将列表转换为动物园，并在列表上使用rollapply

Question

我希望将list对象转换为zoo，然后在zoo对象上应用rollapply。下面是一个简短的例子(我使用UNIX：)来处理90,000个这样的文件。假设我的列表有两个数据格式。

1)我想将每个数据格式中的日期转换为以下格式：

dates <- as.Date(paste0(mylist$year, "-", mylist$month, "-", mylist$day), format="%Y-%m-%d")

z <- zoo(mylist, order.by=mylist[,1])

我知道lapply可以做到这一点，但我尝试了，但没有成功。

一旦我得到了我的zoo对象，我想使用rollapply

library(hydroTSM)#for daily2annual function but aggregate can do 

    x.3max <- rollapply(data=zooobject, width=3, FUN=sum, fill=NA, partial= TRUE,
                         align="center")
    # Maximum value per year of 3-day total rainfall for each one of the simulations
    z.3max.annual <- daily2annual(z.3max,  FUN=max,na.rm=TRUE)#dates=1

上面的代码是在zooobject中的数据文件的每一列上设置一个为期3天的窗口，并对值进行求和。提取出3天总数的最大年值.

      mylist<- list(a,a)
mylist<-lapply(mylist, function(x) x[x[["Month"]] %in% c(12,1,2),])# extract data for DJF for individual sites


    library(zoo)
       a= structure(list(Year = c(1975L, 1975L, 1975L, 1975L, 1975L, 1975L
), Month = c(1L, 1L, 1L, 1L, 1L, 1L), Site = structure(c(1L, 
1L, 1L, 1L, 1L, 1L), .Label = "G100", class = "factor"), Day = 1:6, 
    sim01 = c(28.49, 29.04, 27.62, 28.43, 28.69, 29.16), sim02 = c(29.49, 
    30.04, 28.62, 29.43, 29.69, 30.16), sim03 = c(30.49, 31.04, 
    29.62, 30.43, 30.69, 31.16), sim04 = c(31.49, 32.04, 30.62, 
    31.43, 31.69, 32.16), sim05 = c(32.49, 33.04, 31.62, 32.43, 
    32.69, 33.16), sim06 = c(33.49, 34.04, 32.62, 33.43, 33.69, 
    34.16), sim07 = c(34.49, 35.04, 33.62, 34.43, 34.69, 35.16
    ), sim08 = c(35.49, 36.04, 34.62, 35.43, 35.69, 36.16), sim09 = c(36.49, 
    37.04, 35.62, 36.43, 36.69, 37.16), sim10 = c(37.49, 38.04, 
    36.62, 37.43, 37.69, 38.16), sim11 = c(38.49, 39.04, 37.62, 
    38.43, 38.69, 39.16), sim12 = c(39.49, 40.04, 38.62, 39.43, 
    39.69, 40.16), sim13 = c(40.49, 41.04, 39.62, 40.43, 40.69, 
    41.16), sim14 = c(41.49, 42.04, 40.62, 41.43, 41.69, 42.16
    ), sim15 = c(42.49, 43.04, 41.62, 42.43, 42.69, 43.16), sim16 = c(43.49, 
    44.04, 42.62, 43.43, 43.69, 44.16), sim17 = c(44.49, 45.04, 
    43.62, 44.43, 44.69, 45.16), sim18 = c(45.49, 46.04, 44.62, 
    45.43, 45.69, 46.16), sim19 = c(46.49, 47.04, 45.62, 46.43, 
    46.69, 47.16), sim20 = c(47.49, 48.04, 46.62, 47.43, 47.69, 
    48.16)), .Names = c("Year", "Month", "Site", "Day", "sim01", 
"sim02", "sim03", "sim04", "sim05", "sim06", "sim07", "sim08", 
"sim09", "sim10", "sim11", "sim12", "sim13", "sim14", "sim15", 
"sim16", "sim17", "sim18", "sim19", "sim20"), row.names = c(NA, 
6L), class = "data.frame")

产出应类似于：

Year Site Sim01... 
1975 G100 ...
1976 G100 ...
1977 G100 ...

只需要月份c(12,1,2)中的值。

Answer 1

这将生成一个动物园对象列表，Lz，然后对列表中的每个组件执行rollapply，给出L2。最后，L3每年聚合一次，取每一列的max。

library(zoo)

mylist <- list(a, a) # a is given at bottom of question

Lz <- lapply(mylist, read.zoo, index = 1:3, format = "%Y %m %d")
L2 <- lapply(Lz, rollapply, 3, sum, partial = TRUE)
L3 <- lapply(L2, function(z) aggregate(z, as.numeric(format(time(z), "%Y")), max))

给予：

> L3

[[1]]
     sim01 sim02 sim03 sim04 sim05  sim06  sim07  sim08  sim09  sim10  sim11
1975 86.28 89.28 92.28 95.28 98.28 101.28 104.28 107.28 110.28 113.28 116.28
      sim12  sim13  sim14  sim15  sim16  sim17  sim18  sim19  sim20
1975 119.28 122.28 125.28 128.28 131.28 134.28 137.28 140.28 143.28

[[2]]
     sim01 sim02 sim03 sim04 sim05  sim06  sim07  sim08  sim09  sim10  sim11
1975 86.28 89.28 92.28 95.28 98.28 101.28 104.28 107.28 110.28 113.28 116.28
      sim12  sim13  sim14  sim15  sim16  sim17  sim18  sim19  sim20
1975 119.28 122.28 125.28 128.28 131.28 134.28 137.28 140.28 143.28

Answer 2

解出

lst1 <- lapply(list.files(pattern=".csv"),function(x) read.table(x,header=TRUE,sep="")) # read all files and data and replace -999.9 with NA

lst2<-lapply(lst1, function(x) x[x[["Month"]] %in% c(6,7,8),])#c(6,7,8) extract data for DJF for individual sites
names(lst2)<-list.files(pattern=".csv")
lapply(lst2,tail,4)
lst3<-lapply(lst2, function(x) x[!(names(x) %in% c("Site"))])
Lz <- lapply(lst3, read.zoo, index = 1:3, format = "%Y %m %d")

L2 <- lapply(Lz, rollapply, 3, sum, partial = TRUE)
L3 <- lapply(L2, function(z) aggregate(z, as.numeric(format(time(z), "%Y")), max))

mapply(
  write.table,
  x=L3, file=paste(names(L3), "csv", sep="."),
  MoreArgs=list(row.names=FALSE, sep=",")
) # write files to folder keeping the list names as file names