#Ggplot histograms how to
#Ggplot histograms plus

We can add colour by exploiting the way that ggplot2 stacks colour for different groups. Time to jazz it up with colour! The method I’ll present was motivated by my answer to this StackOverflow question. Let’s simulate data for a continuous variable x in a data frame d: set.seed(070510)Ĭreate the basic ggplot2 histogram via: library(ggplot2)

#Ggplot histograms how to

In this post you’ll learn how to create histograms like this: Right now, it may seem like a lot of typing to accomplish what, in base graphics, could be done with much less typing, but as the graphs get more complicated, the ggplot syntax becomes much, much quicker to write.Pretty histograms with here to make pretty histograms with ggplot2! And using ggplot, we can quickly and easily build 'em up. They give you a sense of how spread out the data is. Both the histogram and the density plot are very useful tools for examining a single variable. Running this, now gives us a nice filled-in density plot. We want to fill in the color underneath the line, so we use fill, and this can take on a number of different values. You might be tempted to use the color argument, but that, in actuality, would just control the color of the line. So let's copy this line, paste it in, and in geom_density, let's add another argument. And it's only a line, doesn't really show that much, so it might be helpful if we fill in the color. Again, the aesthetic mapping will still be the x axis getting carat, and we won't worry about bin width just now. It started in much the same way, with ggplot(data=diamonds), and then instead of geom_histogram, we will do geom underscore density. A density is sort of a more continuous version of a histogram. Let's clear out the console and look at a similar graph, the density plot. Too big of one, the data's too smoothed out and you don't get any information either. Too small of one, and the data's too noisy, doesn't really show you anything. It is important when making a histogram to get the bin width just right. This became much noisier, lots of peaks, lots of valleys.

So I'll copy this line again and this time I'll make a plot that has a bin width of point one and see what that looks like. Running that, we get a plot that's much blockier, there is much less information, much less variation. Right now, I'll set that to point five and see what we get. Inside geom_histogram, but not inside aes, going to make a new argument: binwidth. So I'm gonna take this line of code, copy it, and paste it on the next line. We can adjust this bin width manually using some options that go into the geom histogram function. Playing with that can make a big difference. That right now is being done by default by taking the maximum x value minus the minimum x value and dividing by 30. How big those buckets are is a variable you could tune. What that means is a histogram has to break up the x axis into discrete buckets. We also got a little message saying that the bin width defaulted to the range of the data divided by 30. We close off that aes and we close off geom_histogram, and now we can run the plot. We do that using aes, which is its own function, and say x equals carat. Now, since we haven't specified any aesthetic mappings yet, we need to tell ggplot what variable is going to be mapped to the x axis.

#Ggplot histograms plus

Then we're going to add the histogram layer so that is plus geom underscore histogram. That's the very first thing we're going to do. These graphics are initialized with a call to the ggplot function, so that is ggplot and then we say data equals diamonds. So again, we will look at the carat variable of the diamonds data set.

Much like with base graphics, ggplot offers an easy way to make histograms.