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1 Arranging the plots in a layout

Now that we have all the figures in a list, we can make arrangements with our figures. For this we use the function ggarrange() from the ggpubr library. The ggarrange() takes multiple arguments, but the main one is the figures we want to arrange. We can specify the figures in two ways, defining a list with all our figures, or if we want specific figures we can define the figures one by one. For example:

library(ggpubr) # load the library
ggarrange(plotlist = figures)

This might not be the best arrangement for the figures, since its too much information, so we can arrange it in several ‘pages’. The way ggarrange() organizes the figure is in a n x n grid. If not specified, it will try to arrange the all the figures in a single grid, but we can use the arguments ncol and nrow to limit the number of elements per cell in our grid. For example:

ggarrange(plotlist = figures, ncol = 2, nrow = 1)
## $`1`

## 
## $`2`

## 
## $`3`

## 
## attr(,"class")
## [1] "list"      "ggarrange"

If we want to select specific figures, we will need to either delete them from the list, or just add them one by one. We can also add labels to later reference in our figure caption, for example:

toppanel <- ggarrange(
  figures$bars, figures$box, # These are our figures
  labels = c('a.', 'b.') # The labels
) 

toppanel

We can also make arrangements with arrangements, for example:

p1 <- ggarrange(toppanel, figures$timeseries, ncol = 1, heights = c(2, 1), labels = c('','c')) # Add another figure at the bottom

p1

Finally we can add a general title for the arrangement:

p1 %>% 
  annotate_figure(top = text_grob('Summary of the findings', face = 'bold', size = 20))

Notice that the specification of the text uses the function text_grob() which is a similar to the way we specify text for the themes in ggplot.

2 Facets

Facets are a way of stratifying the data based on variables in the data set, you can think about it in a similar way we have been using groups. To create a stratified plot we can use the function facet_grid() which will ask for a variable to go in the rows and another for columns:

figures$timeseries +
  facet_grid(rows = vars(trap_type))

figures$histogram <- captures %>% # The data we will use 
  ggplot() + # set the canvas
  geom_histogram(aes(treated), fill = 'red4') + # We will create a histogram of the Age
  facet_grid(
    rows = vars(trap_type), # We will use the Sex variable for rows
    cols = vars(municipality) # We use the Result variable for Columns
  )

figures$histogram

3 Exercise

Now that you know some tools to look for information, you will have to make your figures on your own. If you want to make figures with the datasets we have been using you can do those, or you can use any of the code online to replicate them.

  1. Go to the Data to Viz website
  2. Identify a few figures that you would like to do (you can also do figures that we have previously done in the exercises)
  3. Make sure you label your figures!
  4. Experiment changing the colors, variables, themes etc…
  5. Replicate a few figures and put them in an arrangement.

At the end we will have a discussion where you can share your figures and troubleshoot any problems you might have had. If you have something you want to share, feel free to add it to the shared folder below:

This lab has been developed with contributions from: Jose Pablo Gomez-Vazquez.
Feel free to use these training materials for your own research and teaching. When using the materials we would appreciate using the proper credits. If you would be interested in a training session, please contact: