首先，我假设您的data.frame名为dd。这里是一个复制/粘贴的形式

dd <- structure(list(date = structure(c(1L, 2L, 2L, 3L, 4L, 5L, 5L, 
5L, 6L, 7L, 7L, 8L, 9L, 9L), .Label = c("10/22/2013 23:51", "10/22/2013 23:52", 
"10/23/2013 7:03", "10/23/2013 7:13", "10/23/2013 7:31", "10/24/2013 0:54", 
"10/24/2013 1:16", "10/24/2013 3:27", "10/24/2013 7:30"), class = "factor"), 
    apps = structure(c(1L, 2L, 3L, 3L, 1L, 2L, 1L, 2L, 2L, 3L, 
    3L, 1L, 1L, 1L), .Label = c("A", "B", "C"), class = "factor"), 
    long = c(2L, 3L, 1L, 5L, 1L, 4L, 5L, 2L, 3L, 2L, 1L, 2L, 
    3L, 1L)), .Names = c("date", "apps", "long"), class = "data.frame", row.names = c(NA, 
-14L))

您应该将日期转换为适当的日期值

dd$date <- as.POSIXct(as.character(dd$date), format="%m/%d/%Y %H:%M", tz="GMT")

然后，您可以使用aggregate创建一个漂亮的data.frame，这里使用as.Date剥离时间

aggregate(long ~ as.Date(date) + apps, dd, FUN=sum)

这个返回

as.Date(date) apps long
1    2013-10-22    A    2
2    2013-10-23    A    6
3    2013-10-24    A    6
4    2013-10-22    B    3
5    2013-10-23    B    6
6    2013-10-24    B    3
7    2013-10-22    C    1
8    2013-10-23    C    5
9    2013-10-24    C    3

我相当肯定这是重复的地方，但我在我的前三个搜索失败，所以在这里：

tapply( dat$long, list(dt = format( as.POSIXct(dat$date, "%d-%m-%Y %H:%M"), 
                                    "%d-%m-%Y"),
                        grp=dat$apps ),
                  sum)

在Flick先生的dd上使用dplyr

library(dplyr)
dd%>% 
group_by(apps, date=gsub("\\s+.*","",date))%>%
summarize(long=sum(long))
#      apps       date long
# 1    A 10/22/2013    2
# 2    A 10/23/2013    6
# 3    A 10/24/2013    6
# 4    B 10/22/2013    3
# 5    B 10/23/2013    6
# 6    B 10/24/2013    3
# 7    C 10/22/2013    1
# 8    C 10/23/2013    5
# 9    C 10/24/2013    3