为什么我的计算没有根据日期进行总结,而是每次都给我相同的总价值?

_source.time

我在尝试执行计算(订单/日期计数)时一直遇到一个问题,该计算总是产生一个整体值,而不是分组(日期)的等效值。

问题的存在是因为字段 leads_T_orders 和 leads_F_orders leads_T_conv 和 leads_F_conv

我想做 orders / date_count _source.time(源时间)

date_count 是以下各项的总和: leads_TRUE leads_FALSE 和 no_claim_event orders leads\u T\u订单 , leads\u F\u订单 date\u计数 对应方

我哪里做错了?

gn %>%
  group_by(`_source.time`) %>%
  summarize(
    leads_TRUE = sum(lead == TRUE),
    leads_FALSE = sum(lead == FALSE),
    no_claim_event = sum(lead == "no-claim-event"),
    leads_T_orders = sum(gn$orders[gn$lead == TRUE]),
    leads_F_orders = sum(gn$orders[gn$lead == FALSE]),
    date_count = sum(leads_TRUE + leads_FALSE + no_claim_event), # Equal to total number of chats occurring each day.
    leads_T_conv = sum(leads_T_orders / leads_TRUE),
    leads_F_conv = sum(leads_F_orders / leads_FALSE),
    missing_claim_conv = sum(orders / nrow(gn[gn$lead == "no-claim-event", ])),
    overall_conv = sum(leads_T_conv + leads_F_conv + missing_claim_conv)
  ) %>%
  ungroup()

它产生了:

# A tibble: 64 x 11
   `_source.time` leads_TRUE leads_FALSE no_claim_event leads_T_orders leads_F_orders date_count leads_T_conv leads_F_conv
   <date>              <int>       <int>          <int>          <int>          <int>      <int>        <dbl>        <dbl>
 1 2018-05-14              8          86              9             73            216        103      0.00363     0.00181 
 2 2018-05-15             29          71              0             73            216        100      0.00242     0.00121 
 3 2018-05-16             27          82              0             73            216        109      0.00545     0.00272 
 4 2018-05-17             19           1              1             73            216         21      0.00121     0.000604

请注意,所讨论的数据帧包含私有数据,因此我无法提供 dput() 但这个问题更多的是关于 group_by()