％>％来自dplyr包的管道函数，其作用是将前一步的结果直接传参给下一步的函数，从而省略了中间的赋值步骤，可以大量减少内存中的对象，节省内存

％>％，该符号将左边的对象作为第一个参数传递到右边的函数中

直接上栗子??：
library(data.table)
library(tidyr)
# 先虚构一数据框
set.seed(1)
date <- as.Date(‘2016-11-01‘) + 0:14
hour <- sample(1:24, 15)
min <- sample(1:60, 15)
second <- sample(1:60, 15)
event <- sample(letters, 15)
data <- data.table(date, hour, min, second, event)
head(data)[0:2]

##          date hour min second event
## 1: 2016-11-01    7  30     29     u
## 2: 2016-11-02    9  43     36     a

typeof(data)

## [1] "list"

------------------------------------------管道函数(%>%)很简单----------------------------------------------------

# 把date，hour，min和second列合并为新列datetime
# R中的日期时间格式为"Year-Month-Day-Hour:Min:Second"
#（一）(%>%) 合着写
dataNew <- data %>%unite(datehour, date, hour, sep = ‘ ‘) %>%unite(datetime, datehour, min, second, sep = ‘:‘)
dataNew

##                datetime event
##  1:  2016-11-01 7:30:29     u
##  2:  2016-11-02 9:43:36     a
##  3: 2016-11-03 13:58:60     l
##  4: 2016-11-04 20:22:11     q
##  5:  2016-11-05 5:44:47     p
##  6: 2016-11-06 18:52:37     k
##  7: 2016-11-07 19:12:43     r
##  8:  2016-11-08 12:35:6     i
##  9:  2016-11-09 11:7:38     e
## 10:  2016-11-10 1:14:21     b
## 11:  2016-11-11 3:20:42     w
## 12:  2016-11-12 14:1:32     t
## 13: 2016-11-13 23:19:52     h
## 14: 2016-11-14 21:41:26     s
## 15:  2016-11-15 8:16:25     o

# #（二）(%>%) 分步写

dataNew <- data %>%unite(datehour, date, hour, sep = ‘ ‘); dataNew

##          datehour min second event
##  1:  2016-11-01 7  30     29     u
##  2:  2016-11-02 9  43     36     a
##  3: 2016-11-03 13  58     60     l
##  4: 2016-11-04 20  22     11     q
##  5:  2016-11-05 5  44     47     p
##  6: 2016-11-06 18  52     37     k
##  7: 2016-11-07 19  12     43     r
##  8: 2016-11-08 12  35      6     i
##  9: 2016-11-09 11   7     38     e
## 10:  2016-11-10 1  14     21     b
## 11:  2016-11-11 3  20     42     w
## 12: 2016-11-12 14   1     32     t
## 13: 2016-11-13 23  19     52     h
## 14: 2016-11-14 21  41     26     s
## 15:  2016-11-15 8  16     25     o

dataNew %>% unite(datetime, datehour, min, second, sep = ‘:‘)

##                datetime event
##  1:  2016-11-01 7:30:29     u
##  2:  2016-11-02 9:43:36     a
##  3: 2016-11-03 13:58:60     l
##  4: 2016-11-04 20:22:11     q
##  5:  2016-11-05 5:44:47     p
##  6: 2016-11-06 18:52:37     k
##  7: 2016-11-07 19:12:43     r
##  8:  2016-11-08 12:35:6     i
##  9:  2016-11-09 11:7:38     e
## 10:  2016-11-10 1:14:21     b
## 11:  2016-11-11 3:20:42     w
## 12:  2016-11-12 14:1:32     t
## 13: 2016-11-13 23:19:52     h
## 14: 2016-11-14 21:41:26     s
## 15:  2016-11-15 8:16:25     o

# #（三）不用％>％也能写
dt<-unite(data,datehour, date, hour, sep = ‘ ‘)
dt

##          datehour min second event
##  1:  2016-11-01 7  30     29     u
##  2:  2016-11-02 9  43     36     a
##  3: 2016-11-03 13  58     60     l
##  4: 2016-11-04 20  22     11     q
##  5:  2016-11-05 5  44     47     p
##  6: 2016-11-06 18  52     37     k
##  7: 2016-11-07 19  12     43     r
##  8: 2016-11-08 12  35      6     i
##  9: 2016-11-09 11   7     38     e
## 10:  2016-11-10 1  14     21     b
## 11:  2016-11-11 3  20     42     w
## 12: 2016-11-12 14   1     32     t
## 13: 2016-11-13 23  19     52     h
## 14: 2016-11-14 21  41     26     s
## 15:  2016-11-15 8  16     25     o

dt2<-unite(dt,datatime,datehour, min, second, sep = ‘:‘)
dt2

##                datatime event
##  1:  2016-11-01 7:30:29     u
##  2:  2016-11-02 9:43:36     a
##  3: 2016-11-03 13:58:60     l
##  4: 2016-11-04 20:22:11     q
##  5:  2016-11-05 5:44:47     p
##  6: 2016-11-06 18:52:37     k
##  7: 2016-11-07 19:12:43     r
##  8:  2016-11-08 12:35:6     i
##  9:  2016-11-09 11:7:38     e
## 10:  2016-11-10 1:14:21     b
## 11:  2016-11-11 3:20:42     w
## 12:  2016-11-12 14:1:32     t
## 13: 2016-11-13 23:19:52     h
## 14: 2016-11-14 21:41:26     s
## 15:  2016-11-15 8:16:25     o