First let's generate two data series y1 and y2 and plot them with the traditional points methods
x <- seq(0, 4 * pi, 0.1) n <- length(x) y1 <- 0.5 * runif(n) + sin(x) y2 <- 0.5 * runif(n) + cos(x) - sin(x) plot(x, y1, col = "blue", pch = 20) points(x, y2, col = "red", pch = 20)
This is exactly the R code that produced the above plot. It is just a simple plot and points functions to plot multiple data series. It is not really the greatest, smart looking R code you want to use. Better plots can be done in R with ggplot.
Plotting with Ggplot2
Now, let's try this with ggplot2.
First we need to create a data.frame with our series.
If we have very few series we can just plot adding geom_point as needed.
library(ggplot2) df <- data.frame(x, y1, y2) ggplot(df, aes(x, y = value, color = variable)) + geom_point(aes(y = y1, col = "y1")) + geom_point(aes(y = y2, col = "y2"))
But if we have many series to plot an alternative is using melt to reshape the data.frame and with this plot an arbitrary number of rows. For example:
library(reshape) # This creates a new data frame with columns x, variable and value # x is the id, variable holds each of our timeseries designation df.melted <- melt(df, id = "x") ggplot(data = df.melted, aes(x = x, y = value, color = variable)) + geom_point()
And thats how to plot multiple data series using ggplot. The basic trick is that you need to melt your data into a new data.frame. Remember, in data.frames each row represents an observation.
Another option, pointed to me in the comments by Cosmin Saveanu (Thanks!), it to plot the multiple data series with facets (good for B&W):
library(reshape) ggplot(data = df.melted, aes(x = x, y = value)) + geom_point() + facet_grid(variable ~ .)