我认为这是一个split-apply-combine问题,但随着时间序列的扭曲 . 我的数据包含不规则的计数,我需要对每组计数执行一些汇总统计 . 这是数据的快照:
这是适合您的控制台:
library(xts)
date <- as.Date(c("2010-11-18", "2010-11-19", "2010-11-26", "2010-12-03", "2010-12-10",
"2010-12-17", "2010-12-24", "2010-12-31", "2011-01-07", "2011-01-14",
"2011-01-21", "2011-01-28", "2011-02-04", "2011-02-11", "2011-02-18",
"2011-02-25", "2011-03-04", "2011-03-11", "2011-03-18", "2011-03-25",
"2011-03-26", "2011-03-27"))
returns <- c(0.002,0.000,-0.009,0.030, 0.013,0.003,0.010,0.001,0.011,0.017,
-0.008,-0.005,0.027,0.014,0.010,-0.017,0.001,-0.013,0.027,-0.019,
0.000,0.001)
count <- c(NA,NA,1,1,2,2,3,4,5,6,7,7,7,7,7,NA,NA,NA,1,2,NA,NA)
maxCount <- c(NA,NA,0.030,0.030,0.030,0.030,0.030,0.030,0.030,0.030,0.030,
0.030,0.030,0.030,0.030,NA,NA,NA,0.027,0.027,NA,NA)
sumCount <- c(NA,NA,0.000,0.030,0.042,0.045,0.056,0.056,0.067,0.084,0.077,
0.071,0.098,0.112,0.123,NA,NA,NA,0.000,-0.019,NA,NA)
xtsData <- xts(cbind(returns,count,maxCount,sumCount),date)
我不知道如何构造max和cumSum列,特别是因为每个计数序列的长度不规则 . 由于我不会总是知道计数系列的起点和终点,所以我在试图找出这些组的索引时迷失了方向 . 谢谢你的帮助!
更新:这是我的for循环试图计算cumSum . 它不是累积总和,只是必要的回报,我仍然不确定如何将函数应用于这些范围!
xtsData <- cbind(xtsData,mySumCount=NA)
# find groups of returns
for(i in 1:nrow(xtsData)){
if(is.na(xtsData[i,"count"]) == FALSE){
xtsData[i,"mySumCount"] <- xtsData[i,"returns"]
}
else{
xtsData[i,"mySumCount"] <- NA
}
}
更新2:谢谢评论者!
# report returns when not NA count
x1 <- xtsData[!is.na(xtsData$count),"returns"]
# cum sum is close, but still need to exclude the first element
# -0.009 in the first series of counts and .027 in the second series of counts
x2 <- cumsum(xtsData[!is.na(xtsData$count),"returns"])
# this is output is not accurate because .03 is being displayed down the entire column, not just during periods when counts != NA. is this just a rounding error?
x3 <- max(xtsData[!is.na(xtsData$count),"returns"])
解:
# function to pad a vector with a 0
lagpad <- function(x, k) {
c(rep(0, k), x)[1 : length(x)]
}
# group the counts
x1 <- na.omit(transform(xtsData, g = cumsum(c(0, diff(!is.na(count)) == 1))))
# cumulative sum of the count series
z1 <- transform(x1, cumsumRet = ave(returns, g, FUN =function(x) cumsum(replace(x, 1, 0))))
# max of the count series
z2 <- transform(x1, maxRet = ave(returns, g, FUN =function(x) max(lagpad(x,1))))
merge(xtsData,z1$cumsumRet,z2$maxRet)
2 回答
显示的代码与图像中的输出不一致,并且没有提供解释,因此不清楚需要什么操作;然而,问题确实提到主要问题是区分群体,所以我们将解决这个问题 .
为此,我们计算一个新列
g
,其行包含1表示第一组,2表示第二组,依此类推 . 我们还删除了NA行,因为g
列足以区分组 .以下代码通过首先将每个NA位置设置为FALSE并将每个非NA位置设置为TRUE来计算与
count
具有相同长度的矢量 . 然后它将该矢量的每个位置与先前位置区分开 . 为此,它隐式地将FALSE转换为0并将TRUE转换为1,然后执行差分 . 接下来,我们将最后的结果转换为逻辑向量,对于每个1组件,该逻辑向量为TRUE,否则为FALSE . 由于差分向量的第一个分量没有先前位置,因此我们为此前置0 . 前置操作隐式地将刚生成的TRUE和FALSE值分别转换为1和0 . 将cumsum
填入第一组中,将第二组填充为2,依此类推 . 最后省略NA行:赠送:
您现在可以使用
ave
执行您喜欢的任何计算 . 例如,按组收取累计回报金额:将
cumsum
替换为适用于ave
的任何其他功能 .啊,所以“count”是这些组,你想要每组的cumsum和每组的最大数量 . 我认为在data.table中,所以我会这样做 .