Introduction
The ggthemeUL package provides essential tools to
produce charts that align with the comprehensive visual
identity of the University of Ljubljana and its members. The rules
defined by the comprehensive visual identity will be implemented
starting January 1st, 2024.
This package is designed to complement the functionality of
ggplot2, which can be installed from CRAN. The
ggthemeUL package itself can be downloaded from the R-Forge
repository.
Please note that ggthemeUL is currently under active
development. For suggestions or error reporting, please contact Marjan
Cugmas at marjan.cugmas@fdv.uni-lj.si.
While ggthemeUL is intended for basic adjustments to the
visual appearance of charts, more complex chart types and customizations
may require additional modifications. In such cases, consider using
functions like scale_*_steps and
scale_*_gradient. For a deeper understanding of these
customizations, the book ggplot2: Elegant Graphics for Data
Analysis (https://ggplot2-book.org/) is highly recommended.
To ensure visual accessibility, consider using the
colorBlindness package. Specifically, the
displayAllColors function (e.g,
displayAllColors(ul_color())) allows you to preview colors
considering various types of visual impairments, and
cvdPlot enables you to display ggplot objects as they would
be perceived by individuals with different types of visual impairments.
Consider also Coblis
- Color Blindness Simulator.
# install.packages("ggthemeUL", repos="http://R-Forge.R-project.org")
library(ggthemeUL)
#> Loading required package: ggplot2
library(ggplot2)
library(scales)
Color palettes
The visual identity defines six unique colors across three color
palettes. The primary color palette is typically used for chart
annotations such as text color and bandings color.
For marking attributes, the secondary color palettes should be
employed. When utilizing these palettes, it is advisable to avoid
combining red and green hues (for example, pink and turquoise or green
and burgundy). Such combinations may not be accessible to visually
impaired individuals. The colorBlindness package can always
be used to test color accessibility.
The primary color palettes comprises four distinct
colors. The red color, as the foundational color of the University of
Ljubljana, should be used sparingly, typically reserved for special
highlights or accent elements. The antracit color is intended to serve
as a substitute for traditional black, and is recommended as the primary
color for text. The color named lajt may be used as a background color
in certain instances.
red
#E03127
antracit
#58595b
medium
#A7A8AA
lajt
#E8E9EA
The cold color palette is comprised of a selection
of two hues each of blue and green.
darkblue
#0033a0
navyblue
#0082C0
turquoise
#00B1AC
green
#00694E
The warm color palette contains four different
hues.
yellow
#EACE12
orange
#CB511C
burgundy
#9A2F31
pink
#C43788
You can retrieve the HEX codes of individual colors by invoking the
ul_color function (all available HEX codes will be returned
in the case you do not set any color name). For example:
ul_color("lajt")
#> [1] "#E8E9EA"
Theme UL
The theme_ul function applies minor visual adjustments to a chart,
such as rendering titles in bold. By default, the legend is positioned
at the top of the plot, the panel background color is set to ‘lajt’, and
the plot background color is white. However, there may be instances
where you’d want the plot background color to match the ‘lajt’ color,
such as when it aligns with the background color of a publication. In
such scenarios, use plot.background.fill = “#E8E9EA” or
plot.background.fill = ul_color(“lajt”) to modify the plot’s background
color.
Below is an illustration of how the theme_ul function is used,
compared to the default settings. It should be noted that it is employed
in conjunction with the ul_color function to determine the color of the
bars as well as the color of the horizontal line.
basicChart <- ggplot(df, aes(x = country)) +
geom_bar() +
geom_hline(yintercept = 5) +
labs(x = element_blank(),
y = "Frequency",
title = "Lorem ipsum dolor sit amet",
caption = "Data source: this is all fake data.",
subtitle = "Eiusmod tempor incididunt ut labore et dolore magna.")
basicChart
basicChart +
geom_bar(fill = ul_color("navyblue")) +
geom_hline(yintercept = 5, color = ul_color("red")) +
theme_ul(plot.background.fill = "#E8E9EA")
If you wish to modify certain adjustments applied by
theme_ul, you can easily do so by using the theme function
to override these settings. For instance, if you want to change the
color of the chart title to red, you can do this:
basicChart +
geom_bar(fill = ul_color("navyblue")) +
geom_hline(yintercept = 5, color = ul_color("red")) +
theme_ul(plot.background.fill = "#E8E9EA") +
theme(plot.title = element_text(color="red"))
Nominal scales
When visualizing a variable of nominal scale, the
scale_fill_ul function can be used without specifying any
additional parameters. This function will employ all the colors from
both the cold and warm color palettes in an order that optimizes visual
distinction between the colors. However, it is recommended to limit the
colors by selecting either scale_fill_ul("cold") for cold
colors, or scale_fill_ul("warm") for warm colors.
If the data comprises too many categories, consider applying a data
transformation (this could include excluding or combining categories to
reduce their number) or supplementing color with textures or patterns
for better differentiation.
basicChart <- ggplot(df, aes(x = country, y = height, fill = country)) +
facet_grid(.~gender) +
geom_boxplot(show.legend = FALSE) +
labs(y = "Sentiment", x = element_blank()) +
theme_ul(legend.justification = c(0, 1)) +
scale_y_continuous(labels = dollar_format(suffix=" cm",prefix="")) +
labs(x = element_blank(),
y = element_blank(),
title = "Height ipsum dolor sit amet",
caption = "Data source: this is all fake data.",
subtitle = "Eiusmod tempor incididunt ut labore et dolore magna.")
basicChart + scale_fill_ul()
basicChart + scale_fill_ul("cold")
Converging scales
An alternative to employing various hues is to modulate the shades of
a single hue (this is called converging color scale). This method is
especially advantageous for individuals with color blindness, as well as
for instances that require black and white printing. Besides, it often
produces a more coherent and aesthetically pleasing visual. This
approach can also be applied to ordinal variables, depending on the
variable’s content.
This can be accomplished by passing the name of a color to the
scale_fill_ul function, which will automatically generate a
gradient of that hue with different shades. Below, I have also set
reverse = TRUE, which inverts the order of the generated
colors.
basicChart + scale_fill_ul("navyblue", reverse = TRUE)
A similar approach can be adopted for interval and ratio variables.
In this case, set discrete = FALSE within the
scale_color_ul or scale_fill_ul functions.
Note that you can also specify additional parameters, such as
limits to determine the minimum and maximum values of a
variable (the default is derived from the data). This is particularly
useful when natural limits exist.
By adjusting the values parameter, you can manipulate the midpoint of
the color scale. However, this is not generally recommended. If an
asymmetric color scale is required, transforming the variable would
typically provide a more consistent approach.
basicChartCont <- ggplot(df, aes(x = age, y = height, color = weight)) +
geom_point(size = 5) +
theme_ul(legend.justification = c(0, 1)) +
labs(x = "Age (years)",
y = "Height (cm)",
title = "Lorem ipsum dolor sit amet",
caption = "Data source: this is all fake data.",
subtitle = "Eiusmod tempor incididunt ut labore et dolore magna.",
color = "Weight")
basicChartCont + scale_color_ul(palette = "navyblue", discrete = FALSE)
basicChartCont + scale_color_ul(palette = "navyblue", discrete = FALSE, values = c(0, 0.8, 1))
Diverging scales
Diverging color scales are utilized when it’s important to illustrate
how quantities deviate in two directions from a specific breakpoint.
Commonly, this breakpoint is established to visually distinguish values
above or below zero, or to separate values on either side of a specified
threshold. This could be a target value, an average, or a median
value.
In the realm of social sciences, these scales are often employed to
visualize responses captured on a Likert scale. The
ggthemeUL package provides several diverging scales. The
red-green (redGreen) and blue-turquoise
(blueTurquoise) hue combinations are commonly used. The
blue-turquoise combination tends to be more neutral, avoiding the
implication of negative or positive sentiment often associated with
red-green scales. For other avaiable divering scales look at the
package’s manual.
Below, I demonstrate the application of the redGreen
scale, along with the use of a white fill for the panel background. In
this context, the exact percentage values are not particularly crucial,
so to maintain a clean and focused visualization, the major grid lines
are rendered invisible by setting their color to white.
df$country <- factor(df$country, levels = rev(c("Slovenia", "Croatia", "France", "Austria")))
ggplot(df, aes(y = country, fill = satisfaction)) +
scale_x_continuous(labels = dollar_format(suffix=" %",prefix="", scale = 100)) +
geom_bar(position = position_fill(reverse = TRUE)) +
scale_fill_ul("redGreen") +
theme_ul(panel.background.fill = "white", panel.grid.major.color = "white") +
labs(x = element_blank(),
y = element_blank(),
title = "Lorem ipsum dolor sit amet",
caption = "Data source: this is all fake data.",
subtitle = "Eiusmod tempor incididunt ut labore et dolore magna.",
fill = "Agreement")
Exactly the same function can be applied in the case of a neutral
category. If a variable has even number of levels (i.e., possible
values), the middle one will be treated as a neutral category
(asymmetric scales are not yet supported).
The same function can also be utilized in scenarios where there’s a
neutral category. If the variable has an even number of levels (i.e.,
possible values), the midpoint level is treated as neutral. Please note
that asymmetric scales are not yet supported. If you need asymmetric
scales, then define the values manualy within the functions such as
scale_fill_manual.
basicChart <- ggplot(df, aes(y = country, fill = satisfactionWithNeutral)) +
geom_bar(position = position_fill(reverse = TRUE)) +
guides(fill = guide_legend(nrow = 1)) +
theme_ul() +
labs(fill = element_blank()) +
scale_x_continuous(labels = dollar_format(suffix=" %",prefix="", scale = 100)) +
theme_ul(plot.background.fill = ul_color("lajt")) +
labs(x = element_blank(),
y = element_blank(),
title = "Lorem ipsum dolor sit amet",
caption = "Data source: this is all fake data.",
subtitle = "Eiusmod tempor incididunt ut labore et dolore magna.",
fill = "Agreement")
basicChart + scale_fill_ul("redGreen")
By setting the parameter neutralColor one can change the
color of the neutral category. However, not that in the current
implementation, the selected color also affect the color of the
surrounding categories. Use this feature with caution.
Be aware that when using the ‘lajt’ color to color both the plot
background and a neutral category, the neutral category may not be
distinctly visible on the chart due to the color match with the
background. In such cases, you can make the legend keys distinct by
setting
legend.key = element_rect(color = ul_color("antracit"), fill = "transparent")
within the theme_ul function, or by defining
color = ul_color("antracit") within the
geom_bar function. However, keep in mind that the latter
approach may not be ideal. This is because employing the same color for
both the neutral category and the background intends to convey a sense
of empty space between the ‘agreement’ and ‘disagreement’
categories.
ggplot(df, aes(y = country, fill = satisfactionWithNeutral)) +
geom_bar(position = position_fill(reverse = TRUE)) +
guides(fill = guide_legend(nrow = 1)) +
scale_x_continuous(labels = dollar_format(suffix=" %",prefix="", scale = 100)) +
scale_fill_ul("redGreen", neutralColor = "lajt") +
theme_ul(legend.key = element_rect(color = ul_color("antracit"),
fill = "transparent"),
plot.background.fill = ul_color("lajt")) +
labs(fill = element_blank()) +
labs(x = element_blank(),
y = element_blank(),
title = "Lorem ipsum dolor sit amet",
caption = "Data source: this is all fake data.",
subtitle = "Eiusmod tempor incididunt ut labore et dolore magna.",
fill = "Agreement")
ggplot(df, aes(y = country, fill = satisfactionWithNeutral)) +
geom_bar(position = position_fill(reverse = TRUE), color = ul_color("antracit")) +
guides(fill = guide_legend(nrow = 1)) +
scale_x_continuous(labels = dollar_format(suffix=" %",prefix="", scale = 100)) +
scale_fill_ul("redGreen", neutralColor = "lajt") +
theme_ul(plot.background.fill = ul_color("lajt")) +
labs(fill = element_blank()) +
labs(x = element_blank(),
y = element_blank(),
title = "Lorem ipsum dolor sit amet",
caption = "Data source: this is all fake data.",
subtitle = "Eiusmod tempor incididunt ut labore et dolore magna.",
fill = "Agreement")
Note: A better visualisation of such data can be
achieved by using the diverging bar charts.Here, the stacks diverge from
a central baseline in opposite directions. One advantage of this chart
is that the sentiments are clearly presented. This works well if your
audience is most interested in the total sentiment of each side and not
necessarily comparisons between each individual component. Such charts
can be (with some data manipulation) generated using the
ggplot2 package (or use sjPlot::plot_likert or
likert::likert). However, if you place the neutral category
in the middle of the chart along the vertical baseline, there will be a
shift between the two groups, and it will appear that the neutral
responses are split between the two sentiments. To avoid this issue, you
can place a neutral category at the side of the chart (not covered
here).
When you want to apply the diverging scale to interval or ratio
variables, set the parameter discrete = FALSE.
basicChartCont + scale_color_ul(palette = "redGreen", discrete = FALSE)
You can also adjust the midpoint by altering the
midpoint parameter. However, bear in mind that this will
result in a symmetric color scale. Should you require an asymmetric
color scale (with the most extreme colors dictated by the minimum and
maximum values), you will need to specify the values
parameter. This parameter is a vector encompassing three values
(minimum, midpoint, maximum) set within an interval of 0 and 1.
Consequently, you will need to appropriately rescale the midpoint value.
For this task, the scales::rescale function proves to be
quite useful.
basicChartCont + scale_color_ul(palette = "redGreen", discrete = FALSE, midpoint = 70)
basicChartCont + scale_color_ul(palette = "redGreen",
discrete = FALSE,
values = c(0,
scales::rescale(70, to = c(0, 1), from = range(df$weight)),
1))
