Ggplot annotate outside plot

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Annotate Text Outside of ggplot2 Plot in R

Ggplot2 is based on the grammar of graphics, the idea that you can build every graph from the same few components: a data set, a set of geoms—visual marks that represent data points, and a coordinate system. There are many scenarios where we need to annotate outside the plot area or specific area as per client requirements. In this case, the ggplot2 library comes very handy with its sub-options to get the required output and with good customization options for data visualizations. 

To add annotations in R using ggplot2, annotate() function is used.

Syntax: annotate() 

Parameters:

  • geom : specify text
  • x : x axis location
  • y : y axis location
  • label : custom textual content
  • color : color of textual content
  • size : size of text
  • fontface : fontface of text
  • angle : angle of text

Approach

  • Import library
  • Create or load dataset
  • Create a normal plot
  • Add annotate() function with required parameters

Let us first see how annotations are added inside the plot, so that the difference in position of the annotations can be understood better.



Examples : 

R

 

 

Output:

Now let us visualize with annotations outside the plot.

Example:

R

 

 

Output:




Sours: https://www.geeksforgeeks.org/annotate-text-outside-of-ggplot2-plot-in-r/

Annotations

When constructing a data visualisation, it is often necessary to make annotations to the data displayed. Conceptually, an annotation supplies metadata for the plot: that is, it provides additional information about the data being displayed. From a practical standpoint, however, metadata is just another form of data. Because of this, the annotation tools in ggplot2 reuse the same geoms that are used to create other plots. 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.

Plot and axis titles

When customising a plot, it is often useful to modify the titles associated with the plot, axes, and legends. To assist with this task ggplot2 provides the helper function, which lets you set the various titles using name-value pairs like , or :

The values supplied to are typically text strings, with used to specify line breaks, but you can also supply mathematical expressions wrapped in . The rules by which these expressions are interpreted can be found by typing .

It is also possible to include (some) markdown in axis and legend titles with the help of the ggtext package30 and the ggplot2 theme system (see Chapter 18). To enable markdown you need to set the relevant theme element to , as demonstrated below:

There are two ways to remove the axis label. Setting omits the label but still allocates space; setting removes the label and its space.

Text labels

Adding text to a plot is one of the most common forms of annotation. 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. However, text annotation can be tricky due to the way that R handles fonts. The ggplot2 package doesn’t have all the answers, but it does provide some tools to make your life a little easier. The main tool for labelling plots is , which adds text at the specified and positions. has the most aesthetics of any geom, because there are so many ways to control the appearance of a text:

  • The aesthetic provides the name of a font. This aesthetic does allow you to use the name of a system font, but some care is required. There are only three fonts that are guaranteed to work everywhere: “sans” (the default), “serif”, or “mono”. To illustrate these:

    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 two groups of GDs: screen devices such as (for Windows), (for Macs), (mostly for Linux) and (within RStudio) draw the plot to the screen, whereas file devices such as and write the plot to a file. 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. Two packages simplify the quandary a bit:

    Both approaches have pros and cons, so you will to need to try both of them and see which works best for your needs.

  • The aesthetic specifies the face, and can take three values: “plain” (the default), “bold” or “italic”. For example:

  • You can adjust the alignment of the text with the (“left”, “center”, “right”, “inward”, “outward”) and (“bottom”, “middle”, “top”, “inward”, “outward”) aesthetics. By default the aligment is centered, but there are often good reasons to override this. One of the most useful alignments is “inward”. It aligns text towards the middle of the plot, which ensures that labels remain within the plot limits:

  • The font size is controlled by the aesthetic. Unlike most tools, ggplot2 specifies the size in millimeters (mm), rather than the usual points (pts). The reason for this choice is that it makes it the units for font sizes consistent with how other sizes are specified in ggplot2. (There are 72.27 pts in a inch, so to convert from points to mm, just multiply by 72.27 / 25.4).

  • specifies the rotation of the text in degrees.

The ggplot2 package does allow you to map data values to the aesthetics used by , but you should use restraint: it is hard to perceive the relationship between variables mapped to these aesthetics, and rarely useful to do so.

In addition to the various aesthetics, has three parameters that you can specify. Unlike the aesthetics these only take single values, so they must be the same for all labels:

  • 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). In this situation it’s useful to offset the text a little, which you can do with the and parameters:

    (Note that I manually tweaked the x-axis limits to make sure all the text fit on the plot.)

  • The third parameter is . If , overlapping labels will be automatically removed from the plot. The algorithm is simple: labels are plotted in the order they appear in the data frame; if a label would overlap with an existing point, it’s omitted.

    At first glance this feature does not appear very useful, but the simplicity of the algorithm comes in handy. If you sort the input data in order of priority the result is a plot with labels that emphasise important data points.

A variation on is : it draws a rounded rectangle behind the text. This makes it useful for adding labels to plots with busy backgrounds:

Labelling data well poses some challenges:

  • Text does not affect the limits of the plot. Unfortunately there’s no way to make this work since a label has an absolute size (e.g. 3 cm), regardless of the size of the plot. This means that the limits of a plot would need to be different depending on the size of the plot — there’s just no way to make that happen with ggplot2. Instead, you’ll need to tweak and based on your data and plot size.

  • If you want to label many points, it is difficult to avoid overlaps. is useful, but offers little control over which labels are removed. A popular technique for addressing this is to use the ggrepel package https://github.com/slowkow/ggrepel by Kamil Slowikowski. The package supplies , which optimizes the label positioning to avoid overlap. It works quite well so long as the number of labels is not excessive:

  • It can sometimes be difficult to ensure that text labels fit within the space that you want. The ggfittext package https://github.com/wilkox/ggfittext by Claus Wilke contains useful tools that can assist with this, including functions that allow you to place text labels inside the columns in a bar chart.

Building custom annotations

Labelling individual points with text is an important kind of annotation, but it is not the only useful technique. The ggplot2 package provides several other tools to annotate plots using the same geoms you would use to display data. For example you can use:

  • and to add text, as illustrated earlier.

  • to highlight interesting rectangular regions of the plot. has aesthetics , , and .

  • , and to add lines. All these geoms have an parameter, which allows you to place an arrowhead on the line. Create arrowheads with , which has arguments , , and .

  • , and allow you to add reference lines (sometimes called rules), that span the full range of the plot.

Typically, you can either put annotations in the foreground (using if needed so you can still see the data), or in the background. With the default background, a thick white line makes a useful reference: it’s easy to see but it doesn’t jump out at you. To illustrate how ggplot2 tools can be used to annotate plots we’ll start with a time series plotting US unemployment over time:

One useful way to annotate this plot is to use shading to indicate which president was in power at the time. To do this, we use to introduce shading, to introduce separators, to add labels, and then use to overlay the data on top of these background elements:

Notice that there is little new here: for the most part, annotating plots in is a straightforward manipulation of existing geoms. That said, there is one special thing to note in this code: the use of and as positions. These refer to the top and bottom (or left and right) limits of the plot.

This technique can be applied in other ways too. For instance, you can use it to add a single annotation to a plot, but it’s a bit fiddly because you have to create a one row data frame:

This code works, and generates the desired plot, but it is very cumbersome. It would be annoying to have to do this every time you want to add a single annotation, so ggplot2 includes the helper function which creates the data frame for you:

The convenience of the function comes in handy in other situations. For example, a common form of annotation is to highlight a subset of points by drawing larger points in a different colour underneath the main data set. To highlight vehicles manufactured by Subaru you could use this to create the basic plot:

The problem with this is that the highlighted category would not be labelled. This is easily rectified using

This approach has the advantage of creating a label inside the plot region, but the drawback is that the label is distant from the points it picks out (otherwise the orange and black dot adjacent to the label might be confused for real data). An alternative approach is to use a different geom to do the work. and can be used to draw curves and lines connecting points with labels, and can be used in conjunction with as illustrated below:

Direct labelling

The Subaru plots above provide examples of “direct labelling”, in which the plot region itself contains the labels for groups of points instead of using a legend. This usually makes the plot easier to read because it puts the labels closer to the data. The broader ggplot2 ecosystem contains a variety of other tools to accomplish this in a more automated fashion. The directlabels package, by Toby Dylan Hocking, provides a number of tools to make this easier:

Directlabels provides a number of position methods. is a reasonable place to start for scatterplots, but there are other methods that are more useful for frequency polygons and line plots. See the directlabels website, http://directlabels.r-forge.r-project.org, for other techniques.

Another take on this idea comes from the ggforce package by Thomas Lin Pedersen https://github.com/thomasp85/ggforce. The ggforce package contains a lot of useful tools to extend ggplot2 functionality, including functions such as that overlays a plot with circular “highlight” marks. For example:

A third approach to direct labelling is provided in the gghighlight package by Hiroaki Yutani https://github.com/yutannihilation/gghighlight. In many situations is useful for highlighting points or lines (or indeed a variety of different geoms) within a plot, particularly for longitudinal data:

Annotation across facets

When used well, annotations can be a powerful tool to help your reader make sense of your data. One example of this is when you want the reader to compare groups across facets. For example, in the plot below it is easy to see the relationship within each facet, but the subtle differences across facets do not pop out:

It is much easier to see these subtle differences if we add a reference line:

In this plot, each facet displays the data for one category agains the same regression line. This makes it easier to compare the facets to each other because there is shared reference line to assist the visual comparison.

A variation on this theme arises when you want each facet of a plot to display data from a single group, with the complete data set plotted unobtrusively in each panel to aid visual comparison. The gghighlight package is particularly useful in this context:

Sours: https://ggplot2-book.org/annotations.html
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ggplot2 - annotate outside of plot

This is now straightforward with ggplot2 3.0.0, since now clipping can be disabled in plots by using the argument in coordinate functions such as or . Here's an example below.

enter image description here


You don't need to be drawing a second plot. You can use to position grobs anywhere inside or outside the plotting area. The positioning of the grobs is in terms of the data coordinates. Assuming that "5", "10", "15" align with "cat1", "cat2", "cat3", the vertical positioning of the textGrobs is taken care of - the y-coordinates of your three textGrobs are given by the y-coordinates of the three data points. By default, clips grobs to the plotting area but the clipping can be overridden. The relevant margin needs to be widened to make room for the grob. The following (using ggplot2 0.9.2) gives a plot similar to your second plot:

enter image description here


Simplier solution based on

Sours: https://newbedev.com/ggplot2-annotate-outside-of-plot

ggplot2 - annotate outside of plot

I would like to associate sample size values with points on a plot. I can use to position the numbers near the points, but this is messy. It would be much cleaner to line them up along the outside edge of the plot.

For instance, I have:

Which produces this plot: enter image description here

I would prefer something more like this: enter image description here

I know I can create a second plot and use (a la this post) but it would be tedious to determine the spacing of the textGrobs to line up with the y-axis. Is there an easier way to do this? Thanks!

Answer #1:

You don't need to be drawing a second plot. You can use to position grobs anywhere inside or outside the plotting area. The positioning of the grobs is in terms of the data coordinates. Assuming that "5", "10", "15" align with "cat1", "cat2", "cat3", the vertical positioning of the textGrobs is taken care of - the y-coordinates of your three textGrobs are given by the y-coordinates of the three data points. By default, clips grobs to the plotting area but the clipping can be overridden. The relevant margin needs to be widened to make room for the grob. The following (using ggplot2 0.9.2) gives a plot similar to your second plot:

enter image description here

Answered By: Sandy Muspratt

Answer #2:

This is now straightforward with ggplot2 3.0.0, since now clipping can be disabled in plots by using the argument in coordinate functions such as or . Here's an example below.

enter image description here

Answered By: bschneidr

Answer #3:

Simplier solution based on

Answered By: Stepan S. Sushko

The answers/resolutions are collected from stackoverflow, are licensed under cc by-sa 2.5 , cc by-sa 3.0 and cc by-sa 4.0 .


Sours: https://www.py4u.net/discuss/858760

Annotate plot ggplot outside

Annotate Text Outside of ggplot2 Plot in R (Example)

 

In this tutorial, I’ll explain how to add text outside of the plot area of a ggplot2 graph in R.

The tutorial looks as follows:

Here’s the step-by-step process…

 

Example Data, Packages & Basic Graphic

We use the following data as basement for this R programming tutorial:

data <- data.frame(x =1:5, # Example data frame y =1:5) data # Inspect data frame# x y# 1 1 1# 2 2 2# 3 3 3# 4 4 4# 5 5 5

data <- data.frame(x = 1:5, # Example data frame y = 1:5) data # Inspect data frame # x y # 1 1 1 # 2 2 2 # 3 3 3 # 4 4 4 # 5 5 5

The previous output of the RStudio console shows that our example data contains two simple numeric variables.

We also need to install and load the ggplot2 package, if we want to use the functions that are included in the package:

install.packages("ggplot2")# Install ggplot2 package library("ggplot2")# Load ggplot2 package

install.packages("ggplot2") # Install ggplot2 package library("ggplot2") # Load ggplot2 package

As next step, we can plot our data:

ggp <- ggplot(data, aes(x, y, label ="my text"))+# Create ggplot2 plot geom_point()+ geom_text(x =5.5, size =5) ggp # Draw ggplot2 plot

ggp <- ggplot(data, aes(x, y, label = "my text")) + # Create ggplot2 plot geom_point() + geom_text(x = 5.5, size = 5) ggp # Draw ggplot2 plot

 

r graph figure 1 annotate text outside ggplot2 r

 

As shown in Figure 1, the previous R programming code created a typical ggplot2 scatterplot. However, you can also see that our text was not annotated to the plot.

 

Example: Increasing Margins & Allow Text Outside of Plot

In this Example, I’ll explain how to add text elements at the right side of the plot borders using the coord_cartesian function. Have a look at the following R code:

ggp +# Annotate text theme(plot.margin= unit(c(1, 5, 1, 1), "lines"))+ coord_cartesian(clip ="off")

ggp + # Annotate text theme(plot.margin = unit(c(1, 5, 1, 1), "lines")) + coord_cartesian(clip = "off")

 

r graph figure 2 annotate text outside ggplot2 r

 

As shown in Figure 2, the previous code created a ggplot2 graph with multiple text elements outside the plotting area.

 

Video, Further Resources & Summary

Would you like to learn more about the annotation of text elements? Then you might watch the following video of my YouTube channel. In the video, I’m explaining the R programming codes of this article.

 

The YouTube video will be added soon.

 

Furthermore, you may read the related articles of https://statisticsglobe.com/:

 

In this R tutorial you learned how to annotate a character string outside of a ggplot2 plot. If you have any further questions, please let me know in the comments section below.

Sours: https://statisticsglobe.com/annotate-text-outside-of-ggplot2-plot-in-r
Add Text Label to ggplot2 Plot in R (3 Examples) - Annotate Element to Graphic - Modify Color \u0026 Size

Adding Text Outside of ggplot

alistaire:

You could do it with

there is vignette with about this:
https://cran.r-project.org/web/packages/gridExtra/vignettes/tableGrob.html
and there is a function

And I think could help to arrange or

Created on 2019-01-21 by the reprex package (v0.2.1)

You can read the vignettes of all these :package:, there are some example about arranging plot.

4 Likes

Sours: https://community.rstudio.com/t/adding-text-outside-of-ggplot/21950

You will also be interested:

How to Add Text Outside of ggplot2 Plot Borders in R (Example Code)

In this tutorial, I’ll illustrate how to annotate a character string outside of a ggplot2 plot in the R programming language.

Preparing the Example

data(iris)# Loading data head(iris)# Sepal.Length Sepal.Width Petal.Length Petal.Width Species # 1 5.1 3.5 1.4 0.2 setosa # 2 4.9 3.0 1.4 0.2 setosa # 3 4.7 3.2 1.3 0.2 setosa # 4 4.6 3.1 1.5 0.2 setosa # 5 5.0 3.6 1.4 0.2 setosa # 6 5.4 3.9 1.7 0.4 setosa

data(iris) # Loading data head(iris) # Sepal.Length Sepal.Width Petal.Length Petal.Width Species # 1 5.1 3.5 1.4 0.2 setosa # 2 4.9 3.0 1.4 0.2 setosa # 3 4.7 3.2 1.3 0.2 setosa # 4 4.6 3.1 1.5 0.2 setosa # 5 5.0 3.6 1.4 0.2 setosa # 6 5.4 3.9 1.7 0.4 setosa

install.packages("ggplot2")# Install & load ggplot2 library("ggplot2")

install.packages("ggplot2") # Install & load ggplot2 library("ggplot2")

my_plot <- ggplot(iris,# Creating default ggplot2 plot aes(Petal.Length, Petal.Width, label ="some text"))+ geom_point()+ geom_text(x =0, size =5)# Trying to add text my_plot # ggplot2 plot does not show text

my_plot <- ggplot(iris, # Creating default ggplot2 plot aes(Petal.Length, Petal.Width, label = "some text")) + geom_point() + geom_text(x = 0, size = 5) # Trying to add text my_plot # ggplot2 plot does not show text

r graph figure 1 add text outside ggplot2 borders r

Example: Change Plot Margins & Allow Text Outside of Graphic Borders

my_plot +# Adding text theme(plot.margin = unit(c(1,1,1,6),"lines"))+ coord_cartesian(clip ="off")

my_plot + # Adding text theme(plot.margin = unit(c(1, 1, 1, 6), "lines")) + coord_cartesian(clip = "off")

r graph figure 2 add text outside ggplot2 borders r

Sours: https://data-hacks.com/how-add-text-outside-ggplot2-plot-borders-r


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