我正在尝试在数据框中找到组中的特定模式。通过电子邮件，下订单的人员和金额，获取以下订单数据框。
set.seed(123)
dates = sample(seq(as.Date("2017-01-01"),as.Date("2017-12-31"), by = 'day'), 2000, replace = TRUE)
amount <- sample(-50:100, 2000, replace = TRUE)
placedorder <- sample(c(NA, NA, NA, "jeff", "alex", "steve", "amy", "john", "larry", "ryan"), 2000, replace = TRUE)
email <- sample(paste0(1:200, "@gmail.com"), 2000, replace = TRUE)
df <- data.frame(dates, email, placedorder, amount, stringsAsFactors = FALSE)
我想通过电子邮件地址找到所有这三个发生的组，以及它们在日期继承中发生的位置：
placedorder为NAplacedorder为NAplaceholder不是NA例：
# A tibble: 10 x 4
# Groups: email [1]
dates email placedorder amount
<date> <chr> <chr> <int>
1 2017-02-10 [email protected] <NA> 68 # satisfies #1
2 2017-02-27 [email protected] <NA> -21 # satisfies #2
3 2017-03-07 [email protected] jeff -9
4 2017-03-09 [email protected] steve -93
5 2017-03-14 [email protected] steve 22 # satisfies #3
6 2017-03-18 [email protected] steve -81
7 2017-04-28 [email protected] <NA> -12
8 2017-05-06 [email protected] <NA> 4
9 2017-06-03 [email protected] jeff -40
10 2017-06-03 [email protected] larry 13 #(this also satisfies #3)
上面的例子都在同一个email中，并且3个滤波器中的每一个都相对于时间一个接一个地发生。
我的尝试，我认为发现这些发生的地方，但没有考虑到日期和这种情况相继发生。而且，实际上将其过滤到这些订单，将是最好的。
df2 <- df %>%
group_by(email) %>%
filter(any(is.na(placedorder) & amount > 0),
any(is.na(placedorder) & amount < 0),
any(!is.na(placedorder) & amount > 0)
)
提前致谢！
假设我对“第一顺序”和“第二顺序”的解释是正确的，这里是在dplyr中设置命令的一种方法
library(dplyr)
df %>% group_by(email) %>%
arrange(email, dates) %>%
mutate(order_num=1:n()) %>%
#An order was placed with a positive value and where placedorder is NA
filter((is.na(placedorder) & amount>0) |
# An order was placed after the first one, with a negative value and where placedorder is NA
(is.na(placedorder) & amount <0 & order_num >1) |
# An order was placed after the second order, with a positive value and where placeholder is not NA
(!is.na(placedorder) & amount >0 & order_num > 2)
)
更新：非常感谢你澄清这个问题。基本上，您希望“观察客户状态”，并且只有在观察到之前的类型后才开始跟踪下一类事件。这是一个（稍微冗长，但希望可以理解）试图追踪客户过渡这些“状态”的尝试：
df %>% group_by(email) %>%
arrange(email, dates) %>%
mutate(event_1=ifelse(is.na(placedorder) & amount>0, 1, 0),
post_event_1=cumsum(event_1),
# only if at least one event_1 has happened
event_2=ifelse(post_event_1>=1 & is.na(placedorder) & amount <0, 1,0),
post_event_2=cumsum(event_2),
# only if at least one event_2 has happened
event_3=ifelse(post_event_2>=1 & !is.na(placedorder) & amount >0, 1, 0)) %>%
# only interested in first occurance of event_1 and event_2 preceding event_3
filter((event_1==1 & post_event_1==1) | (event_2==1 & post_event_2==1) | event_3 ==1)
# A tibble: 390 x 9
# Groups: email [165]
dates email placedorder amount event_1 post_event_1 event_2 post_event_2 event_3
<date> <chr> <chr> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 2017-01-29 [email protected] <NA> 76 1 1 0 0 0
2 2017-05-25 [email protected] <NA> -37 0 1 1 1 0
3 2017-08-14 [email protected] steve 53 0 1 0 2 1
4 2017-12-21 [email protected] john 92 0 2 0 4 1
5 2017-02-08 [email protected] <NA> 89 1 1 0 0 0
6 2017-01-16 [email protected] <NA> 40 1 1 0 0 0
7 2017-03-18 [email protected] <NA> 20 1 1 0 0 0
8 2017-05-16 [email protected] <NA> -45 0 2 1 1 0
9 2017-06-08 [email protected] larry 46 0 2 0 2 1
10 2017-07-22 [email protected] john 93 0 3 0 2 1
# ... with 380 more rows
有一些“未完成的链”，例如当客户进展到state_1但没有进一步。不确定是否要丢弃这些（为此您可以计算每封电子邮件的观察数量并删除少于3条记录的观察数据）。