I see the other post here about this, but I'm relatively new to R so the answers weren't helpful to me. I'd really appreciate some more in-depth help with how to do this.
I've already made a plot using the commands from the Causal Impact package. In the package documentation, it clearly says that the plots are ggplot2 objects and can be customized the same way as any other object like that. I've successfully done that, adding titles and customizing colors. I need to add a legend (it's required at the journal I'm submitting to). Here is an example of what my graph currently looks like and the code I used to get there.
library(ggplot2)
devtools::install_github("google/CausalImpact")
library(CausalImpact)
## note that I took this example code from the package documentation up until I customize the plot
#create data
set.seed(1)
x1 <- 100 + arima.sim(model = list(ar = 0.999), n = 100)
y <- 1.2 * x1 + rnorm(100)
y[71:100] <- y[71:100] + 10
data <- cbind(y, x1)
#causal impact analysis
> pre.period <- c(1, 70)
> post.period <- c(71, 100)
> impact <- CausalImpact(data, pre.period, post.period)
#graph
example<-plot(impact, c("original", "cumulative")) +
labs(
x = "Time",
y = "Clicks (Millions)",
title = "Figure. Analysis of click behavior after intervention.") +
theme(plot.title = element_text(hjust = 0.5),
plot.caption = element_text(hjust = 0),
panel.background = element_rect(fill = "transparent"), # panel bg
plot.background = element_rect(fill = "transparent", color = NA), # plot bg
panel.grid.major = element_blank(), # get rid of major grid
panel.grid.minor = element_blank()) # get rid of minor grid
In my head, the solution I'd like is to have a legend for each panel of the plot. The first legend (next to the 'original' panel) would show a solid line represents the observed data, the dotted line represents the estimated counterfactual, and the colored band represents the 95% CrI around the estimated counterfactual. The second legend (next to the 'cumulative' panel) would show the dotted line represents the estimated change in trend associated with the intervention and the colored band again represents the 95% CrI around the estimation. Maybe there's a better solution than that, but that's what I've thought of.
Here is a section of the underlying code that runs when you plot:
# Initialize plot
q <- ggplot(data, aes(x = time)) + theme_bw(base_size = 15)
q <- q + xlab("") + ylab("")
if (length(metrics) > 1) {
q <- q + facet_grid(metric ~ ., scales = "free_y")
}
# Add prediction intervals
q <- q + geom_ribbon(aes(ymin = lower, ymax = upper),
data, fill = "slategray2")
# Add pre-period markers
xintercept <- CreatePeriodMarkers(impact$model$pre.period,
impact$model$post.period,
time(impact$series))
q <- q + geom_vline(xintercept = xintercept,
colour = "darkgrey", size = 0.8, linetype = "dashed")
# Add zero line to pointwise and cumulative plot
q <- q + geom_line(aes(y = baseline),
colour = "darkgrey", size = 0.8, linetype = "solid",
na.rm = TRUE)
# Add point predictions
q <- q + geom_line(aes(y = mean), data,
size = 0.6, colour = "darkblue", linetype = "dashed",
na.rm = TRUE)
# Add observed data
q <- q + geom_line(aes(y = response), size = 0.6, na.rm = TRUE)
return(q)
}
One of the answers in that older post here said that I'd have to adapt the pre-existing function to get a legend, and I don't really have the skills yet to see what I'd have to change or add. I thought that legends were supposed to be automatically added according to what's in the aes() bit of the ggplot code, so I'm a little confused why there isn't one in the first place. Can someone help me with this?
