OSWORKFLOW与spring集成(OSWORKFLOW integration with spring)

 "ts"类通常不适合该类型的数据。 假设DF是在本答案末尾的Note中可重复显示的数据帧，我们将其转换为"zoo"类对象，然后执行一些操作。 也可以使用相关的xts包。 
 
library(zoo)

z <- read.zoo(DF, index = 1:2, tz = "")

window(z, start = "2014-05-22 15:25:00")

head(z, 3) # first 3
head(z, -3) # all but last 3
tail(z, 3) # last 3
tail(z, -3) # all but first 3

z[2:4] # 2nd, 3rd and 4th element of z

coredata(z) # numeric vector of data values
time(z) # vector of datetimes

fortify.zoo(z) # data frame whose 2 cols are (1) datetimes and (2) data values

aggregate(z, as.Date, mean) # convert to daily averaging values

ym <- aggregate(z, as.yearmon, mean) # convert to monthly averaging values
frequency(ym) <- 12 # only needed since ym only has length 1
as.ts(ym) # year/month series can be reasonably converted to ts

plot(z)

library(ggplot2)
autoplot(z)
 
 read.zoo也可用于从文件中读取数据。 
 
 注意：以上以可重复的形式使用的DF ： 
 
DF <- structure(list(Date = structure(c(1L, 1L, 1L, 1L, 1L, 1L), .Label = "2014-05-22", 
class = "factor"), 
    Time = structure(1:6, .Label = c("15:15:00", "15:20:00", 
    "15:25:00", "15:30:00", "15:35:00", "15:40:00"), class = "factor"), 
    T1 = c(21.6, 21.2, 21.3, 21.5, 21.1, 21.5)), .Names = c("Date", 
"Time", "T1"), class = "data.frame", row.names = c("1", "2", 
"3", "4", "5", "6"))

"ts" class is typically not a good fit for that type of data. Assuming DF is the data frame shown reproducibly in the Note at the end of this answer we convert it to a "zoo" class object and then perform some manipulations. The related xts package could also be used.
 
library(zoo)

z <- read.zoo(DF, index = 1:2, tz = "")

window(z, start = "2014-05-22 15:25:00")

head(z, 3) # first 3
head(z, -3) # all but last 3
tail(z, 3) # last 3
tail(z, -3) # all but first 3

z[2:4] # 2nd, 3rd and 4th element of z

coredata(z) # numeric vector of data values
time(z) # vector of datetimes

fortify.zoo(z) # data frame whose 2 cols are (1) datetimes and (2) data values

aggregate(z, as.Date, mean) # convert to daily averaging values

ym <- aggregate(z, as.yearmon, mean) # convert to monthly averaging values
frequency(ym) <- 12 # only needed since ym only has length 1
as.ts(ym) # year/month series can be reasonably converted to ts

plot(z)

library(ggplot2)
autoplot(z)
 
read.zoo could also have been used to read the data in from a file.
 
Note: DF used above in reproducible form:
 
DF <- structure(list(Date = structure(c(1L, 1L, 1L, 1L, 1L, 1L), .Label = "2014-05-22", 
class = "factor"), 
    Time = structure(1:6, .Label = c("15:15:00", "15:20:00", 
    "15:25:00", "15:30:00", "15:35:00", "15:40:00"), class = "factor"), 
    T1 = c(21.6, 21.2, 21.3, 21.5, 21.1, 21.5)), .Names = c("Date", 
"Time", "T1"), class = "data.frame", row.names = c("1", "2", 
"3", "4", "5", "6"))