循環遍歷列的唯一值並創建多個列

Question

我試圖打破我以前的問題，並制定了一個計划，以不同的步驟實現我最終尋求的目標。 目前，我正在嘗試進行循環，以確定是否為每個獨特的源打開了機械系統，如下面第一個表中的source列所示。

例如，我給出了以下簡介，告訴我4個季節中每個系統在典型工作日的系統開啟時間。 請注意，一些來源在一天中有多個時段，因此您可以看到堆棧2重復2個時段。

我現在想要實現的是，我已經創建了一些示例日期，並且想要遍歷每個獨特的源，並根據Profile表中提供的信息說明系統是在特定時間打開還是關閉。 到目前為止，我所做的是使用以下代碼創建下表：

以下代碼將創建上表：

# create dates table
dates =data.frame(dates=seq(
  from=as.POSIXct("2010-1-1 0:00", tz="UTC"),
  to=as.POSIXct("2012-12-31 23:00", tz="UTC"),
  by="hour"))  

# add year month day hour weekday column

dates$year <- format(dates[,1], "%Y") # year
dates$month <- format(dates[,1], "%m") # month
dates$day <- format(dates[,1], "%d") # day
dates$hour <- format(dates[,1], "%H") # hour
dates$weekday <- format(dates[,1], "%a") # weekday

# set system locale for reproducibility

Sys.setlocale(category = "LC_TIME", locale = "en_US.UTF-8")

# calculate season column

d = function(month_day) which(lut$month_day == month_day)
lut <- data.frame(all_dates = as.POSIXct("2012-1-1") + ((0:365) * 3600 * 24),
                  season = NA)
lut <- within(lut, { month_day = strftime(all_dates, "%b-%d") })

lut[c(d("Jan-01"):d("Mar-15"), d("Nov-08"):d("Dec-31")), "season"] = "winter"
lut[c(d("Mar-16"):d("Apr-30")), "season"] = "spring"
lut[c(d("May-01"):d("Sep-27")), "season"] = "summer"
lut[c(d("Sep-28"):d("Nov-07")), "season"] = "autumn"
rownames(lut) = lut$month_day

dates = within(dates, {
  season = lut[strftime(dates, "%b-%d"), "season"]
})

我現在要做的是為profile表中“ Source列中的每個唯一值添加右側的列，並根據以下條件估算數據集中每小時系統打開或關閉的天氣。

我正在努力解決如何在新列中使用多個if條件和粘貼值的類似於vlookup的編程概念。 例如，對於我的示例數據，循環應該創建2個程序，因為Source列只有2個唯一的源Stack 1和Stack 2 。 棘手的一點是帶有它的if語句需要類似的東西：

作為一個例子，表2的第一行應與季節列的值profile表，看看是否小時該特定季節期間內落在當系統將繼續運行。 如果它在規定的時間內落入，則說“打開”，如果外面只是說off 。 所以結果應該看起來像下圖中的2個紅色字體列：

冬天的一個例子：

春天的一個例子： 我已設法使用以下代碼獲取列的唯一值：

values <- unique(profile$Source)

但現在它只是不再使用for循環了。

我只是想知道是否有人可以給我任何建議，我如何使用表2中的獨特來源創建另外兩列的循環？

以下是我正在使用的典型的每周“個人資料”數據表：

> dput(profile)
structure(list(`Source no` = c(1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L), Source = structure(c(1L, 
1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
2L, 2L, 2L, 2L), .Label = c("Stack 1", "Stack 2"), class = "factor"), 
    Period = c(1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
    1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L), Day = structure(c(2L, 
    6L, 7L, 5L, 1L, 3L, 4L, 2L, 6L, 7L, 5L, 1L, 3L, 4L, 2L, 6L, 
    7L, 5L, 1L, 3L, 4L), .Label = c("Fri", "Mon", "Sat", "Sun", 
    "Thu", "Tue", "Wed"), class = "factor"), `Spring On` = c(0L, 
    0L, 0L, 0L, 0L, 0L, 0L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 15L, 
    15L, 15L, 15L, 15L, 15L, 15L), `Spring Off` = c(23L, 23L, 
    23L, 23L, 23L, 23L, 23L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 18L, 
    18L, 18L, 18L, 18L, 18L, 18L), `Summer On` = structure(c(1L, 
    1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
    1L, 1L, 1L, 1L, 1L), .Label = "off", class = "factor"), `Summer Off` = structure(c(1L, 
    1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
    1L, 1L, 1L, 1L, 1L), .Label = "off", class = "factor"), `Autumn On` = structure(c(1L, 
    1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
    1L, 1L, 1L, 1L, 1L), .Label = "off", class = "factor"), `Autumn Off` = structure(c(1L, 
    1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
    1L, 1L, 1L, 1L, 1L), .Label = "off", class = "factor"), `Winter On` = structure(c(1L, 
    1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 
    2L, 2L, 2L, 2L, 2L), .Label = c("0", "off"), class = "factor"), 
    `Winter Off` = structure(c(1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
    1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L), .Label = c("23", 
    "off"), class = "factor")), .Names = c("Source no", "Source", 
"Period", "Day", "Spring On", "Spring Off", "Summer On", "Summer Off", 
"Autumn On", "Autumn Off", "Winter On", "Winter Off"), class = "data.frame", row.names = c(NA, 
-21L))

非常感謝

Answer 1

為了實現從profile到dates的所需數據傳輸，您必須轉換profile數據，然后將其與dates 。 對於以下步驟，我使用了data.table包。

1）加載data.table包並將數據集轉換為data.tables（增強型數據幀）：

library(data.table)

setDT(profile)
setDT(dates)

2）重新格式化profile數據集中的值：

# set the 'off' values to NA
profile[profile=="off"] <- NA
# make sure that all the remaining values are numeric (which wasn't the case)
profile <- profile[, lapply(.SD, as.character), by=.(Source,Period,Day)][, lapply(.SD, as.numeric), by=.(Source,Period,Day)]

3）用於與值中的每個季節的eachhour一個（或兩者）的數據集創建Source的是on 。 我只為春季和冬季做過，因為夏季和秋季只有off / NA值（我們將在稍后處理）：

pr.spring <- profile[, .(season = "spring",
                         hour = c(`Spring On`:(`Spring Off`-1))),
                     by=.(Source,Period,Day)]
pr.winter <- profile[!is.na(`Winter On`), .(season = "winter",
                                            hour = c(`Winter On`:(`Winter Off`-1))),
                     by=.(Source,Period,Day)]

請注意，我使用了Spring Off - 1 。 那是因為我認為Stack在23:00關閉了。 通過使用-1我包括第22小時但不包括第23小時。 如果需要，您可以更改此設置。

4）將步驟3中的數據集綁定在一起，並為dcast操作准備結果數據集：

prof <- rbindlist(list(pr.spring,pr.winter))
prof <- prof[, .(weekday = Day, season, Source = gsub(" ",".",Source), hour = sprintf("%02d",hour))]

5）將步驟4中的數據集轉換為具有每個堆棧列的數據集，並將weekday列更改為字符。 后一步中的連接操作需要后者，因為dates數據集中的weekday列也是字符列：

profw <- dcast(prof, weekday + season + hour ~ Source, value.var = "hour", fun.aggregate = length, fill = 0)
profw[, weekday := as.character(weekday)]

6）將兩個數據集連接在一起並用0填充缺失的值（記得我說：“我們將在后面的步驟3處理這些數據集”）：

dates.new <- profw[dates, on=c("weekday", "season", "hour")][is.na(Stack.1), `:=` (Stack.1 = 0, Stack.2 = 0)]

結果數據集現在具有dates數據集中每個日期的堆棧列，其中1 ="on"和0 = "off" 。

---

結果數據集中的快照：

> dates.new[weekday=="Fri" & hour=="03" & month %in% c("03","04","09")]
    weekday season hour Stack.1 Stack.2               dates year month day
 1:     Fri winter   03       1       1 2010-03-05 03:00:00 2010    03  05
 2:     Fri winter   03       1       1 2010-03-12 03:00:00 2010    03  12
 3:     Fri spring   03       1       0 2010-03-19 03:00:00 2010    03  19
 4:     Fri spring   03       1       0 2010-03-26 03:00:00 2010    03  26
 5:     Fri spring   03       1       0 2010-04-02 03:00:00 2010    04  02
 6:     Fri spring   03       1       0 2010-04-09 03:00:00 2010    04  09
 7:     Fri spring   03       1       0 2010-04-16 03:00:00 2010    04  16
 8:     Fri spring   03       1       0 2010-04-23 03:00:00 2010    04  23
 9:     Fri spring   03       1       0 2010-04-30 03:00:00 2010    04  30
10:     Fri summer   03       0       0 2010-09-03 03:00:00 2010    09  03
11:     Fri summer   03       0       0 2010-09-10 03:00:00 2010    09  10
12:     Fri summer   03       0       0 2010-09-17 03:00:00 2010    09  17
13:     Fri summer   03       0       0 2010-09-24 03:00:00 2010    09  24
14:     Fri winter   03       1       1 2011-03-04 03:00:00 2011    03  04
15:     Fri winter   03       1       1 2011-03-11 03:00:00 2011    03  11
16:     Fri spring   03       1       0 2011-03-18 03:00:00 2011    03  18
17:     Fri spring   03       1       0 2011-03-25 03:00:00 2011    03  25
18:     Fri spring   03       1       0 2011-04-01 03:00:00 2011    04  01
19:     Fri spring   03       1       0 2011-04-08 03:00:00 2011    04  08
20:     Fri spring   03       1       0 2011-04-15 03:00:00 2011    04  15
21:     Fri spring   03       1       0 2011-04-22 03:00:00 2011    04  22
22:     Fri spring   03       1       0 2011-04-29 03:00:00 2011    04  29
23:     Fri summer   03       0       0 2011-09-02 03:00:00 2011    09  02
24:     Fri summer   03       0       0 2011-09-09 03:00:00 2011    09  09
25:     Fri summer   03       0       0 2011-09-16 03:00:00 2011    09  16
26:     Fri summer   03       0       0 2011-09-23 03:00:00 2011    09  23
27:     Fri autumn   03       0       0 2011-09-30 03:00:00 2011    09  30
28:     Fri winter   03       1       1 2012-03-02 03:00:00 2012    03  02
29:     Fri winter   03       1       1 2012-03-09 03:00:00 2012    03  09
30:     Fri spring   03       1       0 2012-03-16 03:00:00 2012    03  16
31:     Fri spring   03       1       0 2012-03-23 03:00:00 2012    03  23
32:     Fri spring   03       1       0 2012-03-30 03:00:00 2012    03  30
33:     Fri spring   03       1       0 2012-04-06 03:00:00 2012    04  06
34:     Fri spring   03       1       0 2012-04-13 03:00:00 2012    04  13
35:     Fri spring   03       1       0 2012-04-20 03:00:00 2012    04  20
36:     Fri spring   03       1       0 2012-04-27 03:00:00 2012    04  27
37:     Fri summer   03       0       0 2012-09-07 03:00:00 2012    09  07
38:     Fri summer   03       0       0 2012-09-14 03:00:00 2012    09  14
39:     Fri summer   03       0       0 2012-09-21 03:00:00 2012    09  21
40:     Fri autumn   03       0       0 2012-09-28 03:00:00 2012    09  28