R 组合两个 For 循环（嵌套 For 循环）
R Combinig two For Loops (Nested For Loop)
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$Number.Cases.Authorised[a:ii]))
}
}
c <- data.frame(c)
c <- c %>%
rename(
n.Seen.Cum = c
)
#View(c)
BR <- cbind(BR,c)
循环2中BR需要Unique.Order.Comment[i]
我believe/hope应该是下面的样子。
但我收到错误消息 Error in rep(0, nrow(Unique.Order.Comment[i])) : invalid 'times' argument
（我觉得应该是这样的）
Unique.Order.Comment <- unique(df2$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")
dput(头(Rebuilt.Data))
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")
如果我将 print(Unique.Order.Comment[i]) 放在第二个单独的循环之前，我会得到：
“CN”
理论上，第一个循环根据 Order.Comment 的唯一列表（它可以做到）对数据进行子集化。
然后它做一个累积总和，这将 cbind 到子集数据上。
我想我明白了你的目标，但我可能错过了一些东西。让我知道，我可以编辑。
据我所知，您只需要一个循环，而不是 assign()一堆数据帧，您可以迭代地构建一个摘要 table。
编辑
这里的另一个答案很优雅！我正在根据您的新评论更新我的答案，只是为了好玩。不确定为什么我们有不同的 n.Seen.Cum 值...
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
如果您有多个年份并希望重置累计总和，请更新 ifelse() 中的 test 参数以包含一些最大天数。
首先，如果您提供一个小示例以及您的预期输出，会更容易提供帮助。您可以共享您的原始数据，删除问题不需要的列，或者创建一个与您的原始数据相似的假数据集。
其次，我认为你把它复杂化了。在全球环境中创建多个数据集从来都不是一个好主意。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，下面我使用了具有 rollsumr 功能的 zoo 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
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$Number.Cases.Authorised[a:ii]))
}
}
c <- data.frame(c)
c <- c %>%
rename(
n.Seen.Cum = c
)
#View(c)
BR <- cbind(BR,c)
循环2中BR需要Unique.Order.Comment[i]
我believe/hope应该是下面的样子。 但我收到错误消息 Error in rep(0, nrow(Unique.Order.Comment[i])) : invalid 'times' argument
（我觉得应该是这样的）
Unique.Order.Comment <- unique(df2$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")
dput(头(Rebuilt.Data))
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")
如果我将 print(Unique.Order.Comment[i]) 放在第二个单独的循环之前，我会得到：
“CN”
理论上，第一个循环根据 Order.Comment 的唯一列表（它可以做到）对数据进行子集化。 然后它做一个累积总和，这将 cbind 到子集数据上。
我想我明白了你的目标，但我可能错过了一些东西。让我知道，我可以编辑。
据我所知，您只需要一个循环，而不是 assign()一堆数据帧，您可以迭代地构建一个摘要 table。
编辑
这里的另一个答案很优雅！我正在根据您的新评论更新我的答案，只是为了好玩。不确定为什么我们有不同的 n.Seen.Cum 值...
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
如果您有多个年份并希望重置累计总和，请更新 ifelse() 中的 test 参数以包含一些最大天数。
首先，如果您提供一个小示例以及您的预期输出，会更容易提供帮助。您可以共享您的原始数据，删除问题不需要的列，或者创建一个与您的原始数据相似的假数据集。
其次，我认为你把它复杂化了。在全球环境中创建多个数据集从来都不是一个好主意。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，下面我使用了具有 rollsumr 功能的 zoo 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