Error while Plotting the Cross correlation values using astsa package in R

[arvind]

Hi All,

The dataset I have is a weekly data with one dependent and Independent variable with One grouping Variable(which is the Key variable).
I'm able to get the cross-correlation values between Dep. & Ind. Variables based on the grouping variable using the CCF function.
But for some reason when I try to plot the CCF values i get the below error
Error: Can't determine column value: Dependent.Variable, Independent.Variable.
I have used lag.2 plot function from the astsa package for plotting the CCF values.
Please advise where I'm doing it wrong

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`) %>%
  lag2.plot(Independent.Variable,Dependent.Variable)

Thank you

[gsapijaszko]

Simply reassign instead of piping

Plot <- Df %>% group_by(`Key`)
Plot <- lag2.plot(Plot$Independent.Variable,Plot$Dependent.Variable)

Regards,
Grzegorz

[arvind]

@gsapijaszko
But I want the cross correlation plot grouped by Key which I couldn't see so.
If you look at the ccfvalues object's output it tends to have the correct correlation output by Key variable which is what i'm expecting to see on the plot.

Is there a way we can see the correlation plot by grouping variable(Key)?

Thank you