ggBubbles

Thomas Schwarzl

Contents

1 Introduction

Welcome to this vignette!

1.1 Loading the package

You can load the package with this command

require(ggplot2)
require(ggBubbles)

1.2 In 15 seconds

The package introduces position_surround() for ggplot2.

Parameter is offset which controls the offsets for position corrections (default is 0.1).

position_surround() can be used in many ggplot2 functions like geom_point or geom_text:

Figure 1: Example of a MiniBubble plot

2 Bubbleplot vs Minibubble plot

Here we demonstrate the advantage of MiniBubble plots compared to traditional Bubbleplot in certain usecases with discrete data.

Please not that in this vignette we will use dplyr and tibble from tidyverse.

require(dplyr)
require(tibble)

2.1 Example data

First, we load a small example data

data(MusicianInterestsSmall)

which contains data from musicians about their experience in differente music genres they have with their music instruments.

head(MusicianInterestsSmall)

Table 1: First rows of MusicianInterestsSmall

Instrument	Genre	Level
Piano	Jazz	Experienced
Piano	Jazz	Experienced
Piano	Jazz	Intermediate
Piano	Jazz	Beginner
Piano	Jazz	Interested
Piano	Jazz	Interested

2.2 Traditional Bubble plot

The traditional bubble plot is able to portrait the amount of guitarrists or pianists able to play jazz or classical music by size and display the average experience level by colour coding.

ggplot(data = MusicianInterestsSmall %>% 
               group_by(Instrument, Genre) %>% 
               summarize(Count = n(), AvgLevel = mean(as.integer(Level))),
        aes(x = Instrument, y = Genre, size = Count, col = AvgLevel)) +
        geom_point() + theme_bw(base_size = 18) +
        scale_colour_gradientn(
            colours  = rev(topo.colors(2)),
            na.value = "transparent",
            breaks   = as.integer(MusicianInterestsSmall$Level) %>% 
                             unique %>% sort,
            labels   = levels(MusicianInterestsSmall$Level),
            limits   = c(as.integer(MusicianInterestsSmall$Level) %>% min,
                         as.integer(MusicianInterestsSmall$Level) %>% max)) +
        scale_size_continuous(range = c(3, 11)) 

Figure 2: Traditional Bubble Plot

From a data visualisation point of view, it is debateable how good point sizes are to display counts. However, in general we can agree that averages often hide a lot of useful information.

2.3 MiniBubble Plot

The MiniBubble plot allows to show each musician and their corresponding skill level individually:

ggplot(data = MusicianInterestsSmall,
       aes(x = Instrument,
           y = Genre,
           col = Level)) +
       geom_point(position = position_surround(), size = 4) +
       scale_colour_manual(values = c("#00e5ff",
                                      "#4694ff",
                                      "#465aff",
                                      "#2c00c9")) + 
       theme_bw(base_size = 18)

Figure 3: Example of a MiniBubbleplot with position_surround

This is done by the position_surround() function passed to the position argument of geom_point. Note, that only exact overlaps will be dodged. The points will surround the center in layers which will be filled clockwise.

Since each individual data point is shown seperately, you can also use shape and fill to show further features, as long the plot will not be overloaded with information.

Also, you can use geom_text(position = position_surround()) to overlay the points with text, or make the text appear in shiny when hovering.

MiniBubbleplot allows to show more features in a bubble plot.

3 Offset parameter

The offset of the dodged points can be handed as parameters to position_surround().

ggplot(data = MusicianInterestsSmall,
       aes(x = Instrument,
           y = Genre,
           col = Level)) +
       geom_point(position = position_surround(offset = .2), size = 4) +
       scale_colour_manual(values = c("#00e5ff",
                                      "#4694ff",
                                      "#465aff",
                                      "#2c00c9")) + 
       theme_bw(base_size = 18)

Figure 4: Example of a MiniBubbleplot with position_surround

4 Another example

We load a bigger test data set:

data(MusicianInterests)

This dataset also contains information about the musicians themselves from the multiple - choice survey.

head(MusicianInterests)

Table 2: First rows of MusicianInterests

Genre	Instrument	Level	Musician
Acoustic	Acoustic Guitar	4	F
Acoustic	Acoustic Guitar	3	A
Acoustic	Acoustic Guitar	3	B
Acoustic	Acoustic Guitar	3	C
Acoustic	Acoustic Guitar	2	D
Acoustic	Bass Guitar	3	A

The basis of the plot is simply:

p <- ggplot(data        = MusicianInterests,
            aes(x       = Genre,
                y       = Instrument,
                col     = Level)) +
    geom_point(size     = 1.8, 
               position = position_surround(offset = .2))

Here we add some graphical parameters to make it pretty:

p <- p + theme_bw(base_size = 17) + 
    theme(axis.text.x = element_text(angle = 45, hjust = 1)) +
    scale_colour_gradientn(
            colours  = rev(topo.colors(2)),
            na.value = "transparent",
            breaks   = 1:6,
            labels   = c("Interested",
                         "Beginner",
                         "Intermediate",
                         "Experienced",
                         "Very experienced",
                         "Pro")) +
    xlab("") + ylab("")
p

Figure 5: Use case of position_surround
The levels of experiences can be displayed for each individual musician, rather than displaying an average over the group

5 Algorithmic details

The position_surround() algorithm determines how many points are overlaying and then displays the points in clockwise ration around the center in quadratic layers. Please see the graphical illustration here:

Figure 6: Demonstration of position_surround algorithm
Points will be added clockwise in layers.

6 Support or Feedback

For feedback or suggestions please contact the maintainer: Thomas Schwarzl thomas@schwarzl.net or schwarzl@embl.de.