I want to plot multiple time-series variables in the same plot so that I can see how the time series lags line up. I will have a few different plots for different groups of data, and each of those plots will represent a different group of data, although each plot will have variables with the same name.

I could do it individually, one variable at a time. But I have like a hundred variables.

Here is an example of my dataset where lag is the lag point (from the ccf()) and each var is the ACF of a different variable at that lag point.

sampledf <- data.frame(
  lag = -10:10,
  var1 = rnorm(21),
  var2 = rnorm(21),
  var3 = rnorm(21)
)

Now I could plot them fairly easily like this:

plot(sampledf$lag,
     sampledf$var1, 
     type = "l",
     col = 1,
     xlab = "Lag",
     ylab = "ACF")
lines(sampledf$lag,
     sampledf$var2,
      type = "l",
      col = 2)
lines(sampledf$lag,
     sampledf$var3,
      type = "l",
      col = 3)
legend("topright",                          
       c("Var1", "Var2", "Var3"),
       lty = 1,
       col = 1:3)

But then I am doing each variable manually. And if I wanted to view the correlations in a different way-for example, one plot with var1 from each of sampledf1, sampledf2, through sampledf20, I would have to start all over.

Is there a way to automate this in fewer lines of code? This is just beyond my level of R programming but I realise this probably has something to do with functions and things (R is mainly a "stats" tool in my work).

I'm also open to different functions to view cross-correlations in different ways if there's a completely different (yet easier) way to achieve this.

You will likely very quickly run out of colors, but you can do this:

base R

plot(sampledf$lag,
     sampledf$var1, 
     type = "n",
     col = 1,
     xlab = "Lag",
     ylab = "ACF")
Map(function(y, col) lines(sampledf$lag, y, col=col, type="l"),
    sampledf[,-1], seq_len(ncol(sampledf)-1))
legend("topright",                          
       c("Var1", "Var2", "Var3"),
       lty = 1,
       col = 1:3)

ggplot2

This works much better in a "long" format.

library(ggplot2)
tidyr::pivot_longer(sampledf, -lag) |>
  ggplot(aes(lag, value, color = name, group = name)) +
  geom_line()