left_join with individual lag to new column - r

Question

I need to merge two data frames probably with left_join, offset the joining observation by a specific amount and add it to a new column. The purpose is the preparation of a time-series analysis hence the different shifts in calendar weeks. I would like to stay in the tidyverse.
I read a few posts with a nested left-join() and lag() but that's beyond my current capability.
MWE
library(tidyverse)
set.seed(1234)
df1 <- data.frame(
Week1 = sample(paste("2015", 20:40, sep = "."),10, replace = FALSE),
Qty = as.numeric(sample(1:10)))
df2 <- data.frame(
Week2 = paste0("2015.", 1:52),
Value = as.numeric(sample(1:52)))
df1 %>%
left_join(df2, by = c("Week1" = "Week2")) %>%
rename(Lag_0 = Value)
Current output
+----+---------+-------+-------+
| | Week1 | Qty | Lag_0 |
+====+=========+=======+=======+
| 1 | 2015.35 | 6.00 | 50.00 |
+----+---------+-------+-------+
| 2 | 2015.24 | 10.00 | 26.00 |
+----+---------+-------+-------+
| 3 | 2015.31 | 7.00 | 43.00 |
+----+---------+-------+-------+
| 4 | 2015.34 | 9.00 | 42.00 |
+----+---------+-------+-------+
| 5 | 2015.28 | 4.00 | 10.00 |
+----+---------+-------+-------+
| 6 | 2015.39 | 8.00 | 24.00 |
+----+---------+-------+-------+
| 7 | 2015.25 | 5.00 | 33.00 |
+----+---------+-------+-------+
| 8 | 2015.23 | 1.00 | 39.00 |
+----+---------+-------+-------+
| 9 | 2015.21 | 2.00 | 17.00 |
+----+---------+-------+-------+
| 10 | 2015.26 | 3.00 | 27.00 |
+----+---------+-------+-------+
It might be worthwhile pointing out that the target data frame does not hold the same amount of week observations as the joining data frame.
Desired output
+----+---------+-------+-------+-------+-------+--------+
| | Week1 | Qty | Lag_0 | Lag_3 | Lag_6 | Lag_12 |
+====+=========+=======+=======+=======+=======+========+
| 1 | 2015.35 | 6.00 | 50.00 | 9.00 | | |
+----+---------+-------+-------+-------+-------+--------+
| 2 | 2015.24 | 10.00 | 26.00 | 17.00 | | |
+----+---------+-------+-------+-------+-------+--------+
| 3 | 2015.31 | 7.00 | 43.00 | 10.00 | | |
+----+---------+-------+-------+-------+-------+--------+
| 4 | 2015.34 | 9.00 | 42.00 | 43.00 | | |
+----+---------+-------+-------+-------+-------+--------+
| 5 | 2015.28 | 4.00 | 10.00 | 33.00 | | |
+----+---------+-------+-------+-------+-------+--------+
| 6 | 2015.39 | 8.00 | 24.00 | 13.00 | | |
+----+---------+-------+-------+-------+-------+--------+
| 7 | 2015.25 | 5.00 | 33.00 | 25.00 | | |
+----+---------+-------+-------+-------+-------+--------+
| 8 | 2015.23 | 1.00 | 39.00 | 38.00 | | |
+----+---------+-------+-------+-------+-------+--------+
| 9 | 2015.21 | 2.00 | 17.00 | 6.00 | | |
+----+---------+-------+-------+-------+-------+--------+
| 10 | 2015.26 | 3.00 | 27.00 | 39.00 | | |
+----+---------+-------+-------+-------+-------+--------+
Column Lag_3, which I added manually, contains the values from the matching df2 week value but offset by three rows. Lag_6 would be offset by six rows, etc.
I suppose the challenge is, that the lag() would have to happen in the joining table after the matching but before the returning of the value.
Hope this makes sense and thanks for the assistance.

Answer 1

We just need to create the lag before in the second data and then do the join
library(dplyr)
df2 %>%
mutate(Lag_3 = lag(Value, 3), Lag_6 = lag(Value, 6)) %>%
left_join(df1, ., by = c("Week1" = "Week2")) %>%
rename(Lag_0 = Value)
-output
# Week1 Qty Lag_0 Lag_3 Lag_6
#1 2015.35 6 50 9 46
#2 2015.24 10 26 17 6
#3 2015.31 7 43 10 33
#4 2015.34 9 42 43 10
#5 2015.28 4 10 33 25
#6 2015.39 8 24 13 16
#7 2015.25 5 33 25 49
#8 2015.23 1 39 38 15
#9 2015.21 2 17 6 32
#10 2015.26 3 27 39 38