我有以下数据集：

df = data.frame(cbind(user_id = c(rep(1, 4), rep(2,4)),
                  complete_order = c(rep(c(1,0,0,1), 2)),
                  order_date = c('2015-01-28', '2015-01-31', '2015-02-08', '2015-02-23', '2015-01-25', '2015-01-28', '2015-02-06', '2015-02-21')))  

library(lubridate)
df$order_date = as_date(df$order_date)

user_id complete_order order_date
      1              1 2015-01-28
      1              0 2015-01-31
      1              0 2015-02-08
      1              1 2015-02-23
      2              1 2015-01-25
      2              0 2015-01-28
      2              0 2015-02-06
      2              1 2015-02-21

我正在尝试计算每个用户仅完成的订单之间的天数差异。理想的结果将如下所示：

user_id complete_order order_date complete_order_time_diff
<fctr>         <fctr>     <date>              <time>
   1              1    2015-01-28             NA days
   1              0    2015-01-31              3 days
   1              0    2015-02-08             11 days
   1              1    2015-02-23             26 days
   2              1    2015-01-25             NA days
   2              0    2015-01-28              3 days
   2              0    2015-02-06             12 days
   2              1    2015-02-21             27 days

当我尝试这个解决方案时：

library(dplyr)

df %>% 
group_by(user_id) %>%
mutate(complete_order_time_diff = order_date[complete_order==1]-lag(order_date[complete_order==1))

它返回错误：

Error: incompatible size (3), expecting 4 (the group size) or 1

对此的任何帮助都会很棒，谢谢！

try this

library(dplyr)

df %>% group_by(user_id, complete_order) %>% 
   mutate(c1 = order_date - lag(order_date)) %>% 
   group_by(user_id) %>% mutate(c2 = order_date - lag(order_date)) %>% ungroup %>% 
   mutate(complete_order_time_diff = ifelse(complete_order==0, c2, c1)) %>% 
   select(-c(c1, c2))

Update

对于多个已取消的订单

 df %>% mutate(c3=cumsum( complete_order != "0")) %>% group_by(user_id, complete_order) %>% 
  mutate(c1 = order_date - lag(order_date)) %>% 
  group_by(user_id) %>% mutate(c2 = order_date - lag(order_date)) %>% 
  mutate(c2=as.numeric(c2)) %>% group_by(user_id, c3) %>% 
  mutate(c2=cumsum(ifelse(complete_order==1, 0, c2))) %>% ungroup %>% 
  mutate(complete_order_time_diff = ifelse(complete_order==0, c2, c1)) %>% 
  select(-c(c1, c2, c3))

logic

c3 is an id每次有订单时（即complete_order not 0) 增加 1。

c1计算日差 buuser_id（但对于不完整的订单，结果是错误的）

c2修复了这种不一致的情况c1关于不完整的订单。

希望这能解决问题。

我建议你结合使用group_by() and mutate(cumsum())更好地理解具有多个分组变量的结果。