R-供应周数
我试图计算一个有1000万行的数据集的销售预测，手头的库存将持续多少周。我在下面给出的数据结构的最后一列中列出了预期的输出。我还附上了这个在Excel中的实现R-供应周数,r,generate-series,R,Generate Series,我试图计算一个有1000万行的数据集的销售预测，手头的库存将持续多少周。我在下面给出的数据结构的最后一列中列出了预期的输出。我还附上了这个在Excel中的实现 Logic Weeksofsupply = Number of weeks the current inventory on hand will last. example - in the attached image (SKU_CD 222, STORE_CD 33), the inventory on hand is 19, the
Logic
Weeksofsupply = Number of weeks the current inventory on hand will last.
example - in the attached image (SKU_CD 222, STORE_CD 33), the inventory on hand is 19, the sales values are
WK1 + WK2 = 15, Wk1 + Wk2 + Wk3 = 24, Which is greater than 19,
So we are picking 2, which the count of Weeks the current inventory will last.
=IF（A2这里有一种方法，使用线性插值法approxfun
data$WeeksSupply <- sapply(1:nrow(data),function(i)
approxfun(cumsum(as.vector(c(data[i,2:11]))),1:10)(data$Inventory[i]))
data$WeeksSupply <- replace(data$WeeksSupply,is.na(data$WeeksSupply),
"Inventory Exceeds Forecast")
data
# A tibble: 2 x 12
Inventory Wk1 Wk2 Wk3 Wk4 Wk5 Wk6 Wk7 Wk8 Wk9 Wk10 WeeksSupply
<dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <chr>
1 200 20 15 25 40 35 45 30 50 45 55 6.66666666666667
2 2000 20 15 25 40 35 45 30 50 45 55 Inventory Exceeds Forecast
data$WeeksSupply对于长格式的修订数据，您可以执行以下操作
library(dplyr) #for the grouping functionality
#define a function to calculate weeks Supply from Sales and Inventory
weekSup <- function(sales,inv){
sales <- unlist(sales)
inv <- unlist(inv)
n <- length(sales)
weeksup <- rep(NA,n)
for(i in 1:n){
if(i==n | inv[i]<sales[i]){
weeksup[i] <- ifelse(inv[i]>sales[i],NA,inv[i]/sales[i])
} else {
weeksup[i] <- approxfun(cumsum(sales[i:n]),1:(n-i+1))(inv[i])
}
}
#Your 'inventory more' is coded as -1 (a number) to avoid whole column being forced to a character vector
weeksup <- replace(weeksup,is.na(weeksup),-1)
return(weeksup) #for whole weeks, change this to `return(floor(weeksup))`
}
Data2 <- Data %>% group_by(SKU_CD,STORE_CD) %>% mutate(weekSup=weekSup(SALES,INVENTORY))
head(Data2,20)
SKU_CD STORE_CD FWK_CD SALES INVENTORY WeeksofSupply weekSup
<dbl> <dbl> <dbl> <dbl> <dbl> <chr> <dbl>
1 111 22 201627 5 29 11 11.3333333
2 111 22 201628 2 27 10 10.8333333
3 111 22 201629 2 25 9 9.8333333
4 111 22 201630 2 23 8 8.8333333
5 111 22 201631 1 22 8 8.0000000
6 111 22 201632 3 19 6 6.6666667
7 111 22 201633 2 17 5 5.8333333
8 111 22 201634 2 15 4 4.8333333
9 111 22 201635 3 12 3 3.6666667
10 111 22 201636 2 10 2 2.8333333
11 111 22 201637 3 25 Inventory More -1.0000000
12 111 22 201638 6 1 Inventory Less 0.1666667
13 222 33 201627 7 19 2 2.4444444
14 222 33 201628 8 17 2 2.0000000
15 222 33 201629 9 15 1 1.6000000
16 222 33 201630 10 13 1 1.2727273
17 222 33 201631 11 12 1 1.0833333
18 222 33 201632 12 9 Inventory Less 0.7500000
19 222 33 201633 13 7 Inventory Less 0.5384615
20 222 33 201634 14 5 Inventory Less 0.3571429
library（dplyr）#用于分组功能
#定义一个函数，用于根据销售和库存计算供应周数
weekSup谢谢你。关于如何将周作为一个专栏而不是多个专栏来实现这一点，有什么想法吗？非常感谢你的意见！我已经将其作为第二个答案发布了。非常感谢！这非常有帮助。
data$WeeksSupply <- sapply(1:nrow(data),function(i)
approxfun(cumsum(as.vector(c(data[i,2:11]))),1:10)(data$Inventory[i]))
data$WeeksSupply <- replace(data$WeeksSupply,is.na(data$WeeksSupply),
"Inventory Exceeds Forecast")
data
# A tibble: 2 x 12
Inventory Wk1 Wk2 Wk3 Wk4 Wk5 Wk6 Wk7 Wk8 Wk9 Wk10 WeeksSupply
<dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <chr>
1 200 20 15 25 40 35 45 30 50 45 55 6.66666666666667
2 2000 20 15 25 40 35 45 30 50 45 55 Inventory Exceeds Forecast
library(dplyr) #for the grouping functionality
#define a function to calculate weeks Supply from Sales and Inventory
weekSup <- function(sales,inv){
sales <- unlist(sales)
inv <- unlist(inv)
n <- length(sales)
weeksup <- rep(NA,n)
for(i in 1:n){
if(i==n | inv[i]<sales[i]){
weeksup[i] <- ifelse(inv[i]>sales[i],NA,inv[i]/sales[i])
} else {
weeksup[i] <- approxfun(cumsum(sales[i:n]),1:(n-i+1))(inv[i])
}
}
#Your 'inventory more' is coded as -1 (a number) to avoid whole column being forced to a character vector
weeksup <- replace(weeksup,is.na(weeksup),-1)
return(weeksup) #for whole weeks, change this to `return(floor(weeksup))`
}
Data2 <- Data %>% group_by(SKU_CD,STORE_CD) %>% mutate(weekSup=weekSup(SALES,INVENTORY))
head(Data2,20)
SKU_CD STORE_CD FWK_CD SALES INVENTORY WeeksofSupply weekSup
<dbl> <dbl> <dbl> <dbl> <dbl> <chr> <dbl>
1 111 22 201627 5 29 11 11.3333333
2 111 22 201628 2 27 10 10.8333333
3 111 22 201629 2 25 9 9.8333333
4 111 22 201630 2 23 8 8.8333333
5 111 22 201631 1 22 8 8.0000000
6 111 22 201632 3 19 6 6.6666667
7 111 22 201633 2 17 5 5.8333333
8 111 22 201634 2 15 4 4.8333333
9 111 22 201635 3 12 3 3.6666667
10 111 22 201636 2 10 2 2.8333333
11 111 22 201637 3 25 Inventory More -1.0000000
12 111 22 201638 6 1 Inventory Less 0.1666667
13 222 33 201627 7 19 2 2.4444444
14 222 33 201628 8 17 2 2.0000000
15 222 33 201629 9 15 1 1.6000000
16 222 33 201630 10 13 1 1.2727273
17 222 33 201631 11 12 1 1.0833333
18 222 33 201632 12 9 Inventory Less 0.7500000
19 222 33 201633 13 7 Inventory Less 0.5384615
20 222 33 201634 14 5 Inventory Less 0.3571429