My dataframe looks like this. The two rightmost columns are my desired columns.
**Name ActivityType ActivityDate Email(last 21 says) Webinar(last
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")