Extracting event types from last 21 day window - r

Question

My dataframe looks like this. The two rightmost columns are my desired columns.
**Name ActivityType ActivityDate Email(last 21 says) Webinar(last21)**
John Email 1/1/2014 NA NA
John Webinar 1/5/2014 NA NA
John Sale 1/20/2014 Yes Yes
John Webinar 3/25/2014 NA NA
John Sale 4/1/2014 No Yes
John Sale 7/1/2014 No No
Tom Email 1/1/2015 NA NA
Tom Webinar 1/5/2015 NA NA
Tom Sale 1/20/2015 Yes Yes
Tom Webinar 3/25/2015 NA NA
Tom Sale 4/1/2015 No Yes
Tom Sale 7/1/2015 No No
I am just trying to create a yes/no variable that denotes whether there was an email or a webinar in the last 21 days for each "Sale" transaction. I was thinking(mock code) along the lines of using dplyr this way:
custlife %>%
group_by(Name) %>%
mutate(Email(last21days)=lag(ifelse(ActivityType = "Email" & ActivityDate of email within (activity date of sale - 21),Yes,No)).
I am not sure of the way to implement this. Kindly help. Your help is sincerely appreciated!

Answer 1

Here's a possible data.table solution. Here I'm creating 2 temporary data sets- one for Sale and one for the rest of activity types and then joining between them by a rolling window of 21 while using by = .EACHI in order to check conditions in each join. Then, I'm joining the result to the original data set.
Convert the date column to Date class and key the data by Name and Date (for the final/rolling join)
library(data.table)
setkey(setDT(df)[, ActivityDate := as.IDate(ActivityDate, "%m/%d/%Y")], Name, ActivityDate)
Create 2 temporary data sets per each activity
Saletemp <- df[ActivityType == "Sale", .(Name, ActivityDate)]
Elsetemp <- df[ActivityType != "Sale", .(Name, ActivityDate, ActivityType)]
Join by a rolling window of 21 to the sales temporary data set while checking conditions
Saletemp[Elsetemp, `:=`(Email21 = as.logical(which(i.ActivityType == "Email")),
Webinar21 = as.logical(which(i.ActivityType == "Webinar"))),
roll = -21, by = .EACHI]
Join everything back
df[Saletemp, `:=`(Email21 = i.Email21, Webinar21 = i.Webinar21)]
df
# Name ActivityType ActivityDate Email21 Webinar21
# 1: John Email 2014-01-01 NA NA
# 2: John Webinar 2014-01-05 NA NA
# 3: John Sale 2014-01-20 TRUE TRUE
# 4: John Webinar 2014-03-25 NA NA
# 5: John Sale 2014-04-01 NA TRUE
# 6: John Sale 2014-07-01 NA NA
# 7: Tom Email 2015-01-01 NA NA
# 8: Tom Webinar 2015-01-05 NA NA
# 9: Tom Sale 2015-01-20 TRUE TRUE
# 10: Tom Webinar 2015-03-25 NA NA
# 11: Tom Sale 2015-04-01 NA TRUE
# 12: Tom Sale 2015-07-01 NA NA

Answer 2

Here is another option with base R:
df is first split according to Name and then, among each subset, for each Sale, it looks if there is an Email (Webinar) within 21 days from the Sale. Finally, the list is unsplit according to Name.
You just have to replace FALSE by no and TRUE by yes afterwards.
df_split <- split(df, df$Name)
df_split <- lapply(df_split, function(tab){
i_s <- which(tab[,2]=="Sale")
tab$Email21[i_s] <- sapply(tab[i_s, 3], function(d_s){any(tab[tab$ActivityType=="Email", 3] >= d_s-21)})
tab$Webinar21[i_s] <- sapply(tab[i_s, 3], function(d_s){any(tab[tab$ActivityType=="Webinar", 3] >= d_s-21)})
tab
})
df_res <- unsplit(df_split, df$Name)
df_res
# Name ActivityType ActivityDate Email21 Webinar21
#1 John Email 2014-01-01 NA NA
#2 John Webinar 2014-01-05 NA NA
#3 John Sale 2014-01-20 TRUE TRUE
#4 John Webinar 2014-03-25 NA NA
#5 John Sale 2014-04-01 FALSE TRUE
#6 John Sale 2014-07-01 FALSE FALSE
#7 Tom Email 2015-01-01 NA NA
#8 Tom Webinar 2015-01-05 NA NA
#9 Tom Sale 2015-01-20 TRUE TRUE
#10 Tom Webinar 2015-03-25 NA NA
#11 Tom Sale 2015-04-01 FALSE TRUE
#12 Tom Sale 2015-07-01 FALSE FALSE
data
df <- structure(list(Name = c("John", "John", "John", "John", "John",
"John", "Tom", "Tom", "Tom", "Tom", "Tom", "Tom"), ActivityType = c("Email",
"Webinar", "Sale", "Webinar", "Sale", "Sale", "Email", "Webinar",
"Sale", "Webinar", "Sale", "Sale"), ActivityDate = structure(c(16071,
16075, 16090, 16154, 16161, 16252, 16436, 16440, 16455, 16519,
16526, 16617), class = "Date")), .Names = c("Name", "ActivityType",
"ActivityDate"), row.names = c(NA, -12L), index = structure(integer(0), ActivityType = c(1L,
7L, 3L, 5L, 6L, 9L, 11L, 12L, 2L, 4L, 8L, 10L)), class = "data.frame")