Cross-correlation plot in R which has lead relationship

arvind · July 11, 2022, 10:37am

I'm trying to do a cross-correlation plot in a data frame which has multiple values on the key variable(Ship-to-party)
I'm able to get the cross correlation values for different values of the key variable using "CCF" function from feasts package.
From the correlation it clearly implies that the lead values of independent variable is correlating well with the dependent variable for all values in the Key variable(Ship-to-party).
But there is no specific function to show the lead relationship on a plot.

There is this function lag2.plot from ASTSA package which shows all lag period relationship between independent and dependent variable in a single plot.

Could someone please advise on how to do a similar plot when there is a lead relationship between independent and dependent variable?

library(tidyverse)
library(data.table)
library(tibble)
library(tibbletime)
library(lubridate)
library(tsibble)
library(ISOweek)
library(fpp3)
library(ggplot2)

Df<-structure(list(`Ship-To.Party` = c("A442", "A442", "A442", "A442", 
"A442", "A442", "A442", "A442", "A442", "A442", "A442", "A442", 
"A442", "A442", "A442", "A442", "A442", "A442", "A442", "A442", 
"A442", "A442", "A442", "A442", "A442", "A442", "A442", "A442", 
"A442", "A442", "A442", "A442", "A442", "A442", "A442", "A442", 
"A442", "A442", "A442", "A442", "A442", "A442", "A442", "A442", 
"A442", "A442", "A442", "A442", "A442", "A442", "A442", "A442", 
"A442", "A442", "A442", "A442", "A442", "A442", "A442", "A442", 
"A442", "A442", "A442", "A442", "A442", "A442", "A442", "A442", 
"A442", "A442", "A442", "A442", "A442", "A442", "A442", "A442", 
"A442", "A442", "A442", "A442", "A442", "A442", "A442", "A442", 
"A442", "A442", "A442", "A442", "A442", "A442", "A442", "A442", 
"A442", "A442", "A442", "A442", "A442", "A442", "A442", "A442", 
"A442", "A442", "A442", "A442", "A442", "A442", "A442", "A442", 
"A442", "A442", "A442", "A442", "A442", "A442", "A442", "A442", 
"A442", "A442", "A442", "A442", "A442", "A442", "A442", "A442", 
"A442", "A442", "A442", "A442", "A442", "A442", "A442", "A442", 
"A442", "A442", "A442", "A442", "A442", "A442", "A442", "A442", 
"A442", "A442", "A442", "A442", "A442", "A442", "A442", "A442", 
"A442", "A442", "A442", "A442", "A442", "A442", "A442", "A442", 
"A442", "A442", "A442", "A442", "A442", "A442", "A442", "A442", 
"A442", "C100", "C100", "C100", "C100", "C100", "C100", "C100", 
"C100", "C100", "C100", "C100", "C100", "C100", "C100", "C100", 
"C100", "C100", "C100", "C100", "C100", "C100", "C100", "C100", 
"C100", "C100", "C100", "C100", "C100", "C100", "C100", "C100", 
"C100", "C100", "C100", "C100", "C100", "C100", "C100", "C100", 
"C100", "C100", "C100", "C100", "C100", "C100", "C100", "C100", 
"C100", "C100", "C100", "C100", "C100", "C100", "C100", "C100", 
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"C100", "C100", "C100", "C100", "C100", "C100", "C100", "C100", 
"C100", "C100", "C100", "C100", "C100", "C100"), Shipment = c(29, 
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        65L, 66L, 67L, 68L, 69L, 70L, 71L, 72L, 73L, 74L, 75L, 
        76L, 77L, 78L, 79L, 80L, 81L, 82L, 83L, 84L, 85L, 86L, 
        87L, 88L, 89L, 90L, 91L, 92L, 93L, 94L, 95L, 96L, 97L, 
        98L, 99L, 100L, 101L, 102L, 103L, 104L, 105L, 106L, 107L, 
        108L, 109L, 110L, 111L, 112L, 113L, 114L, 115L, 116L, 
        117L, 118L, 119L, 120L, 121L, 122L, 123L, 124L, 125L, 
        126L, 127L, 128L, 129L, 130L, 131L, 132L, 133L, 134L, 
        135L, 136L, 137L, 138L, 139L, 140L, 141L, 142L, 143L, 
        144L, 145L, 146L, 147L, 148L, 149L, 150L, 151L, 152L, 
        153L, 154L, 155L, 156L, 157L, 158L, 159L, 160L, 161L, 
        162L, 163L, 164L, 165L, 166L, 167L, 168L, 169L, 170L, 
        171L, 172L, 173L, 174L, 175L, 176L, 177L, 178L, 179L, 
        180L, 181L, 182L, 183L, 184L, 185L, 186L, 187L, 188L, 
        189L, 190L, 191L, 192L, 193L, 194L, 195L, 196L, 197L, 
        198L, 199L, 200L, 201L, 202L, 203L, 204L, 205L, 206L, 
        207L, 208L, 209L, 210L, 211L, 212L, 213L, 214L, 215L, 
        216L, 217L, 218L, 219L, 220L, 221L, 222L, 223L, 224L, 
        225L, 226L, 227L, 228L, 229L, 230L, 231L, 232L, 233L, 
        234L, 235L, 236L, 237L, 238L, 239L, 240L, 241L, 242L, 
        243L, 244L, 245L, 246L, 247L, 248L, 249L, 250L, 251L, 
        252L, 253L, 254L, 255L, 256L, 257L, 258L, 259L, 260L, 
        261L, 262L, 263L, 264L, 265L, 266L, 267L, 268L, 269L, 
        270L, 271L, 272L, 273L, 274L, 275L, 276L, 277L, 278L, 
        279L, 280L, 281L, 282L, 283L, 284L, 285L, 286L, 287L, 
        288L, 289L, 290L, 291L, 292L, 293L, 294L, 295L, 296L, 
        297L, 298L, 299L, 300L, 301L, 302L, 303L, 304L, 305L, 
        306L, 307L, 308L, 309L, 310L, 311L, 312L, 313L, 314L, 
        315L, 316L, 317L, 318L, 319L, 320L, 321L, 322L, 323L, 
        324L, 325L, 326L, 327L, 328L, 329L, 330L), ptype = integer(0), class = c("vctrs_list_of", 
    "vctrs_vctr", "list"))), row.names = c(NA, -330L), class = c("tbl_df", 
"tbl", "data.frame"), .drop = TRUE))

CCFvalues<- Df %>% CCF(ADJUSTEDSALESUNITS,Shipment,type = "correlation")

#Plot the cross-correlation 

ccf.plot<- Final.Original.df.2 %>% filter(`Ship-To.Party` %in% c("A162")) %>%
  lag2.plot(ADJUSTEDSALESUNITS,Shipment,10)

Thank you

system · August 1, 2022, 10:38am

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