Question

我对R来说真的很陌生，我仍然试图绕过（）函数。我的数据dput（MergedData）链接here（我为篇幅道歉;我试图尽可能地缩短它）。我正在做一个小项目，我有以下功能：

new.trend <- function(MergedData)
{
  ret <- as.list(rep(NA, length(MergedData))) 
  ma.sig <- ma.crossover(MergedData)
  pricebreak <- price.channel(MergedData)
  sig <- intersect(which((ma.sig[1,])==1), which(!pricebreak[1,]==0))
  for (i in sig) { #Calculates output variables based on active signals
    x <- MergedData[[i]]
    x <- xts(x[,-1], order.by=x[,1])
    dev20 <- (x[,4]-SMA(x[,4], n=20))/x[,4]*100
    dev50 <- (x[,4]-SMA(x[,4], n=50))/x[,4]*100
    RSI <- RSI(x[,4], n=14)
    ret[[i]]<- na.omit(merge(tail(dev20, n=1L), tail(dev50, n=1L), tail(RSI, n=1L)))
  }
  na.omit(print(ret))
}
print(new.trend(MergedData))

问题/问题

return（ret）返回此：

> new.trend(MergedData)
[[1]]
             EUR.LAST EUR.LAST.1      EMA
2017-02-09 -0.6968559  0.3526983 44.68176

[[2]]
[1] NA

[[3]]
             GBP.LAST GBP.LAST.1      EMA
2017-02-09 -0.1920461   1.027927 52.27664

[[4]]
            CHF.OPEN CHF.OPEN.1      EMA
2017-02-09 0.5066387 -0.7241689 52.56533

[[5]]
[1] NA

[[6]]
[1] NA

[[7]]
[1] NA

[[8]]
[1] NA

[[9]]
[1] NA

[[10]]
[1] NA

[[11]]
[1] NA

[[12]]
[1] NA

[[13]]
           PLN.CLOSE PLN.CLOSE.1      EMA
2017-02-09 0.2824105   -1.569392 48.24069

[[14]]
[1] NA

[[15]]
           TRY.CLOSE TRY.CLOSE.1      EMA
2017-02-09 -2.315328  -0.2501765 42.52731

[[16]]
             ZAR.CLOSE ZAR.CLOSE.1      EMA
2017-02-09 -0.09598239   -1.492148 46.06286

[[17]]
[1] NA

[[18]]
            CLP.CLOSE CLP.CLOSE.1      EMA
2017-02-09 -0.2433194   -2.112368 40.93616

[[19]]
[1] NA

[[20]]
           MXN.CLOSE MXN.CLOSE.1      EMA
2017-02-09 -2.460443   -3.490762 34.67792

[[21]]
            PEN.CLOSE PEN.CLOSE.1      EMA
2017-02-09 -0.4138617   -1.974541 37.84737

[[22]]
             CNY.CLOSE CNY.CLOSE.1      EMA
2017-02-09 -0.08749199  -0.5004658 44.39283

[[23]]
            IDR.CLOSE IDR.CLOSE.1      EMA
2017-02-09 -0.4064827   -0.631571 35.91677

[[24]]
           INR.CLOSE INR.CLOSE.1      EMA
2017-02-09 -1.291429   -1.594705 21.83156

[[25]]
            KRW.CLOSE KRW.CLOSE.1      EMA
2017-02-09 -0.8529425   -2.840274 34.61214

[[26]]
           MYR.CLOSE MYR.CLOSE.1      EMA
2017-02-09 0.1407816  -0.4020273 49.80231

[[27]]
           SGD.CLOSE SGD.CLOSE.1      EMA
2017-02-09  0.123548  -0.7103133 49.73621

[[28]]
           PHP.CLOSE PHP.CLOSE.1      EMA
2017-02-09 0.1355443    0.236601 55.61772

[[29]]
           THB.CLOSE THB.CLOSE.1      EMA
2017-02-09 -0.518655   -1.396926 23.51997

但我想检索for（）函数中打印的内容，例如：

> for (i in sig) { #Calculates output variables based on active signals
+     x <- MergedData[[i]]
+     x <- xts(x[,-1], order.by=x[,1])
+     dev20 <- (x[,4]-SMA(x[,4], n=20))/x[,4]*100
+     dev50 <- (x[,4]-SMA(x[,4], n=50))/x[,4]*100
+     RSI <- RSI(x[,4], n=14)
+     print(ret[[i]]<- na.omit(merge(tail(dev20, n=1L), tail(dev50, n=1L), tail(RSI, n=1L))))
+ }- (x[,4]-SMA(x[,4], n=50))/x[,4]*100
        RSI <- RSI(x[,4], n=14)
        print(ret[[i]]<- na.omit(merge(tail(dev20, n=1L), tail(dev50, n=1L), tail(RSI, n=1L))))
      }

             EUR.LAST EUR.LAST.1      EMA
2017-02-09 -0.6968559  0.3526983 44.68176
             GBP.LAST GBP.LAST.1      EMA
2017-02-09 -0.1920461   1.027927 52.27664
            CHF.OPEN CHF.OPEN.1      EMA
2017-02-09 0.5066387 -0.7241689 52.56533
           PLN.CLOSE PLN.CLOSE.1      EMA
2017-02-09 0.2824105   -1.569392 48.24069
           TRY.CLOSE TRY.CLOSE.1      EMA
2017-02-09 -2.315328  -0.2501765 42.52731
             ZAR.CLOSE ZAR.CLOSE.1      EMA
2017-02-09 -0.09598239   -1.492148 46.06286
            CLP.CLOSE CLP.CLOSE.1      EMA
2017-02-09 -0.2433194   -2.112368 40.93616
           MXN.CLOSE MXN.CLOSE.1      EMA
2017-02-09 -2.460443   -3.490762 34.67792
            PEN.CLOSE PEN.CLOSE.1      EMA
2017-02-09 -0.4138617   -1.974541 37.84737
             CNY.CLOSE CNY.CLOSE.1      EMA
2017-02-09 -0.08749199  -0.5004658 44.39283
            IDR.CLOSE IDR.CLOSE.1      EMA
2017-02-09 -0.4064827   -0.631571 35.91677
           INR.CLOSE INR.CLOSE.1      EMA
2017-02-09 -1.291429   -1.594705 21.83156
            KRW.CLOSE KRW.CLOSE.1      EMA
2017-02-09 -0.8529425   -2.840274 34.61214
           MYR.CLOSE MYR.CLOSE.1      EMA
2017-02-09 0.1407816  -0.4020273 49.80231
           SGD.CLOSE SGD.CLOSE.1      EMA
2017-02-09  0.123548  -0.7103133 49.73621
           PHP.CLOSE PHP.CLOSE.1      EMA
2017-02-09 0.1355443    0.236601 55.61772
           THB.CLOSE THB.CLOSE.1      EMA
2017-02-09 -0.518655   -1.396926 23.51997

提示，建议和指示将非常感谢！我知道for（）函数可能并不理想，但它是我知道如何仅循环通过与sig中索引相对应的MergedData值的唯一方法。

Answer 1

好的，现在我明白了。如果你想保持循环，你可以改变：

 na.omit(print(ret))

到

lapply(ret, function(x) x[!is.na(x)])

Answer 2

尝试使用apply函数来评估数据帧或动物园时间序列。在R中，最好避免使用lapply之类的结构。使用产生列表的apply或给出矩阵的library(dplyr) library(zoo) #create zoo object z <- zoo(data.frame(column1 = c(1, 2, 3), column2 = c(5, 6, 7)), order.by = seq(from = as.Date('2017-01-01'), by = 'day', length.out = 3)) z #create function to calculate each row f <- function(row){ row ^ 2 } #apply function f to each row (MARGIN = 1) of zoo (z) object, transpose matrix and create zoo time series res<-apply(z,MARGIN = 1,function (row) f(row)) %>% t %>% as.zoo(.,order.by=rownames(.)) res class(res)。

考虑这个例子：

> library(dplyr)
> library(zoo)
> #create zoo object
> z <- zoo(data.frame(column1 = c(1, 2, 3), column2 = c(5, 6, 7)), order.by = seq(from = as.Date('2017-01-01'), by = 'day', length.out = 3))
> z
        column1 column2
2017-01-01       1       5
2017-01-02       2       6
2017-01-03       3       7
> #create function to calculate each row
> f <- function(row){
+     row ^ 2
+ }
> #apply function f to each row (MARGIN = 1) of zoo (z) object, transpose matrix and create zoo time series
> res<-apply(z,MARGIN = 1,function (row) f(row)) %>% t %>% as.zoo(.,order.by=rownames(.))
> res
        column1 column2
2017-01-01       1      25
2017-01-02       4      36
2017-01-03       9      49
> class(res)
[1] "zoo"
>

这将产生

printenv

将for（）函数中的打印值保存为变量？

2 个答案: