
# 1 12020 2013-09-02 70847.28
# 2 20000 2013-09-02 21138.00
# 3 20200 2013-09-02 2089440.00
# 4 23000 2013-09-02 6090.00