Do I always have to use data frames in ggplot2

Question

Dear All,

I'm running a monte-carlo simulation and the output is in the form:

> d = data.frame(iter=seq(1, 2), k1 = c(0.2, 0.6), k2=c(0.3, 0.4))
> d
iter  k1   k2
1     0.2  0.3
2     0.6  0.4

The plots I want to generate are:

plot(d$iter, d$k1)
plot(density(d$k1))

I know how to do equivalent plots using ggplot2, convert to data frame

new_d = data.frame(iter=rep(d$iter, 2), 
                   k = c(d$k1, d$k2), 
                   label = rep(c('k1', 'k2'), each=2))

then plotting is easy. However the number of iterations can be very large and the number of k's can also be large. This means messing about with a very large data frame.

Is there anyway I can avoid creating this new data frame?

Thanks

Answer 1

You can use the reshape function to transform your data frame to "long" format. May be it is a bit faster than your code ?

R> reshape(d, direction="long",varying=list(c("k1","k2")),v.names="k",times=c("k1","k2"))
     iter time   k id
1.k1    1   k1 0.2  1
2.k1    2   k1 0.6  2
1.k2    1   k2 0.3  1
2.k2    2   k2 0.4  2

Answer 2

Short answer is "no," you can't avoid creating a data frame. ggplot requires the data to be in a data frame. If you use qplot, you can give it separate vectors for x and y, but internally, it's still creating a data frame out of the parameters you pass in.

I agree with juba's suggestion -- learn to use the reshape function, or better yet the reshape package with melt/cast functions. Once you get fast with putting your data in long format, creating amazing ggplot graphs becomes one step closer!

Answer 3

So just to add to the previous answers. With qplot you could do

p <- qplot(y=d$k2, x=d$k1)

and then from there building it further, e.g. with

p + theme_bw()

But I agree - melt/cast is genereally the way forward.