r - 按名称排列的每一行最近 21 天的事件

Question

这是我的数据框的样子。最右边的两列是我想要的列。这两列检查在过去 21 天内是否有“电子邮件”ActivityType 以及在过去 21 天内是否有“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

根据这里的帮助: Extracting event types from last 21 day window我试过:

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，因为我只得到带有“Sale”的行的 TRUE。例如，第二行 ActivityType = Webinar，Email21 和 Webinar21 都应为 TRUE。当我定义过去 21 天时，我试图将事件发生的那一天也包括在内。

Answer 1

这个怎么样？

使用来自 data.table 的滚动连接:

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