A ggplot color palette featuring the best band in the world: Pearl Jam. I used all the 11 studio albums covers from the band to compile a color palette from each album the band has realeased. I used studio albums rather than compilations or redux albums but later updates will consider all covers art work.
The realeased version of PeaRlJam could be installed by running the following command:
remotes::install_github("jorgeluis8ar/PeaRlJam")and loaded with:
library(PeaRlJam)I extract the colors out of the album's covers by using two packages. The first is colorfindr. The package allows to read and abstract the image information and stored by using the function get_colors. This information is then passed trough the function make_palette and we obtain our final colors. Note that I only use 15 colors and that the function have other parameters to fine tune your colors. I the use the package purrr and use the fuction map to help me create a list of the colors for all my album covers. The final colors are in HEX format, but the get_colors function allows the user to abstract other color schemes.
images <- list.files(path = directory) %>% paste0(directory,'/',.)
list_colors <- map(images,function(x) get_colors(img = x) %>% make_palette(n = 15))For further information on color extraction see this geniully awesome repo.
I explore firsly the mpg dataset to analyze the City Miles per Gallon (CMPG) comparison across engine displacement using our first color palette : Backspacer album cover. The following code is used to replicate the graph:
ggplot(mpg,aes(x = displ,y = cty, color = class)) + geom_point(size = 3) +
scale_color_pearl(palette = "Backspacer") +
labs(x = "Engine displacement (Ltrs)",
y = "City miles per gallon (CMPG)",
color = "Type of car",
title = "CMPG comparisson across engine displacement",
caption = "Color palettes based on PeaRlJam Package",
subtitle = "PeaRlJam color palette: Backspacer")+
theme_bw() + theme(plot.title = element_text(hjust = 0.5,size = 13),
plot.subtitle = element_text(hjust = 0.5,size = 13),
axis.title = element_text(size = 12),
legend.text = element_text(size = 10),
legend.title = element_text(size = 11),
legend.box="vertical",
legend.margin=margin())
Here I change the palette to see the pros and cons of each one. In this case I use Pearl Jam album cover. The following code is used to replicate the graph:
ggplot(mpg,aes(x = displ,y = cty, color = class)) + geom_point(size = 3) +
scale_color_pearl(palette = "Pearl Jam") +
labs(x = "Engine displacement (Ltrs)",
y = "City miles per gallon (CMPG)",
color = "Type of car",
title = "CMPG comparisson across engine displacement",
caption = "Color palettes based on PeaRlJam Package",
subtitle = "PeaRlJam color palette: Pearl Jam")+
theme_bw() + theme(plot.title = element_text(hjust = 0.5,size = 13),
plot.subtitle = element_text(hjust = 0.5,size = 13),
axis.title = element_text(size = 12),
legend.text = element_text(size = 10),
legend.title = element_text(size = 11),
legend.box="vertical",
legend.margin=margin())
We can now use the palettes will the fill option. Using the mpg database and the No Code album cover it's posible to produce the following graph:
ggplot(mpg, aes(manufacturer, fill = manufacturer)) +
geom_bar(color = "black", size = 0.25) + scale_fill_pearl(palette = "No Code") +
labs(x = "Car manufacturer",
y = "Count of observations",
color = "Manufacturer",
title = "Observations per car manufacturer",
caption = "Color palettes based on PeaRlJam Package",
subtitle = "PeaRlJam color palette: No Code")+
theme_bw() + theme(plot.title = element_text(hjust = 0.5,size = 13),
plot.subtitle = element_text(hjust = 0.5,size = 13),
axis.title = element_text(size = 12),
legend.text = element_text(size = 10),
legend.title = element_text(size = 11),
# legend.position = "bottom",
legend.box="vertical",
legend.margin=margin())
The same graph with a different palette. In this case I use the Yield album cover to reproduce the previous graph:
ggplot(mpg, aes(manufacturer, fill = manufacturer)) +
geom_bar(color = "black", size = 0.25) + scale_fill_pearl(palette = "Yield") +
labs(x = "Car manufacturer",
y = "Count of observations",
color = "Manufacturer",
title = "Observations per car manufacturer",
caption = "Color palettes based on PeaRlJam Package",
subtitle = "PeaRlJam color palette: Yield")+
theme_bw() + theme(plot.title = element_text(hjust = 0.5,size = 13),
plot.subtitle = element_text(hjust = 0.5,size = 13),
axis.title = element_text(size = 12),
legend.text = element_text(size = 10),
legend.title = element_text(size = 11),
# legend.position = "bottom",
legend.box="vertical",
legend.margin=margin())
For this section I use the famous and renowed iris database. The following graphs use the graphs Gigaton (album cover) and Lightning Bolt (album cover) palettes respectevely.
ggplot(data = iris,aes(x = Sepal.Length, y =Sepal.Width, color = Species)) + geom_point(size = 3)+
scale_color_pearl(discrete = T,palette = "Gigaton") +
labs(x = "Sepal Length",
y = "Sepal Width",
color = "Specie",
title = "Relationship between Sepal length and Sepal width",
caption = "Color palettes based on PeaRlJam Package",
subtitle = "PeaRlJam color palette: Gigaton")+
theme_bw() + theme(plot.title = element_text(hjust = 0.5,size = 13),
plot.subtitle = element_text(hjust = 0.5,size = 13),
axis.title = element_text(size = 12),
legend.text = element_text(size = 10),
legend.title = element_text(size = 11),
legend.box="vertical",
legend.margin=margin())
ggplot(data = iris,aes(x = Sepal.Length, y =Sepal.Width, color = Species)) + geom_point(size = 3)+
scale_color_pearl(discrete = T,palette = "Lightning Bolt") +
labs(x = "Sepal Length",
y = "Sepal Width",
color = "Specie",
title = "Relationship between Sepal length and Sepal width",
caption = "Color palettes based on PeaRlJam Package",
subtitle = "PeaRlJam color palette: Lightning Bolt")+
theme_bw() + theme(plot.title = element_text(hjust = 0.5,size = 13),
plot.subtitle = element_text(hjust = 0.5,size = 13),
axis.title = element_text(size = 12),
legend.text = element_text(size = 10),
legend.title = element_text(size = 11),
legend.box="vertical",
legend.margin=margin())
The following tables resumes all the posible palettes the package PeaRlJam takes into account.
| Album Name | Palette Name | Number of colors | Colors generated with | Link |
|---|---|---|---|---|
| Gigaton | Gigaton | 15 | function get_colors + make_palette |
Album's page |
| Lightning Bolt | Lightning Bolt | 15 | function get_colors + make_palette |
Album's page |
| Backspacer | Backspacer | 15 | function get_colors + make_palette |
Album's page |
| Pearl Jam | Pearl Jam | 15 | function get_colors + make_palette |
Album's page |
| Riot Act | Riot Act | 15 | function get_colors + make_palette |
Album's page |
| Binaural | Binaural | 15 | function get_colors + make_palette |
Album's page |
| Yield | Yield | 15 | function get_colors + make_palette |
Album's page |
| No Code | No Code | 15 | function get_colors + make_palette |
Album's page |
| Vitalogy | Vitalogy | 15 | function get_colors + make_palette |
Album's page |
| Vs. | Vs. | 15 | function get_colors + make_palette |
Album's page |
| Ten | Ten | 15 | function get_colors + make_palette |
Album's page |
There are several extensios to this type of packages in R. But I mainly drew inspiration on the taylorRswift package by asteves. TO my extent this is the first ever packages to use Pearl Jam's album cover art to create color palette extensions. Related packages are:
- metallicaRt by johnmackintosh.
- rockthemes by johnmackintosh.
- nycpalettes by kellycotton.
- shakiRa by camartinezbu.