R – Plot probability with ggplot2 (not density)

ggplot2r

I'd like to plot data such that on y axis there would be probability (in range [0,1]) and on x-axis I have the data values. The data is contiguous (also in range [0,1]), thus I'd like to use some kernel density estimation function and normalize it such that the y-value at some point x would mean the probability of seeing value x in input data.

So, I'd like to ask:

a) Is it reasonable at all? I understand that I cannot have probability of seeing values I do not have in the data, but I just would like to interpolate between points I have using a kernel density estimation function and normalize it afterwards.

b) Are there any built-in options in ggplot I could use, that would override default behavior of geom_density() for example for doing this?

Thanks in advance,

Timo

EDIT:
when i said "normalize" before, I actually meant "scale". But I got the answer, so thanks guys for clearing up my mind about this.

Best Answer

Just making up a quick merge of @JD Long's and @yesterday's answers:

ggplot(df, aes(x=x)) +
  geom_histogram(aes(y = ..density..), binwidth=density(df$x)$bw) +
  geom_density(fill="red", alpha = 0.2) +
  theme_bw() +
  xlab('') +
  ylab('')

enter image description here

This way the binwidth for ggplot2 was calculated by the density function, and also the latter is drawn on the top of a histogram with a nice transparency. But you should definitely look into stat_densitiy as @yesterday suggested for further customization.

Any ggplots side-by-side (or n plots on a grid)

The function grid.arrange() in the gridExtra package will combine multiple plots; this is how you put two side by side.

require(gridExtra)
plot1 <- qplot(1)
plot2 <- qplot(1)
grid.arrange(plot1, plot2, ncol=2)

This is useful when the two plots are not based on the same data, for example if you want to plot different variables without using reshape().

This will plot the output as a side effect. To print the side effect to a file, specify a device driver (such as pdf, png, etc), e.g.

pdf("foo.pdf")
grid.arrange(plot1, plot2)
dev.off()

or, use arrangeGrob() in combination with ggsave(),

ggsave("foo.pdf", arrangeGrob(plot1, plot2))

This is the equivalent of making two distinct plots using par(mfrow = c(1,2)). This not only saves time arranging data, it is necessary when you want two dissimilar plots.

Appendix: Using Facets

Facets are helpful for making similar plots for different groups. This is pointed out below in many answers below, but I want to highlight this approach with examples equivalent to the above plots.

mydata <- data.frame(myGroup = c('a', 'b'), myX = c(1,1))

qplot(data = mydata, 
    x = myX, 
    facets = ~myGroup)

ggplot(data = mydata) + 
    geom_bar(aes(myX)) + 
    facet_wrap(~myGroup)

Update

the plot_grid function in the cowplot is worth checking out as an alternative to grid.arrange. See the answer by @claus-wilke below and this vignette for an equivalent approach; but the function allows finer controls on plot location and size, based on this vignette.

Rotating and spacing axis labels in ggplot2

Change the last line to

q + theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1))

By default, the axes are aligned at the center of the text, even when rotated. When you rotate +/- 90 degrees, you usually want it to be aligned at the edge instead:

alt text

The image above is from this blog post.

Best Answer

Related Solutions

R – Side-by-side plots with ggplot2

Any ggplots side-by-side (or n plots on a grid)

Appendix: Using Facets

Update

Rotating and spacing axis labels in ggplot2

Related Topic