Help Specifying lag value in lag2.plot and generating plot using multiple groups for two variable series data

[arvind]

Hi,

This is two part query regarding the Correlation plot

Note- I use astsa package to generate cross correlation plot for the two variable series. The two series data has multiple groups

1. Is it possible to specify a lag value in the lag2.plot function because the positive lag values are not available on the plot if I use lag2.plot
2. My data has two series but it has been grouped by one Key variable which has multiple groups- Is it possible to generate a CCF plot for multiple groups in a single shot rather than generating it for one group at a time

Attached my reproducible code

library(tidyverse)
library(lubridate)
library(tibble)
library(tsibble)
library(norm)
library(fpp3)
library(norm)
library(ISOweek)
library(tseries)
library(astsa)
Df<-structure(list(Week = c("201901", "201901", "201901", "201902", 
"201902", "201902", "201903", "201903", "201903", "201904", "201904", 
"201904", "201905", "201905", "201905", "201906", "201906", "201906", 
"201907", "201907", "201907", "201908", "201908", "201908", "201909", 
"201909", "201909", "201910", "201910", "201910", "201911", "201911", 
"201911", "201912", "201912", "201912", "201913", "201913", "201913"
), Key = c("A", "B", "C", "A", "B", "C", "A", "B", "C", "A", 
"B", "C", "A", "B", "C", "A", "B", "C", "A", "B", "C", "A", "B", 
"C", "A", "B", "C", "A", "B", "C", "A", "B", "C", "A", "B", "C", 
"A", "B", "C"), Dependent.Variable = c(19, 11, 11, 98, 127, 44, 
135, 148, 34, 112, 108, 39, 77, 141, 50, 89, 151, 41, 86, 120, 
70, 149, 102, 62, 89, 94, 60, 146, 128, 66, 91, 119, 106, 160, 
132, 56, 143, 152, 75), Independent.Variable = c(794, 794, 794, 
1400, 1400, 1400, 1505, 1505, 1505, 1055, 1055, 1055, 1396, 1396, 
1396, 1331, 1331, 1331, 1461, 1461, 1461, 1623, 1623, 1623, 1513, 
1513, 1513, 1667, 1667, 1667, 1737, 1737, 1737, 1264, 1264, 1264, 
1722, 1722, 1722)), row.names = c(NA, 39L), class = "data.frame")

#Converting to Week format
Df <- Df %>%
  mutate(
    isoweek =stringr::str_replace(Week, "^(\\d{4})(\\d{2})$", "\\1-W\\2-1"),
    date = ISOweek::ISOweek2date(isoweek)
  )

Df <- Df %>% 
  mutate(Week.1 = yearweek(ISOweek::ISOweek(date))) %>% 
  group_by(`Key`,Week.1) %>%
  dplyr::select(-Week,-date,-isoweek) %>%
  as_tsibble(key = `Key`,index = Week.1)

has_gaps(Df,.full = TRUE)

#Generating Cross-Correlation values
ccfvalues<- Df %>%
  CCF(Independent.Variable,Dependent.Variable,type = "correlation")

#Plot the CCF values using astsa package
Plot<-Df %>% group_by(`Key`) %>%  filter(Key == 'A') %>%
  lag2.plot(Independent.Variable,Dependent.Variable)