Causal impact package example for campaign data

causalimpact.r

options(scipen = 100)
install.packages("CausalImpact")
library(CausalImpact)
##YT video on running it in R : https://www.youtube.com/watch?v=H64ucBjgY6w
View(causaldata)

#convert data to time series
campaign_mkt <- ts(causaldata$sales)
non_campaign_mkt_1 <- ts(causaldata$sales_non_campaign_mkt_1)
non_campaign_mkt_2 <- ts(causaldata$sales_non_campaign_mkt_2)

##correlation check
corrcheck <- cbind(campaign_mkt,non_campaign_mkt_1, non_campaign_mkt_2)
correlation <- window(corrcheck,start=1, end=319)
cor(correlation)

pre.period <- as.Date(c("2019-01-01" , "2019-11-20"))
post.period <- as.Date(c("2019-11-21" , "2019-12-03"))

time.point <- seq.Date(as.Date("2019-01-01"), by=1, length.out=337)

causalimpact <- zoo(cbind(campaign_mkt,non_campaign_mkt_1,non_campaign_mkt_2),time.point)

impact <- CausalImpact(causalimpact,pre.period, post.period)
plot(impact)
summary(impact)
summary(impact,"report")