I have 2 datasets, called A and B. I want to compare the distribution of one common variable, called k, showing up in both dataset, but of different lengths (A contains 2000 values of k, while B has 1000, both have some N/A). So I would like to plot the distribution of A$k anf B$k in the same plot.
I have tried:
g1 <- ggplot(A, aes(x=A$k)) + geom_density()
g2 <- ggplot(B, aes(x=B$k)) + geom_density()
g <- g1 + g2
But then comes the error:
Don't know how to add o to a plot.
How can I overcome this problem?
Since we dont have any data it is hard to provide a specific solution that meets your scenario. But below is a general principle of what I think you trying to do.
The trick is to put your data together and have another column that identifies group A and group B. This is then used in the aes() argument in ggplot. Bearing in mind that combining your data frames might not be as simple as what I have done since you might have some extra columns etc.
# generating some pseudo data from a poisson distribution
A <- data.frame(k = rpois(2000, 4))
B <- data.frame(k = rpois(1000, 7))
# Create identifier
A$id <- "A"
B$id <- "B"
A_B <- rbind(A, B)
g <- ggplot(data = A_B, aes(x = k,
group = id, colour = id, fill = id)) + # fill/colour aes is not required
geom_density(alpha = 0.6) # alpha for some special effects
g