For example, if you use text, you can change every text element if you use title, you would change only the title text elements (plot/axes/legend titles) if you used axis.title, that would only change the axes titles, whereas would only change the x axis title, and so on!įor most arguments in theme(), you specify what kind of element you are changing ( element_line(), element_rect(), element_text(), or element_blank() to remove something). You can probably get a sense for what many of those might do, and as you can see there are often several versions so you can either apply a change to everything of that type or specific elements. This has a LOT of arguments that you can change here is the list from the documentation. The things changed within these built in themes can be accessed through the theme() function. These impact the colour of the plot, thickness and existence of gridlines, font details, legend position and more. There are several themes built into ggplot2, so you can easily use one of those to quickly achieve a look you are happy with. Scale_fill_discrete(labels = c("Adult", "Infant")) Mapping = aes(x = forcats::fct_infreq(Species), I’m also going to assign this to an object now so I can work on it without having to write the same lines repetitively. Bear in mind that if you have the forcats packaged loaded, you don’t need to specific the package in the function call, which makes it look a little less busy. Note that we also have to add in a label for the x axis otherwise we would get the unsightly forcats::fct_infreq(Species) as the label. The change comes in defining the x argument in aes(). The alternative is to transform your original data so your variable is a factor ordered by count, which is a bit cumbersome if the only reason you are doing it is for one plot. This is a quick and easy way using the forcats package, which doesn’t change your original data. This would be easier to interpret if the species were ordered by count (which would be more true if you had lots of species to compare). Scale_fill_discrete(labels = c("Adult", "Infant")) + Labs(y = "Count", title = "Number of hawks by species") + ggplot(data = hawks, mapping = aes(x = Species, fill = Age)) + The labels argument changes the labels.įor illustrative purposes, I’ve also used scale_y_continuous to change where the tick marks land (every 150 hawks) and spell out the labels. So here, we can change the labels on the legend with scale_fill_discrete(), because it relates to the fill aesthetic and it’s a categorical variable.
#GGPLOT RENAME X TICKS MANUAL#
The first part is scale, the second part is the aesthetic you are changing and the third part is whether it is discrete, continuous or manual (i.e. specified by you).
There are a group of functions that cover these that all start with scale_*(). These are to do with the legend category labels and the labels on the axes ticks. However, there’s still text on there that we haven’t changed.
There are other functions you can use that are more specialist, like xlab() and ylab() for the axes and ggtitle() for the title, but obviously those only cover those specific labels. Subtitle = "Red-tailed hawks were counted most often",
Here’s an example with all of those things added. I like to use this function because you can use it to change the title, subtitle, caption or tags as well as the axes labels or other aesthetics. Starting with the labs() function, we can change a lot of the text.
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