Question

我对dplyr在生成排名列时遇到了一些挑战来自特定消费者的事务日志的tbl_df对象。我的数据看起来像这样：

                                        consumerid merchant_id      eventtimestamp merchant_visit_rank
                                              (chr)       (int)              (time)          (dbl)
            1  004a5cc3-3d60-4d14-85b3-706e454aae13          52 2015-01-15 13:33:00              0
            2  004a5cc3-3d60-4d14-85b3-706e454aae13          56 2015-01-16 13:58:03              1
            3  004a5cc3-3d60-4d14-85b3-706e454aae13          56 2015-01-16 13:58:41              0
            4  004a5cc3-3d60-4d14-85b3-706e454aae13          52 2015-01-16 13:59:05              1
            5  004a5cc3-3d60-4d14-85b3-706e454aae13          52 2015-01-16 13:59:55              1
            6  004a5cc3-3d60-4d14-85b3-706e454aae13          52 2015-01-16 14:15:56              0
            7  004a5cc3-3d60-4d14-85b3-706e454aae13          58 2015-01-21 13:52:18              1
            8  004a5cc3-3d60-4d14-85b3-706e454aae13          58 2015-01-21 13:52:19              0
            9  004a5cc3-3d60-4d14-85b3-706e454aae13          54 2015-01-21 13:52:24              0
            10 004a5cc3-3d60-4d14-85b3-706e454aae13          58 2015-01-21 13:52:29              0
            ..                                  ...         ...                 ...            ...

我希望生成商家访问排名，以便在此次交易中告诉我此商家的订单会话。在我们的例子中，正确的排名看起来是：

                                        consumerid merchant_id      eventtimestamp merchant_visit_rank
                                              (chr)       (int)              (time)          (dbl)
            1  004a5cc3-3d60-4d14-85b3-706e454aae13          52 2015-01-15 13:33:00              1
            2  004a5cc3-3d60-4d14-85b3-706e454aae13          56 2015-01-16 13:58:03              2
            3  004a5cc3-3d60-4d14-85b3-706e454aae13          56 2015-01-16 13:58:41              2
            4  004a5cc3-3d60-4d14-85b3-706e454aae13          52 2015-01-16 13:59:05              3
            5  004a5cc3-3d60-4d14-85b3-706e454aae13          52 2015-01-16 13:59:55              3
            6  004a5cc3-3d60-4d14-85b3-706e454aae13          52 2015-01-16 14:15:56              3
            7  004a5cc3-3d60-4d14-85b3-706e454aae13          58 2015-01-21 13:52:18              4
            8  004a5cc3-3d60-4d14-85b3-706e454aae13          58 2015-01-21 13:52:19              4
            9  004a5cc3-3d60-4d14-85b3-706e454aae13          54 2015-01-21 13:52:24              5
            10 004a5cc3-3d60-4d14-85b3-706e454aae13          58 2015-01-21 13:52:29              6
            ..                                  ...         ...                 ...            ...

我试图在dplyr中使用窗口函数，如下所示：

            measure_media_interaction %>% 
              #selecting the fields we wish from the dataframe
              select(consumerid,merchant_id,eventtimestamp) %>%
              #mutate a placeholder column to be used for the rank 
              mutate(merchant_visit = 0) %>% 
              #sort them by consumer and timestamp
              arrange(consumerid,eventtimestamp) %>%
              #change the column so it shows that this merchant was the first this consumer visited 
              #or not 
              mutate(merchant_visit = 
                       ifelse(lead(merchant_id)!=merchant_id,merchant_visit,merchant_visit+1))

但是我被困住了，我不知道如何有效地做到这一点。有什么想法吗？

Answer 1

这是一个解决方案。我们使用lag来测试merchant_id是否更改以及cumsum来增加计数器。

measure_media_interaction %>% 
  select(consumerid,merchant_id,eventtimestamp) %>%
  arrange(consumerid,eventtimestamp) %>%
  mutate(merchant_visit=cumsum(c(1,(merchant_id != lag(merchant_id))[-1])))

根据分组变量的变化，使用dplyr生成订单排名列

1 个答案: