r - R 组合两个 For 循环（嵌套 For 循环）

Question

我一直在尝试将两个 For 循环组合成一个循环。

循环 1：

    Unique.Order.Comment <- unique(df2$Rebuilt.Order.Comment)
length(Unique.Order.Comment)

#loop for the calculations
for (i in 1:length(Unique.Order.Comment)) {
  #a <- i-11
  #c[i] <-  print(sum(n.Cases.per.month$nCases[a:i]))
  a <- subset.data.frame(Rebuilt.Data, Rebuilt.Order.Comment == Unique.Order.Comment[i])  
  assign(Unique.Order.Comment[i],a)


}

循环 2：

    #loop for the calculations
c <- rep(0, nrow(BR))
for (ii in 1:nrow(BR)) {
  if (ii < 12){
    print(0)
  }else { 
    a <- ii-11
    c[ii] <-  print(sum(BR$Number.Cases.Authorised[a:ii]))
  }
  
} 


c <- data.frame(c)
c <- c %>% 
  rename(
    n.Seen.Cum = c
  )

#View(c)

BR <- cbind(BR,c)

在循环 2 中，BR 需要是Unique.Order.Comment[i] 。

我相信/希望它看起来应该是下面的。但我收到错误消息Error in rep(0, nrow(Unique.Order.Comment[i])) : invalid 'times' argument

（我认为它应该是什么样子）

    Unique.Order.Comment <- unique(df2$Rebuilt.Order.Comment)
length(Unique.Order.Comment)

#loop for the calculations
for (i in 1:length(Unique.Order.Comment)) {
  #a <- i-11
  #c[i] <-  print(sum(n.Cases.per.month$nCases[a:i]))
  a <- subset.data.frame(Rebuilt.Data, Rebuilt.Order.Comment == Unique.Order.Comment[i])  
  assign(Unique.Order.Comment[i],a)

  
  
  #loop for the calculations
  c <- rep(0, nrow(Unique.Order.Comment[i]))
  for (ii in 1:nrow(Unique.Order.Comment[i])) {
    if (ii < 12){
      print(0)
    }else { 
      a <- ii-11
      c[ii] <-  print(sum(Unique.Order.Comment[i]$Number.Cases.Authorised[a:ii]))
    }
    
  } 
  
  
  c <- data.frame(c)
  c <- c %>% 
    rename(
      n.Seen.Cum = c
    )
  
  #View(c)
  
  Unique.Order.Comment[i] <- cbind(Unique.Order.Comment[i],c)
}

编辑示例数据：dput(Unique.Order.Comment)

c("CN", "DM", "DR", "FF", "PG", "HN", "SK", "GI", "GYN", "BR", 
"UR", "LYMPH", "HPB", "BST", "ENDOC", "PAEDGI", "CT", "PERI", 
"NEURO", "MOHS", "ICC", "RE", "PAED", "MN", "EMR", "PR", "LBX", 
"HAEM", "CTT", "UGI", "NEUR", "URGI", "GYNAE")

输入（头（重建。数据））

structure(list(Rebuilt.Order.Comment = c("BR", "BR", "BR", "BR", 
"BR", "BR"), Period.Received = c("2019-01", "2019-02", "2019-03", 
"2019-04", "2019-05", "2019-06"), Number.Cases.Received = c(838L, 
730L, 778L, 832L, 574L, 626L), Number.Cases.Authorised = c(680L, 
587L, 896L, 715L, 761L, 554L), Number.Cases.Authorised.Less7Days = c(550L, 
343L, 520L, 389L, 393L, 374L), Number.Cases.Authorised.Less10.Days = c(628L, 
475L, 723L, 595L, 555L, 474L), Percentage.Authorsied.Less7Days = c(0.808823529411765, 
0.584327086882453, 0.580357142857143, 0.544055944055944, 0.516425755584757, 
0.675090252707581), Percentage.Authorsied.Less10Days = c(0.923529411764706, 
0.809199318568995, 0.806919642857143, 0.832167832167832, 0.729303547963206, 
0.855595667870036), Avg.TaT.for.Authorised.Cases = structure(c(5.26470588235294, 
8.74616695059625, 8.34709821428571, 8.09370629370629, 12.826544021025, 
6.22021660649819), class = "difftime", units = "days"), MDM.Received = c(2L, 
13L, 2L, NA, NA, 5L), MDM.Received.Avg.TAT = structure(c(5, 29.2307692307692, 
0.5, NA, NA, 5.4), class = "difftime", units = "days"), So.Received = c(NA, 
1L, NA, 1L, NA, 2L), So.Received.Avg.TAT = structure(c(NA, 14, 
NA, 9, NA, 54), class = "difftime", units = "days")), row.names = c(NA, 
6L), class = "data.frame")

如果我放在print(Unique.Order.Comment[i])第二个单独循环之前，我会得到：“CN”

理论上，第一个循环根据 Order.Comment 的唯一列表（它可以这样做）对数据进行子集化。然后它进行累积和，这将 cbind 绑定到子集数据上。

score 1 · Accepted Answer

首先，如果您提供一个小示例以及您的预期输出，则更容易提供帮助。您可以共享原始数据，删除问题不需要的列，或创建与原始数据相似的假数据集。

其次，我认为你过于复杂了。在您的全球环境中创建多个数据集绝不是一个好主意。它们非常难以管理并且不必要地污染全球环境。您可以改用列表。

在这种情况下，我认为我们不需要将数据集拆分到不同的列表中，因为我们有不同的包可以执行滚动计算。例如，下面我使用zoo了具有rollsumr功能的包。

library(dplyr)
library(zoo)

df <- df %>%
  group_by(Rebuilt.Order.Comment) %>%
  mutate(n.Seen.Cum = rollsumr(Number.Cases.Authorised, 12, fill = 0)) %>%
  ungroup

df

#   Rebuilt.Order.Comment Period.Received Number.Cases.Authorised n.Seen.Cum
#   <chr>                 <chr>                             <int>      <int>
# 1 BR                    2019-01                             680          0
# 2 BR                    2019-02                             587          0
# 3 BR                    2019-03                             896          0
# 4 BR                    2019-04                             715          0
# 5 BR                    2019-05                             761          0
# 6 BR                    2019-06                             554          0
# 7 BR                    2019-07                             843          0
# 8 BR                    2019-08                             815          0
# 9 BR                    2019-09                             704          0
#10 BR                    2019-10                             939          0
#11 BR                    2019-11                             834          0
#12 BR                    2019-12                             880       9208
#13 BR                    2020-01                             801       9329
#14 BR                    2020-02                             610       9352
#15 BR                    2020-03                             853       9309

score 0 · Accepted Answer

我想我看到了你的目标，但我可能错过了一些东西。告诉我，我可以编辑。

据我所知，您只需要一个循环，而不是assign()一堆数据框，您可以迭代地构建一个汇总表。

编辑

这里的另一个答案非常优雅！我正在根据您的新评论更新我的答案，只是为了好玩。不知道为什么我们有不同的n.Seen.Cum价值观......

df2 <- structure(list(
  Rebuilt.Order.Comment = c("BR", "BR", "BR", "BR", "BR", "BR", "BR", "BR", "BR", "BR", "BR", "BR", "BR", "BR", "BR" ),
  Period.Received = c("2019-01", "2019-02", "2019-03", "2019-04", "2019-05", "2019-06", "2019-07", "2019-08", "2019-09", "2019-10", "2019-11", "2019-12", "2020-01", "2020-02", "2020-03"),
  Number.Cases.Authorised = c(680L, 587L, 896L, 715L, 761L, 554L, 843L, 815L, 704L, 939L, 834L, 880L, 801L, 610L, 853L),
  n.Seen.Cum = c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 9208, 9329, 9352, 9309)),
  row.names = c(NA, 15L), class = "data.frame")

# This will hold results
output <- list()

# Loop over this vector
Unique.Order.Comment <- unique(df2$Rebuilt.Order.Comment)

for(comment in Unique.Order.Comment){
  # Temporary dataframe that is subset of 'df2'
  temp <- df2[df2$Rebuilt.Order.Comment == comment,] 
  
  # We can do arithmetic with dates that have days
  temp$Period.Received2 <- as.Date(paste(temp$Period.Received, "-01", sep=""))
  
  # Calculate cumsum after 333 days have passed
  temp$n.Seen.cum2 <- ifelse(
    test = temp$Period.Received2 - min(temp$Period.Received2) > 333,
    yes = cumsum(temp$Number.Cases.Authorised),
    no = NA)
  
  # better
  output[[comment]] <- temp
  
  # quick and dirty
  # assign(x = comment, value = temp)
}

output[[1]]
#>    Rebuilt.Order.Comment Period.Received Number.Cases.Authorised n.Seen.Cum
#> 1                     BR         2019-01                     680          0
#> 2                     BR         2019-02                     587          0
#> 3                     BR         2019-03                     896          0
#> 4                     BR         2019-04                     715          0
#> 5                     BR         2019-05                     761          0
#> 6                     BR         2019-06                     554          0
#> 7                     BR         2019-07                     843          0
#> 8                     BR         2019-08                     815          0
#> 9                     BR         2019-09                     704          0
#> 10                    BR         2019-10                     939          0
#> 11                    BR         2019-11                     834          0
#> 12                    BR         2019-12                     880       9208
#> 13                    BR         2020-01                     801       9329
#> 14                    BR         2020-02                     610       9352
#> 15                    BR         2020-03                     853       9309
#>    Period.Received2 n.Seen.cum2
#> 1        2019-01-01          NA
#> 2        2019-02-01          NA
#> 3        2019-03-01          NA
#> 4        2019-04-01          NA
#> 5        2019-05-01          NA
#> 6        2019-06-01          NA
#> 7        2019-07-01          NA
#> 8        2019-08-01          NA
#> 9        2019-09-01          NA
#> 10       2019-10-01          NA
#> 11       2019-11-01          NA
#> 12       2019-12-01        9208
#> 13       2020-01-01       10009
#> 14       2020-02-01       10619
#> 15       2020-03-01       11472

如果您有多年时间并希望重置累积总和，请更新test参数ifelse()以包含一些最大天数。

r - R 组合两个 For 循环（嵌套 For 循环）

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