检查R数据。为另一列中的相等值设置框架列

Question

我正在寻找以下问题的矢量化解决方案。有些客户可以同时拥有两种不同产品x或y中的一种。我想识别同一客户的产品x后面跟有产品y的所有行。在这种情况下，产品x的< code>to_date将与产品y的< code>from_date相同。下面是一个示例:

customerid = c(rep(1,2),rep(2,3))
product = c("x", "y", "x", "x", "y")
from_date = as.Date(c("2000-01-01", "2000-06-07","2001-02-01","2005-01-01","2005-11-01"))
to_date = as.Date(c("2000-06-07", "2000-10-31","2002-04-01","2005-11-01","2006-01-01"))

data.frame(customerid, product, from_date, to_date)

      customerid product  from_date    to_date
1          1       x 2000-01-01 2000-06-07
2          1       y 2000-06-07 2000-10-31
3          2       x 2001-02-01 2002-04-01
4          2       x 2005-01-01 2005-11-01
5          2       y 2005-11-01 2006-01-01

所需的输出如下所示：

  customerid product  from_date    to_date followed_by_y
1          1       x 2000-01-01 2000-06-07             yes
2          1       y 2000-06-07 2000-10-31             no
3          2       x 2001-02-01 2002-04-01             no
4          2       x 2005-01-01 2005-11-01             yes
5          2       y 2005-11-01 2006-01-01             no

到目前为止，我的方法是使用 dplyr 按服装对 data.frame 进行分组。但是我不知道如何在from_date中检查to_date中相等的值。

Answer 1

您可以检查所有条件，如下所示：

library(dplyr)

df %>%
  group_by(customerid) %>%
  mutate(followed_by_y = c('no', 'yes')[(product == 'x' &
                                         lead(product) == 'y' &
                                         to_date == lead(from_date)) + 1])

输出:

# A tibble: 5 x 5
# Groups:   customerid [2]
  customerid product from_date  to_date    followed_by_y
       <dbl> <fct>   <date>     <date>     <chr>        
1          1 x       2000-01-01 2000-06-07 yes          
2          1 y       2000-06-07 2000-10-31 no           
3          2 x       2001-02-01 2002-04-01 no           
4          2 x       2005-01-01 2005-11-01 yes          
5          2 y       2005-11-01 2006-01-01 no   

注意，这与说：

library(dplyr)

df %>%
  group_by(customerid) %>%
  mutate(followed_by_y = case_when(
    product == 'x' & lead(product) == 'y' & to_date == lead(from_date) ~ 'yes',
    TRUE ~ 'no')
  )