In this situation it’s useful to offset the text a little, which you can do with the nudge_x and nudge_y parameters: Often you want to label existing points on the plot, but you don’t want the text to overlap with the points (or bars etc). Unlike the aesthetics these only take single values, so they must be the same for all labels: In addition to the various aesthetics, geom_text() has three parameters that you can specify. The ggplot2 package does allow you to map data values to the aesthetics used by geom_text(), but you should use restraint: it is hard to perceive the relationship between variables mapped to these aesthetics, and rarely useful to do so. (There are 72.27 pts in a inch, so to convert from points to mm, just multiply by 25.4 / 72.27).Īngle specifies the rotation of the text in degrees. The reason for this choice is that it makes it the units for font sizes consistent with how other sizes are specified in ggplot2. Unlike most tools, ggplot2 specifies the size in millimeters (mm), rather than the usual points (pts). The font size is controlled by the size aesthetic. The fontface aesthetic specifies the face, and can take three values: “plain” (the default), “bold” or “italic”. Showtext,, by Yixuan Qiu, makes GD-independent plots by rendering all text as polygons.Įxtrafont,, by Winston Chang, converts fonts to a standard format that all devices can use.īoth approaches have pros and cons, so you will to need to try both of them and see which works best for your needs. Two packages simplify the quandary a bit: Unfortunately, the devices do not specify fonts in the same way so if you want a font to work everywhere you need to configure the devices in different ways. There are two groups of GDs: screen devices such as windows() (for Windows), quartz() (for Macs), x11() (mostly for Linux) and RStudioGD() (within RStudio) draw the plot to the screen, whereas file devices such as png() and pdf() write the plot to a file. The reason that it can be tricky to use system fonts in a plot is that text drawing is handled differently by each graphics device (GD). There are only three fonts that are guaranteed to work everywhere: “sans” (the default), “serif”, or “mono”.

This aesthetic does allow you to use the name of a system font, but some care is required. The family aesthetic provides the name of a font. geom_text() has the most aesthetics of any geom, because there are so many ways to control the appearance of a text: The main tool for labelling plots is geom_text(), which adds label text at the specified x and y positions. The ggplot2 package doesn’t have all the answers, but it does provide some tools to make your life a little easier. However, text annotation can be tricky due to the way that R handles fonts. Most plots will not benefit from adding text to every single observation on the plot, but labelling outliers and other important points is very useful. To learn more about how labs() is related to scales in ggplot2, see Section 14.2.Īdding text to a plot is one of the most common forms of annotation.

Setting labs(x = '') omits the label but still allocates space setting labs(x = NULL) removes the label and its space. There are two ways to remove the axis label. To assist with this task ggplot2 provides the labs() helper function, which lets you set the various titles using name-value pairs like title = My plot title', x = 'X axis' or fill = 'fill legend': When customising a plot, it is often useful to modify the titles associated with the plot, axes, and legends. However, to meet the specific needs that users often have when annotating plots, there are some helper functions in ggplot2 itself, and a number of other packages have extended ggplot2 in ways you may find helpful. Because of this, the annotation tools in ggplot2 reuse the same geoms that are used to create other plots. From a practical standpoint, however, metadata is just another form of data. Conceptually, an annotation supplies metadata for the plot: that is, it provides additional information about the data being displayed. When constructing a data visualisation, it is often necessary to make annotations to the data displayed. This chapter should be readable but is currently undergoing final polishing.

You are reading the work-in-progress third edition of the ggplot2 book.

R ggplot plot rename x values