的R - 透視預測結果

Question

我有以下來源的數據：根據以往的156周

，我想明年52周做預測。而下面的代碼工作正常

my.ds <- myDS[1, -c(3,4,5,6)] #reading my source file 
my.start <- myDS[1, c(3)] 
my.product <- myDS[1, c("Product")] 
my.product <- myDS[1, c("Location")] 
my.result <- melt(my.ds, id = c("Product","Location")) 
my.result[order(my.result$variable),] 
my.ts <- ts(my.result$value, frequency=52, start=c(my.start,1)) 
my.fc <- forecast(my.ts, h=52) 
my.fc 

預測給我下面的輸出：

  Point Forecast  Lo 80  Hi 80  Lo 95  Hi 95 
2003.000  1637.7675 -8.610502 3284.146 -880.15039 4155.685 
2003.019  1453.9059 -195.169681 3102.981 -1068.13753 3975.949 
2003.038  8668.6921 7016.923492 10320.461 6142.53000 11194.854 
2003.058  5851.0741 4196.616771 7505.531 3320.79997 8381.348 
2003.077  4333.9240 2676.782333 5991.066 1799.54453 6868.303 
2003.096  4284.5899 2624.768291 5944.412 1746.11178 6823.068 

我想現在要做的是：

1. 添加產品&位置回到這個結果集
2. 添加一個計算列：（你好95） - （點預測）（我需要這個以及點預測列）
3. 樞轉回表如下

試圖重塑在這裏，但真的不知道如何執行它，因爲結果似乎並沒有以表格的形式。

Link to download source file in csv

Answer 1

繼確實給我想要的輸出：

library(forecast) 
library(reshape) 
library(plyr) 

#exclude non required columns 
my.ds <- myDS[, -c(3,4,5,6)] 
#set the required date, Product, Location 
my.start <- myDS[1, c(3)] 
my.product <- myDS[1, c("Product")] 
my.location <- myDS[1, c("Location")] 
#unpivot the table 
my.result <- melt(my.ds, id = c("Product","Location")) 
#run forecasting 
# set the CIs we want to use here, so we can reuse this vector 
cis <- c(80, 95) 
# generate the forecast using those ci levels 
my.ts <- ts(my.result$value, frequency=52, start=c(my.start,1)) 
f <- forecast(my.ts, h=52, level=cis) 
# make a data frame containing the forecast information, including the index 
z <- as.data.frame(cbind(seq(1:52), 
        f$mean, 
        Reduce(cbind, lapply(seq_along(cis), function(i) cbind(f$lower[,i], f$upper[,i]))))) 
# give the columns better names 
names(z) <- c("index", "mean", paste(rep(c("lower", "upper"), times = length(cis)), rep(cis, each = 2), sep = ".")) 
# manipulate the results as you describe 
zw <- z %>% 
    # keep only the variable you want and its index 
    mutate(sssf = upper.95 - mean) %>% 
    select(index, mean, sssf) %>% 
    # add product and location info 
    mutate(product = my.product, 
    location = my.location) %>% 
# rearrange columns so it's easier to read 
select(product, location, index, mean, sssf) 
zw <- melt(zw, id.vars = c("product", "location", "index"), measure.vars = c("mean","sssf")) 
data.set <- cast(zw, product + location ~ index + variable, value = "value")