In data analysis, one may want to visualize data for a specific subgroup of observations. Simply filtering out observations that do not belong to the subgroup may take the information out of context. Therefore, it is necessary to have tools that allow the analyst to draw attention (focus) on that subgroup within the complete relevant information available.
ggplot2 is a great tool for data visualization in general,
constructing graphics that focus on those subgroups may need very
troublesome manipulation of data and graphical scales (for example
colors) together, i.e. setting low alpha for unimportant observations,
coloring things in a way that highlights the focused subgroup, etc.
ggfocus allows you to build graphics that focus on those specific
subgroups by doing the scale manipulation automatically while keeping
all the flexibility from
ggplot. The idea behind this package is from
this issue from
The package is available on CRAN, but you can also install the latest version from Github with devtools.
devtools::install_github("Freguglia/ggfocus") # Latest version install.packages("ggfocus") # CRAN version
The workflow of
ggfocus is the same as any
ggplot graphic with the
addition of the focus scales family of functions:
scale_color_focus(focus_levels = character(0), color_focus = NULL, color_other = "black", palette_focus = "Set1")
scale_fill_focus(focus_levels = character(0), color_focus = NULL, color_other = "gray", palette_focus = "Set1")
scale_alpha_focus(focus_levels = character(0), alpha_focus = 1, alpha_other = .05)
scale_linetype_focus(focus_levels = character(0), linetype_focus = 1, linetype_other = 3)
scale_shape_focus(focus_levels = character(0), shape_focus = 8, shape_other = 1)
scale_size_focus(focus_levels = character(0), size_focus = 3, size_other = 1)
The user should map the variable with the grouping variable to all the
aes used to highlight observations and then use these functions to
automatically create scales that highlight a specified group of
Both the selected and unselected groups characteristics are customizable with the parameters of focus scales. See the examples below.
Creating an example dataset.
library(ggfocus) set.seed(1) # Create an example dataset df <- data.frame(u1 = runif(300), u2 = runif(300), grp = sample(LETTERS[1:10], 300, replace = TRUE)) dplyr::glimpse(df) #> Observations: 300 #> Variables: 3 #> $ u1 <dbl> 0.26550866, 0.37212390, 0.57285336, 0.90820779, 0.20168193, 0.898… #> $ u2 <dbl> 0.67371223, 0.09485786, 0.49259612, 0.46155184, 0.37521653, 0.991… #> $ grp <fct> C, E, B, E, E, C, J, B, G, H, B, J, G, A, I, H, F, F, C, J, A, F,…
Visualizing with focus on observations such that
ggplot(df, aes(x = u1, y = u2, color = grp, alpha = grp)) + geom_point() + scale_color_focus(c("A", "B"), color_focus = c("blue", "red")) + scale_alpha_focus(c("A", "B"))
ggfocus creates the focused visualization solely by controlling
ggplot extensions and types of graphics can interact
with it the same way, an example with the
maps package is shown below.
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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