r - 过去21天每行各名称的事件

这就是我的数据框的样子。最右边的两列是我想要的列。这两列将检查条件，在过去21天内是否存在“电子邮件” ActivityType，以及在过去21天内是否存在“网络研讨会” ActivityType。

 Name      ActivityType     ActivityDate  Email(last21days) Webinar(last21day)**
John       Email            1/1/2014        TRUE                  NA
John       Webinar          1/5/2014        TRUE                 TRUE
John       Sale             1/20/2014       TRUE                 TRUE
John       Webinar          3/25/2014       NA                   TRUE
John       Sale             4/1/2014        NA                   TRUE
John       Sale             7/1/2014        NA                   NA
Tom        Email            1/1/2015        TRUE                   NA
Tom        Webinar          1/5/2015        TRUE                 TRUE
Tom        Sale             1/20/2015      TRUE                 TRUE
Tom        Webinar          3/25/2015       NA                   TRUE
Tom        Sale              4/1/2015        NA                   TRUE
Tom        Sale              7/1/2015       NA                   NA

基于此处的帮助:
Extracting event types from last 21 day window
我试过了:

df$ActivityDate <- as.Date(df$ActivityDate)
library(data.table)
setDT(df)
setkey(df, Name,ActivityDate)
Elsetemp <- df[, .(Name, ActivityDate, ActivityType)]
df[Elsetemp, `:=`(Email21 = as.logical(which(i.ActivityType == "Email")),
                        Webinar21 = as.logical(which(i.ActivityType == "Webinar"))),
         roll = -21, by = .EACHI]

无济于事，因为我只为带有“销售”的行获取TRUE。例如，第二行，其中ActivityType =网络研讨会，Email21和Webinar21都应说TRUE。当我定义过去21天时，我试图包括事件发生的那一天。

最佳答案

这个怎么样？

使用来自data.table的滚动联接:

require(data.table)
dt[, ActivityDate := as.Date(ActivityDate, format="%m/%d/%Y")]
setkey(dt, Name, ActivityDate)

roll_index <- function(x, types, roll=21) {
    lapply(types, function(type) {
         idx = x[ActivityType == type][x, roll=roll, which=TRUE]
         as.logical(idx)
    })
}
dt[, c("Email_21", "Webinar_21") := roll_index(dt, c("Email", "Webinar"))]

#     Name ActivityType ActivityDate Email_21 Webinar_21
#  1: John        Email   2014-01-01     TRUE         NA
#  2: John      Webinar   2014-01-05     TRUE       TRUE
#  3: John         Sale   2014-01-20     TRUE       TRUE
#  4: John      Webinar   2014-03-25       NA       TRUE
#  5: John         Sale   2014-04-01       NA       TRUE
#  6: John         Sale   2014-07-01       NA         NA
#  7:  Tom        Email   2015-01-01     TRUE         NA
#  8:  Tom      Webinar   2015-01-05     TRUE       TRUE
#  9:  Tom         Sale   2015-01-20     TRUE       TRUE
# 10:  Tom      Webinar   2015-03-25       NA       TRUE
# 11:  Tom         Sale   2015-04-01       NA       TRUE
# 12:  Tom         Sale   2015-07-01       NA         NA