按产品按月汇总R中XTS对象的值

我有一个包含900列（x1-x900）日值的xts对象，我需要将其计算成月度回报

x <- data.frame(date=seq(as.Date("2016/7/4"), as.Date("2016/10/1"), "day"),x1=runif(90,.95,1.07),x2=runif(90,.95,1.07),
      x3=runif(90,.95,1.07),x4=runif(90,.95,1.07),x5=runif(90,.95,1.07),x6=runif(90,.95,1.07),x7=runif(90,.95,1.07))

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下面是一个带有

dplyr

和

lubridate

包的解决方案：

set.seed(1) ; x <- data.frame(date=seq(as.Date("2016/7/4"), as.Date("2016/10/1"), "day"),x1=runif(90,.95,1.07),x2=runif(90,.95,1.07),
                x3=runif(90,.95,1.07),x4=runif(90,.95,1.07),x5=runif(90,.95,1.07),x6=runif(90,.95,1.07),x7=runif(90,.95,1.07))
library(dplyr) ; library(lubridate)
x %>% 
  group_by(yearmon = paste(year(date), month(date), sep = "-")) %>% 
  summarise_each(funs(prod), - c(date, yearmon)) 

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xts.x和缺少的部分：这里是我的解决方案和FXQuantRader解决方案之间的快速比较。毫无疑问，他的速度要快得多（大约4倍）<代码>库（微基准）；微基准（Apom={x%%>%group_by（yearmon=paste（year（date），month（date），sep=“-”））%%>%mutate（date=max（date））%%>%group_by（date）%%>%summary_each（funs（prod），-c（yearmon，date，date）），FXQuantTrader={xts.x
set.seed(1) ; x <- data.frame(date=seq(as.Date("2016/7/4"), as.Date("2016/10/1"), "day"),x1=runif(90,.95,1.07),x2=runif(90,.95,1.07),
                x3=runif(90,.95,1.07),x4=runif(90,.95,1.07),x5=runif(90,.95,1.07),x6=runif(90,.95,1.07),x7=runif(90,.95,1.07))
library(dplyr) ; library(lubridate)
x %>% 
  group_by(yearmon = paste(year(date), month(date), sep = "-")) %>% 
  summarise_each(funs(prod), - c(date, yearmon)) 

x %>% 
  group_by(yearmon = paste(year(date), month(date), sep = "-")) %>% 
  mutate(Date = max(date)) %>% 
  group_by(Date) %>% 
  summarise_each(funs(prod), - c(yearmon, date, Date)) 

xts.x <- xts(x[, !colnames(x) %in% "date"], order.by = x[, "date"])
xts.x.mthly <- apply.monthly(xts.x, FUN = function(x) unlist(lapply(x, prod)))

> xts.x.mthly
                  x1       x2        x3        x4        x5       x6        x7
2016-07-31 0.9924681 1.306556 1.0919181 0.8019117 1.3563864 1.853631 0.8563263
2016-08-31 1.4780971 1.946373 1.4265027 1.8508386 1.4926483 1.651613 1.4224733
2016-09-30 1.5926547 1.478231 1.0414107 1.4204825 1.2540149 1.374734 1.0768668
2016-10-01 1.0643725 1.005987 0.9813467 1.0545426 0.9964061 1.005145 1.0146190

# If you want data.frame output with explicit date column:
df.mthly <- data.frame("date" = index(xts.x.mthly), coredata(xts.x.mthly))