以.csv格式储存的通量数据为例，这些半小时时间分辨率的通量数据，通常是以第一列数据作为ID。R读取这一列数据时会遇到各种情况。如何读成R里面的时间格式的呢？以下是我用到的几种方法，供大家参考，希望有一种适合你的数据。

方法一：

# 如果你遇到这样的时间数据

DT <- c("2014/4/11 8:00:00","2014/4/11 8:30:00")

class(DT)

# "character"

library(timeDate)

DT.new <- timeDate (DT,  format="%Y/%m/%d %H:%M:%S")

class(DT.new)

# "timeDate"

# OK,解决了

# 如果你遇到这样的时间数据

DT <- c("2014-4-11 8:00:00","2014/4/11 8:30:00") # / 成了 -

class(DT)

# "character"

DT.new <- timeDate (DT,  format="%Y-%m-%d %H:%M:%S")

class(DT.new)

# "timeDate"

# OK,解决了

# timeDate 里面有好用的时间处理函数

dayOfYear(DT.new)

# 101 101

# 计算day of year

# 方法二

# 自己写

DT2 <- seq(strptime('2014/4/11 0:00:00',"%Y/%m/%d %H:%M:%S"), by = "30 mins", length.out = 48)

# 2014/4/11 0:00:00 是数据开始的时间，by = "30 mins"表示数据的频率，length.out = 48表示数据的长度

class(DT2)

#"POSIXct" "POSIXt"

DT2

#[1] "2014-04-11 00:00:00 CST" "2014-04-11 00:30:00 CST" "2014-04-11 01:00:00 CST"

#[4] "2014-04-11 01:30:00 CST" "2014-04-11 02:00:00 CST" "2014-04-11 02:30:00 CST"

#[7] "2014-04-11 03:00:00 CST" "2014-04-11 03:30:00 CST" "2014-04-11 04:00:00 CST"

#[10] "2014-04-11 04:30:00 CST" "2014-04-11 05:00:00 CST" "2014-04-11 05:30:00 CST"

#[13] "2014-04-11 06:00:00 CST" "2014-04-11 06:30:00 CST" "2014-04-11 07:00:00 CST"

#[16] "2014-04-11 07:30:00 CST" "2014-04-11 08:00:00 CST" "2014-04-11 08:30:00 CST"

#[19] "2014-04-11 09:00:00 CST" "2014-04-11 09:30:00 CST" "2014-04-11 10:00:00 CST"

#[22] "2014-04-11 10:30:00 CST" "2014-04-11 11:00:00 CST" "2014-04-11 11:30:00 CST"

#[25] "2014-04-11 12:00:00 CST" "2014-04-11 12:30:00 CST" "2014-04-11 13:00:00 CST"

#[28] "2014-04-11 13:30:00 CST" "2014-04-11 14:00:00 CST" "2014-04-11 14:30:00 CST"

#[31] "2014-04-11 15:00:00 CST" "2014-04-11 15:30:00 CST" "2014-04-11 16:00:00 CST"

#[34] "2014-04-11 16:30:00 CST" "2014-04-11 17:00:00 CST" "2014-04-11 17:30:00 CST"

#[37] "2014-04-11 18:00:00 CST" "2014-04-11 18:30:00 CST" "2014-04-11 19:00:00 CST"

#[40] "2014-04-11 19:30:00 CST" "2014-04-11 20:00:00 CST" "2014-04-11 20:30:00 CST"

#[43] "2014-04-11 21:00:00 CST" "2014-04-11 21:30:00 CST" "2014-04-11 22:00:00 CST"

#[46] "2014-04-11 22:30:00 CST" "2014-04-11 23:00:00 CST" "2014-04-11 23:30:00 CST"