循环遍历列的唯一值并创建多个列
Ach*_*hak 3 loops r dataframe dplyr data.table
我试图打破我以前的问题,并制定了一个计划,以不同的步骤实现我最终寻求的目标.目前,我正在尝试进行循环,以确定是否为每个独特的源打开了机械系统,如下面的第一个表中所示source.
例如,我给出了以下简介,告诉我4个季节中每个系统在典型工作日的系统开启时间.请注意,一些来源在一天中有多个时段,因此您可以看到堆栈2重复2个时段.
我现在想要实现的是我已经创建了一些样本日期,并希望循环每个独特的源,并根据Profile表中提供的信息说明系统是否在特定时间打开或关闭.到目前为止,我所做的是使用以下代码创建下表:
以下代码将创建上表:
# 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$month_day
dates = within(dates, {
season = lut[strftime(dates, "%b-%d"), "season"]
})
我现在要做的是为表中Source列中的每个唯一值添加右侧的列,profile并根据以下标准估算系统在数据集中每小时打开或关闭的天气.
我正在努力解决如何在新列中使用多个if条件和粘贴值的类似于vlookup的编程概念.例如,对于我的示例数据,循环应该创建2个程序,因为该Source列只有2个唯一的源Stack 1和Stack 2.棘手的一点是带有它的if语句需要类似的东西:
作为示例,表2的第一行应该与季节列的值匹配,profile并查看该小时是否在系统将打开的特定季节的时间段内.如果它落在规定的时间内,那么说"打开",如果外面只是说off.所以结果应该看起来像下图中的2个红色字体列:
冬天的一个例子:
春天的一个例子:
我已设法使用以下代码获取列的唯一值:
values <- unique(profile$Source)
但现在它只是不再使用for循环了.
我只是想知道是否有人可以给我任何建议,我如何使用表2中的独特来源创建另外两列的循环？
以下是我正在使用的典型的每周"个人资料"数据表:
> 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))
非常感谢
为了实现数据从期望转移profile到dates,你将不得不转换profile数据,然后用加入它dates.对于以下步骤,我使用了data.table包.
1)加载data.table包并将数据集转换为data.tables(增强型数据帧):
library(data.table)
setDT(profile)
setDT(dates)
2)重新格式化profile数据集中的值:
# 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)为每个季节创建数据集,其中包含每个季节的一个(或两个)Source的值on.我只为春季和冬季做过,因为夏季和秋季只有off/ NA值(我们将在稍后处理):
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)]
请注意,我用过Spring Off - 1.那是因为我认为Stack在23:00关闭了.通过使用-1我包括第22小时但不是第23小时.如果需要,您可以更改此设置.
4)将步骤3中的数据集绑定在一起,并为dcast操作准备结果数据集:
prof <- rbindlist(list(pr.spring,pr.winter))
prof <- prof[, .(weekday = Day, season, Source = gsub(" ",".",Source), hour = sprintf("%02d",hour))]
5)将步骤4中的数据集转换为具有每个堆栈列的数据集,并将列更改weekday为字符.后一步中的连接操作需要后者,因为数据集中的weekday列dates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)将两个数据集连接在一起,并用0's记住缺失的值(记得我说:"我们将在后面的步骤3中处理它们":
dates.new <- profw[dates, on=c("weekday", "season", "hour")][is.na(Stack.1), `:=` (Stack.1 = 0, Stack.2 = 0)]
结果数据集现在具有数据集中每个日期的堆栈列dates,其中1 ="on"和0 = "off".
结果数据集中的快照:
> 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