knitr::opts_chunk$set( collapse = TRUE, comment = "#>", fig.width = 6, warning = FALSE, message = FALSE )
library(ggfocus)
ggfocus
is a ggplot2
extension that allows the creation of special scales
with the purpose of highlighting subgroups of data. The user is able to define
what levels of mapped variables should be selected and
how the selected subroup should be displayed as well as the unselected subgroup.
We shall create a sample dataset to be used throughout this guide: the variables
u1
and u2
are numeric values and grp
is a factor variable with values in
A
, B
, \dots, J
.
set.seed(2) # Create an example dataset df <- data.frame(u1 = runif(300) + 1*rbinom(300, size = 1, p = 0.01), u2 = runif(300), grp = sample(LETTERS[1:10], 300, replace = TRUE)) dplyr::glimpse(df)
A natural type of visualization should be mapping u1
and u2
to the x
and y
axes and mapping grp
to color.
ggplot(df, aes(x = u1, y = u2, color = grp)) + geom_point()
Suppose you want focus the analysis on the levels A
and B
. It is not easy to
identify where the points are because there is a lot of "noise" in the colors
used due to the amount of levels of grp
. A simple solution would be filtering
out other groups.
library(dplyr) df %>% filter(grp %in% c("A", "B")) %>% ggplot(aes(x = u1, y = u2, color = grp)) + geom_point()
While it solves the problems of too many colors making the viewer unable to quickly locate points of A
and B
and differentiate them, we did lose important information during the filtering, e.g., there are only 4 observations with u1
greater than 1, and 3 of them are in the grp
A
or B
. This is an important
information contained in the data that should be considered when the analysis
focuses on A
and B
but require the other observations (a context) in
order to be obtained. Therefore, we want to focus on specific levels without
taking them out of the context of the data.
The solution to focus the analysis in the subgroup and keep the context is to use all the data but group each "unfocused" level in a new level and manipulate scales. This requires data wrangling and scale manipulation.
df %>% mutate(grp = ifelse(grp %in% c("A", "B"), as.character(grp), "other")) %>% ggplot(aes(x = u1, y = u2, color = grp)) + geom_point() + scale_color_manual(values = c("A" = "red", "B" = "blue", "other" = "gray"))
This is a solution to the visualization but it required us to:
"red"
, "blue"
and "gray"
resulted in a focus on "red"
and "blue"
, therefore the "gray"
color is the one that should be used on the unselected group.ggfocus
has the goal of creating graphs that focus on a subgroup of the data
like the one in the previous example, but without the three drawbacks mentioned.
No data wrangling is required (it is all done internally), good scales for
focusing on the subgroup are automatically created by default and as a result it
is less verbose than selecting scales manually.
Not only color
scales are available, but also scales for every other aes
in
ggplot
: fill
, alpha
, size
, linetype
, shape
, etc. Making it easy to
guide the viewer towards the information to focus on using the most appropriate
aesthetics for each graph.
The fact that ggfocus
manipulates scales only, makes it usable with other
extensions of ggplot
. Examples using each scale are provided in this guide.
color
and fill
scale_color_focus(focus_levels, color_focus = NULL, color_other = "gray", palette_focus = "Set1") scale_fill_focus(focus_levels, color_focus = NULL, color_other = "gray", palette_focus = "Set1")
color
and fill
scales have the same default focus scales. They use the color
"gray"
for unselected observations and the "Set1"
palette. Usually, a
qualitative color scale is best to visualize the levels focused. The available
palettes can be viewed with RColorBrewer::display.brewer.all()
.
ggplot(df, aes(x = u1, y = u2, color = grp)) + geom_point() + scale_color_focus(c("A", "B")) ggplot(iris, aes(x = Petal.Length, fill = Species)) + geom_histogram() + scale_fill_focus("virginica")
One may also use a single color in the color_focus
argument to make all the
highlighted levels use the same color value. This allows to focus on the subroup
as a whole instead of in its individual levels.
ggplot(df, aes(x = u1, y = u2, color = grp)) + geom_point() + scale_color_focus(c("A", "B"), color_focus = "red")
alpha
scale_alpha_focus(focus_levels, alpha_focus = 1, alpha_other = 0.2)
alpha
is probably one of the most important aes
when drawing focus to
specific subroups of your data as the transparency naturally removes the
importance given to certain elements. It does not distinguish different groups,
therefore it is usually used as a secondary highlighting scale. The argument
alpha_other
can be used to control the visibility if the unselected
observations.
ggplot(df, aes(x = u1, y = u2, alpha = grp)) + geom_point() + scale_alpha_focus(c("A", "B")) # Does not distinguish A and B.
ggplot(df, aes(x = u1, y = u2, alpha = grp, color = grp)) + geom_point() + scale_alpha_focus(c("A", "B"), alpha_other = 0.5) + scale_color_focus(c("A", "B")) + theme_bw() # White background
linetype
scale_linetype_focus(focus_levels, linetype_focus = 1, linetype_other = 3)
By default, a continuous line is used for focused levels and dotted line for
other levels. Similar to color
, one can pass a vector of values in
linetype_focus
to create different linetypes for each highlighted subgroup
although the highest contrast is between continuous and dotted lines.
ggplot(datasets::airquality, aes(x = Day, y = Temp, linetype = factor(Month), group = factor(Month))) + geom_line() + scale_linetype_focus(focus_levels = c(5,7))
ggplot(datasets::airquality, aes(x = Day, y = Temp, linetype = factor(Month), group = factor(Month))) + geom_line() + scale_linetype_focus(focus_levels = c(5,7), linetype_focus = c(1,5))
shape
scale_shape_focus(focus_levels, shape_focus = 8, shape_other = 1)
Not to useful to focus on subroups, but it is available. Works just like
linetype
.
ggplot(df, aes(x = u1, y = u2, shape = grp)) + geom_point() + scale_shape_focus(c("A", "B"))
ggplot(df, aes(x = u1, y = u2, shape = grp)) + geom_point() + scale_shape_focus(c("A", "B"), shape_focus = c(2,3))
size
scale_size_focus(focus_levels, size_focus = 3, size_other = 1)
Have similar properties as alpha
, but using the size of the elements instead
to reduce importance instead of transparency.
ggplot(df, aes(x = u1, y = u2, size = grp)) + geom_text(aes(label = grp)) + scale_size_focus(c("A", "B"))
ggplot(df, aes(x = u1, y = u2, size = grp, shape = grp)) + geom_point() + scale_size_focus(c("A", "B")) + scale_shape_focus(c("A", "B"))
The main advantage of ggfocus lies in the fact it only manipulates scales to
create the focus in the graphs. This fact allows it to interact with other
ggplot
extensions naturally, as it will work with any type of geom
.
Some examples are below:
library(dplyr) library(ggrepel) iris %>% mutate(id = row_number()) %>% ggplot(aes(x = Petal.Length, y = Sepal.Length, label = id, size = id)) + geom_text_repel() + scale_size_focus(c(100,127), size_focus = 8, size_other = 2)
library(maps) wm <- map_data("world") ggplot(wm, aes(x=long, y = lat, group = group, fill = region)) + geom_polygon(color="black") + theme_void() + scale_fill_focus(c("Brazil", "Canada", "Australia", "India"), color_other = "gray")
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