R Combinig two For Loops (Nested For Loop) - r

Question

I've been trying to combine the two For Loops into a single loop.
Loop 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)
}
Loop 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$Number.Cases.Authorised[a:ii]))
}
}
c <- data.frame(c)
c <- c %>%
rename(
n.Seen.Cum = c
)
#View(c)
BR <- cbind(BR,c)
The BR need to be Unique.Order.Comment[i] in Loop 2.
What I believe/hope it would look like should be the below.
But I get the error message Error in rep(0, nrow(Unique.Order.Comment[i])) : invalid 'times' argument
(What I think it should look like)
Unique.Order.Comment <- unique(df2$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)
}
Edit example data:
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(head(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")
if I place print(Unique.Order.Comment[i]) before the second seperate loop I get:
"CN"
In theory the first loop subsets data based upon a unique list of Order.Comment (which it can do).
Then it does a cumlative sum and this gets cbind onto the subsetted data.

Answer 1

First, it is easier to help if you provide a small example along with the your expected output. You can share your original data removing the columns which are not necessary to the question or create a fake dataset which is similar to your original data.
Second, I think you are overcomplicating this. It is never a good idea to create multiple datasets in your global environment. They are very difficult to manage and unnecessary pollute the global environment. You can use lists instead.
In this case I don't think we need to split the datasets in different lists as we have different packages that can perform rolling calculations. For example, below I have used zoo package which has rollsumr function.
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

Answer 2

I think I see what you are aiming for, but I may have missed something. Let me know, and I can edit.
From what I can tell, you only need one loop, and instead of assign()ing a bunch of dataframes, you can iteratively build a summary table.
edit
The other answer here is quite elegant! I'm updating my answer based on your new comments just for fun. Not sure why we have different n.Seen.Cum values...
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
If you have multiple years and want the cumulative sum to reset, update the test parameter in ifelse() to include some max number of days.