How to speed up code with loop in R - r

Question

Problem:
I have two data frames.
DF with payment log:
Classes ‘tbl_df’, ‘tbl’ and 'data.frame': 53682 obs. of 7 variables:
str(moneyDB)
$ user_id : num 59017170 57859746 58507536 59017667 59017795 ...
$ reg_date: Date, format: "2016-08-06" "2016-07-01" "2016-07-19" ...
$ date : Date, format: "2016-08-06" "2016-07-01" "2016-07-19" ...
$ money : num 0.293 0.05 0.03 0.03 7 ...
$ type : chr "1" "2" "2" "1" ...
$ quality : chr "VG" "no_quality" "no_quality" "VG" ...
$ geo : chr "Canada" "NO GEO" "NO GEO" "Canada" ...
Here is its structure. Its just a log of all transactions.
Also i have second data frame:
str(grPaysDB)
Classes ‘tbl_df’, ‘tbl’ and 'data.frame': 335591 obs. of 9 variables:
$ reg_date : Date, format: "2016-05-01" "2016-05-01" "2016-05-01" ...
$ date : Date, format: "2016-05-01" "2016-05-01" "2016-05-01" ...
$ type : chr "1" "1" "1" "1" ...
$ quality : chr "VG" "VG" "VG" "VG" ...
$ geo : chr "Australia" "Canada" "Finland" "Canada" ...
$ uniqPayers : num 0 1 0 1 1 0 0 1 0 3 ...
Its Grouped data from first data frame + zero transactions. For example, there is a lot of rows in second data frame with zero payers. Thats why second data frame is greater then first.
I need to add column weeklyPayers to the second data frames. Weekly payers is sum unique payers for the last 7 days. I tried do it via loop, but it wooks too long. Is there any another vectorized ideas, how to realise this?
weeklyPayers <- vector()
for (i in 1:nrow(grPaysDB)) {
temp <- moneyDB %>%
filter(
geo == grPaysDB$geo[i],
reg_date == grPaysDB$reg_date[i],
quality == grPaysDB$quality[i],
type == grPaysDB$type[i],
between(date, grPaysDB$date[i] - 6, grPaysDB$date[i])
)
weeklyPayers <- c(weeklyPayers, length(unique(temp$user_id)))
}
grPaysDB <- cbind(grPaysDB, weeklyPayers)
In this loop for each row in second data frame i find rows in first data frame with right geo,type, quality and reg_date and range of dates. And then I can calculate number of unique payers.

Answer 1

I may be misunderstanding, but I think this should be fairly simple, using filter and summarise in dplyr. However, as #Hack-R mentioned, it would be helpful to have your dataset. But it would look something like:
library(dplyr)
weeklyPayers <- grPaysDB %>%
filter(date > ADD DATE IN QUESTION) %>%
summarise(sumWeeklyPayers = sum(uniqPayers))
Then again, I may well have misunderstood. If your question involves summing for each week, then you may want to investigate daily2weekly in the timeSeries package and then using group_by for the weekly variable that transpires.

Answer 2

I would try making a join on your datasets using merge on multiple columns (c('geo', 'reg_date', 'quality', 'type') and filter the result based on the dates. After that, aggregate using summarise.
But I am not completely sure why you want to add the weeklypayers to every transaction. Isn't it more informative or easier to aggregate your data on week number (with dplyr). Like so:
moneyDB %>% mutate(week = date- as.POSIXlt(date)$wday) %>%
group_by(geo, reg_date, quality, type, week) %>%
summarise(weeklyPayers = n())