我有一个来自支持票务系统的数据集，它记录了代理商在分类和响应客户请求时所做的每次点击。系统会为每次点击分配一个新的hist_id，但代理会点击几个字段，触发表格中的多行，他们认为是单个“交互”。
我的目标是通过对每个组中的第一个和最后一个modify_time值执行diff来计算每个交互的句柄时间。
我目前陷入困境，因为代理人将全天与案件进行多次互动。
以下是一个示例数据框：
hist_id <- c(1234, 2345, 3456, 4567, 5678, 6789, 7890)
case_id <- c(1, 1, 1, 1, 1, 1, 1)
agent_name <- c("John", "John", "John", "Paul", "Paul", "John", "John")
modify_time <- as.POSIXct(c(1510095120, 1510095180, 1510095240, 1510098600, 1510098720, 1510135200, 1510135320), origin = "1970-01-01")
df <- data.frame(hist_id, case_id, agent_name, modify_time)
在case_id和agent_name上使用group by按预期分组符合条件的所有行：
df %>% group_by(case_id, agent_name) %>% mutate(first = first(modify_time), last = last(modify_time), diff = min(difftime(last, first)))
这给了我这个：
# A tibble: 7 x 7
# Groups: case_id, agent_name [2]
hist_id case_id agent_name modify_time first last diff
<dbl> <dbl> <fctr> <dttm> <dttm> <dttm> <time>
1 1234 1 John 2017-11-07 16:52:00 2017-11-07 16:52:00 2017-11-08 04:02:00 40200 secs
2 2345 1 John 2017-11-07 16:53:00 2017-11-07 16:52:00 2017-11-08 04:02:00 40200 secs
3 3456 1 John 2017-11-07 16:54:00 2017-11-07 16:52:00 2017-11-08 04:02:00 40200 secs
4 4567 1 Paul 2017-11-07 17:50:00 2017-11-07 17:50:00 2017-11-07 17:52:00 120 secs
5 5678 1 Paul 2017-11-07 17:52:00 2017-11-07 17:50:00 2017-11-07 17:52:00 120 secs
6 6789 1 John 2017-11-08 04:00:00 2017-11-07 16:52:00 2017-11-08 04:02:00 40200 secs
7 7890 1 John 2017-11-08 04:02:00 2017-11-07 16:52:00 2017-11-08 04:02:00 40200 secs
返回John的第一个和最后一个modify_times。但是，我需要对case_id和agent_name的连续匹配进行分组，以便考虑Paul的交互。所以这里记录了三个互动：一个来自John，一个来自Paul，另一个来自John。
所需的输出将是这样的：
# A tibble: 7 x 7
# Groups: case_id, agent_name [2]
hist_id case_id agent_name modify_time first last diff
<dbl> <dbl> <fctr> <dttm> <dttm> <dttm> <time>
1 1234 1 John 2017-11-07 16:52:00 2017-11-07 16:52:00 2017-11-07 16:54:00 120 secs
2 2345 1 John 2017-11-07 16:53:00 2017-11-07 16:52:00 2017-11-07 16:54:00 120 secs
3 3456 1 John 2017-11-07 16:54:00 2017-11-07 16:52:00 2017-11-07 16:54:00 120 secs
4 4567 1 Paul 2017-11-07 17:50:00 2017-11-07 17:50:00 2017-11-07 17:52:00 120 secs
5 5678 1 Paul 2017-11-07 17:52:00 2017-11-07 17:50:00 2017-11-07 17:52:00 120 secs
6 6789 1 John 2017-11-08 04:00:00 2017-11-08 04:00:00 2017-11-08 04:02:00 120 secs
7 7890 1 John 2017-11-08 04:02:00 2017-11-08 04:00:00 2017-11-08 04:02:00 120 secs
答案 0 :(得分：4)
以下是一种整数方法，可以按processing cluster identity以及case_id和agent_name对组进行分区：
按顺序排列所有点击，每次hist_id序列遇到向新agent_name的转换时，都会生成一个新的ID标记。 cumsum每个集群处理块为每个代理生成一个唯一prcl_id的标记。df %>%
arrange(hist_id) %>% # to ensure there are no wrinkles
mutate(ag_chg_flg = ifelse(lag(agent_name) != agent_name, 1, 0) %>%
coalesce(0) # to reassign the first click in a case_id to 0 (from NA)
) %>%
group_by(case_id, agent_name) %>%
mutate(prcl_id = cumsum(ag_chg_flg) + 1) %>% # generate the proc_clst_id (starting at 1)
group_by(case_id, agent_name, prcl_id) %>% # group by the complete composite id
mutate(first = first(modify_time),
last = last(modify_time),
diff = min(difftime(last, first))
)
使用所有三个id，您可以在所需的分区中运行您选择的突变。
# A tibble: 7 x 9
# Groups: case_id, agent_name, prcl_id [3]
hist_id case_id agent_name modify_time ag_chg_flg prcl_id first last diff
<dbl> <dbl> <fctr> <dttm> <dbl> <dbl> <dttm> <dttm> <time>
1 1234 1 John 2017-11-07 14:52:00 0 1 2017-11-07 14:52:00 2017-11-07 14:54:00 2 mins
2 2345 1 John 2017-11-07 14:53:00 0 1 2017-11-07 14:52:00 2017-11-07 14:54:00 2 mins
3 3456 1 John 2017-11-07 14:54:00 0 1 2017-11-07 14:52:00 2017-11-07 14:54:00 2 mins
4 4567 1 Paul 2017-11-07 15:50:00 1 2 2017-11-07 15:50:00 2017-11-07 15:52:00 2 mins
5 5678 1 Paul 2017-11-07 15:52:00 0 2 2017-11-07 15:50:00 2017-11-07 15:52:00 2 mins
6 6789 1 John 2017-11-08 02:00:00 1 2 2017-11-08 02:00:00 2017-11-08 02:02:00 2 mins
7 7890 1 John 2017-11-08 02:02:00 0 2 2017-11-08 02:00:00 2017-11-08 02:02:00 2 mins
哪个可以帮到你：
Base::Base()