Can I avoid using data frames in ggplot2?

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 开发者_如何学GoI 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

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!

Yes, it is possible for you to avoid creating a data frame: just give an empty argument list to the base layer, ggplot(). Here is a complete example based on your code:

library(ggplot2)

d = data.frame(iter=seq(1, 2), k1 = c(0.2, 0.6), k2=c(0.3, 0.4))
# desired plots:
# plot(d$iter, d$k1)
# plot(density(d$k1))

ggplot() + geom_point(aes(x = d$iter, y = d$k1))
# there is not enough data for a good density plot,
# but this is how you would do it:
ggplot() + geom_density(aes(d$k1))

Note that although this allows for you not to create a data frame, a data frame might still be created internally. See, e.g., the following extract from ?geom_point:

All objects will be fortified to produce a data frame.

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

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.

Just pass NULL as the data frame, and define the necessary aesthetics using the data vectors. Quick example:

library(MASS)
library(tidyverse)
library(ranger)

rf <- ranger(medv ~ ., data = Boston, importance = "impurity")

rf$variable.importance

ggplot(NULL, aes(x = fct_reorder(names(rf$variable.importance), rf$variable.importance),
                 y = rf$variable.importance)) +
    geom_col(fill = "navy blue", alpha = 0.7) +
    coord_flip() +
    labs(x = "Predictor", y = "Importance", title = "Random Forest") +
    theme_bw()