Inserting rows into a dataframe based on a vector that contains dates - r

Question

This is what my dataframe looks like:
df <- read.table(text='
Name ActivityType ActivityDate
John Email 2014-01-01
John Webinar 2014-01-05
John Webinar 2014-01-20
John Email 2014-04-20
Tom Email 2014-01-01
Tom Webinar 2014-01-05
Tom Webinar 2014-01-20
Tom Email 2014-04-20
', header=T, row.names = NULL)
I have this vector x which contains different dates
x<- c("2014-01-03","2014-01-25","2015-05-27"). I want to insert rows in my original dataframe in a way that incorporates these dates in the x vector.This is what the output should look like:
Name ActivityType ActivityDate
John Email 2014-01-01
John NA 2014-01-03
John Webinar 2014-01-05
John Webinar 2014-01-20
John NA 2014-01-25
John Email 2014-04-20
John NA 2015-05-27
Tom Email 2014-01-01
Tom NA 2014-01-03
Tom Webinar 2014-01-05
Tom Webinar 2014-01-20
Tom NA 2014-01-25
Tom Email 2014-04-20
Tom NA 2015-05-27
Sincerely appreciate your help!

Answer 1

It looks like you've added one of the 'new' dates aginst each of the people, correct?
In which case you can turn your x into a data.frame, and merge/join it on
## original dataframe
df <- data.frame(Name = c(rep("John", 4), rep("Tom", 4)),
ActivityType = c("Email","Web","Web","Email","Email","Web","Web", "Email"),
ActivityDate = c("2014-01-01","2014-05-01","2014-20-01","2014-20-04","2014-01-01","2014-05-01","2014-20-01","2014-20-04"))
## Turning x into a dataframe.
x <- data.frame(ActivityDate = rep(c("2014-01-03","2014-01-25","2015-05-27"), 2),
Name = rep(c("John","Tom"), 3))
merge(df, x, by=c("Name", "ActivityDate"), all=T)
# Name ActivityDate ActivityType
# 1 John 2014-01-01 Email
# 2 John 2014-05-01 Web
# 3 John 2014-20-01 Web
# 4 John 2014-20-04 Email
# 5 John 2014-01-03 <NA>
# 6 John 2014-01-25 <NA>
# 7 John 2015-05-27 <NA>
# 8 Tom 2014-01-01 Email
# 9 Tom 2014-05-01 Web
# 10 Tom 2014-20-01 Web
# 11 Tom 2014-20-04 Email
# 12 Tom 2014-01-03 <NA>
# 13 Tom 2014-01-25 <NA>
# 14 Tom 2015-05-27 <NA>
Update
As you are having memory issues, you can use data.table thusly
library(data.table)
dt <- as.data.table(df)
x_dt <- as.data.table(x)
merge(dt, x_dt, by=c("Name","ActivityDate"), all=T)
or, if you're not looking to merge you can rbind them, using data.table's rbindlist
rbindlist(list(dt, x_dt), fill=TRUE) ## fill sets the 'ActivityType' to NA in X
Update 2
To generate your x with 16000 uniqe names (I've used numbers here, but the principle is the same) and 30 dates
ActivityDates <- seq(as.Date("2014-01-01"), as.Date("2014-01-31"), by=1)
Names <- seq(1,16000)
x <- data.frame(Names = rep(Names, length(ActivityDates)),
ActivityDates = rep(ActivityDates, length(Names)))

Answer 2

1) expand.grid Using expand.grid create a data frame adds with the rows to be added and then use rbind to combine df and adds converting the ActivityDate column to "Date" class. Then sort. No packages are used.
adds <- expand.grid(Name = levels(df$Name), ActivityType = NA, ActivityDate = x)
both <- transform(rbind(df, adds), ActivityDate = as.Date(ActivityDate))
o <- with(both, order(Name, ActivityDate))
both[o, ]
giving:
Name ActivityType ActivityDate
1 John Email 2014-01-01
9 John <NA> 2014-01-03
2 John Webinar 2014-01-05
3 John Webinar 2014-01-20
11 John <NA> 2014-01-25
4 John Email 2014-04-20
13 John <NA> 2015-05-27
5 Tom Email 2014-01-01
10 Tom <NA> 2014-01-03
6 Tom Webinar 2014-01-05
7 Tom Webinar 2014-01-20
12 Tom <NA> 2014-01-25
8 Tom Email 2014-04-20
14 Tom <NA> 2015-05-27
2) sqldf This uploads adds and df to an sqlite data base which it creates on the fly, then it performs the sql query and downloads the result. The computation occurs outside of R so it might work with your large data.
adds <- data.frame(Name = NA, ActivityDate = x)
library(sqldf)
sqldf("select *
from (select *
from df
union
select a.Name, NULL ActivityType, ActivityDate
from (select distinct Name from df) a
cross join adds b
) order by 1, 3"
)
giving:
Name ActivityType ActivityDate
1 John Email 2014-01-01
2 John <NA> 2014-01-03
3 John Webinar 2014-01-05
4 John Webinar 2014-01-20
5 John <NA> 2014-01-25
6 John Email 2014-04-20
7 John <NA> 2015-05-27
8 Tom Email 2014-01-01
9 Tom <NA> 2014-01-03
10 Tom Webinar 2014-01-05
11 Tom Webinar 2014-01-20
12 Tom <NA> 2014-01-25
13 Tom Email 2014-04-20
14 Tom <NA> 2015-05-27