copy from https://cran.rproject.org/web/packages/ggsci/vignettes/ggsci.html
https://mp.weixin.qq.com/s/Y3H6CCgIiymiOfaQ8Unb7w
1 Introduction
My eyes were finally opened and I understood nature.
I learned at the same time to love it.
— Claude Monet
ggsci offers a collection of high-quality color palettes inspired by colors used in scientific journals, data visualization libraries, science fiction movies, and TV shows. The color palettes in ggsci are available as ggplot2scales.
(ggsci是ggplot的主题包，它提供一系列高质量的调色板，其灵感来自于科学期刊、数据可视化库、科幻电影和电视节目中使用的颜色。ggsci中的调色板可以作为ggplot2 scales使用。)
For all the color palettes, the corresponding scales are named as:
(所有有调色板通用格式为:)
scale_color_palname()
scale_fill_palname()
We also provided aliases, such as scale_colour_palname() for scale_color_palname().
使用的时候将palname替换为调色板的名称即可。
目前ggsci一共含有18中配色方案：
2 Discrete Color Palettes
We will use scatterplots with smooth curves, and bar plots to demonstrate the discrete color palettes in ggsci.
library("ggsci")
library("ggplot2")
library("gridExtra")
data("diamonds")
p1 = ggplot(subset(diamonds, carat >= 2.2), aes(x = table, y = price, colour = cut)) +geom_point(alpha = 0.7) + geom_smooth(method = "loess", alpha = 0.05, size = 1, span = 1) +theme_bw()
p2 = ggplot(subset(diamonds, carat > 2.2 & depth > 55 & depth < 70), aes(x = depth, fill = cut)) +geom_histogram(colour = "black", binwidth = 1, position = "dodge") + theme_bw()
2.1 NPG
The NPG palette is inspired by the plots in the journals published by Nature Publishing Group:
(NPC调色板的灵感来源于NATURE)
p1_npg = p1 + scale_color_npg()
p2_npg = p2 + scale_fill_npg()
grid.arrange(p1_npg, p2_npg, ncol = 2)
2.2 AAAS
The AAAS palette is inspired by the plots in the journals published by American Association for the Advancement of Science:
(AAAS 调色板的灵感来源于Science)
p1_aaas = p1 + scale_color_aaas()
p2_aaas = p2 + scale_fill_aaas()
grid.arrange(p1_aaas, p2_aaas, ncol = 2)
2.3 NEJM
The NEJM palette is inspired by the plots in The New England Journal of Medicine:
(NEJM调色板的灵感来源于新英格兰医学杂志)
p1_nejm = p1 + scale_color_nejm()
p2_nejm = p2 + scale_fill_nejm()
grid.arrange(p1_nejm, p2_nejm, ncol = 2)
2.4 Lancet
The Lancet palette is inspired by the plots in Lancet journals, such as Lancet Oncology:
(Lancet调色板的灵感来源于柳叶刀杂志)
p1_lancet = p1 + scale_color_lancet()
p2_lancet = p2 + scale_fill_lancet()
grid.arrange(p1_lancet, p2_lancet, ncol = 2)
2.5 JAMA
The JAMA palette is inspired by the plots in The Journal of the American Medical Association:
(JAMA调色板的灵感来源于美国医学协会杂志)
p1_jama = p1 + scale_color_jama()
p2_jama = p2 + scale_fill_jama()
grid.arrange(p1_jama, p2_jama, ncol = 2)
2.6 JCO
The JCO palette is inspired by the the plots in Journal of Clinical Oncology:
（JCO调色板的灵感来源于临床肿瘤杂志）
p1_jco = p1 + scale_color_jco()
p2_jco = p2 + scale_fill_jco()
grid.arrange(p1_jco, p2_jco, ncol = 2)
2.7 UCSCGB
The UCSCGB palette is from the colors used by UCSC Genome Browser for representing chromosomes. This palette has been intensively used in visualizations produced by Circos.
（UCSCGB调色板的灵感来源于UCSC 基因组浏览器，用于表示染色体的颜色。）
p1_ucscgb = p1 + scale_color_ucscgb()
p2_ucscgb = p2 + scale_fill_ucscgb()
grid.arrange(p1_ucscgb, p2_ucscgb, ncol = 2)
2.8 D3
The D3 palette is from the categorical colors used by D3.js (version 3.x and before). There are four palette types (category10, category20, category20b, category20c) available.
D3调色板的灵感来源于D3.js使用的分类颜色。有四种调色板类型(category10、category20、category20b、category20c)。
p1_d3 = p1 + scale_color_d3()
p2_d3 = p2 + scale_fill_d3()
grid.arrange(p1_d3, p2_d3, ncol = 2)
2.9 LocusZoom
The LocusZoom palette is based on the colors used by LocusZoom.
LocusZoom调色板的灵感来源于LocusZoom网站，其提供GWAS结果的快速可视化。
p1_locuszoom = p1 + scale_color_locuszoom()
p2_locuszoom = p2 + scale_fill_locuszoom()
grid.arrange(p1_locuszoom, p2_locuszoom, ncol = 2)
2.10 IGV
The IGV palette is from the colors used by Integrative Genomics Viewer for representing chromosomes. There are two palette types (default, alternating) available.
IGV调色板来自整合基因组查看器用于表示染色体的颜色。
p1_igv_default = p1 + scale_color_igv()
p2_igv_default = p2 + scale_fill_igv()
grid.arrange(p1_igv_default, p2_igv_default, ncol = 2)
2.11 UChicago
The UChicago palette is based on the colors used by the default, light, dark) available.
UChicago调色板的灵感来源于芝加哥大学
p1_uchicago = p1 + scale_color_uchicago()
p2_uchicago = p2 + scale_fill_uchicago()
grid.arrange(p1_uchicago, p2_uchicago, ncol = 2)
2.12 Star Trek
This palette is inspired by the (uniform) colors in Star Trek:
StarTrek调色板的灵感来源于《星际迷航》
p1_startrek = p1 + scale_color_startrek()
p2_startrek = p2 + scale_fill_startrek()
grid.arrange(p1_startrek, p2_startrek, ncol = 2)
2.13 Tron Legacy
This palette is inspired by the colors used in Tron Legacy. It is suitable for displaying data when using a dark theme:
TronLegacy调色板的灵感来源于《创战记》，它适用于在使用暗主题时显示数据。
p1_tron = p1 + theme_dark() + theme(
panel.background = element_rect(fill = "#2D2D2D"),
legend.key = element_rect(fill = "#2D2D2D")) +
scale_color_tron()
p2_tron = p2 + theme_dark() + theme(
panel.background = element_rect(fill = "#2D2D2D")) +
scale_fill_tron()
grid.arrange(p1_tron, p2_tron, ncol = 2)
2.14 Futurama
This palette is inspired by the colors used in the TV show Futurama:
Futurama调色板的灵感来源于电视《Futurama》
p1_futurama = p1 + scale_color_futurama()
p2_futurama = p2 + scale_fill_futurama()
grid.arrange(p1_futurama, p2_futurama, ncol = 2)
2.15 Rick and Morty
This palette is inspired by the colors used in the TV show Rick and Morty:
Rick and Morty调色板的灵感来源于电视《 Rick and Morty》
p1_rickandmorty = p1 + scale_color_rickandmorty()
p2_rickandmorty = p2 + scale_fill_rickandmorty()
grid.arrange(p1_rickandmorty, p2_rickandmorty, ncol = 2)
2.16 The Simpsons
This palette is inspired by the colors used in the TV show The Simpsons:
TheSimpsons调色板的灵感来源于电视《辛普森一家》
p1_simpsons = p1 + scale_color_simpsons()
p2_simpsons = p2 + scale_fill_simpsons()
grid.arrange(p1_simpsons, p2_simpsons, ncol = 2)
3 Continuous Color Palettes
We will use a correlation matrix visualization (a special type of heatmap) to demonstrate the continuous color palettes in ggsci.
library("reshape2")
data("mtcars")
cor = cor(unname(cbind(mtcars, mtcars, mtcars, mtcars)))
cor_melt = melt(cor)
p3 = ggplot(cor_melt,
aes(x = Var1, y = Var2, fill = value)) +
geom_tile(colour = "black", size = 0.3) +
theme_bw() +
theme(axis.title.x = element_blank(),
axis.title.y = element_blank())
3.1 GSEA
The GSEA palette (continuous) is inspired by the heatmaps generated by GSEA GenePattern.
p3_gsea = p3 + scale_fill_gsea()
p3_gsea_inv = p3 + scale_fill_gsea(reverse = TRUE)
grid.arrange(p3_gsea, p3_gsea_inv, ncol = 2)
3.2 Material Design
The
We generate a random matrix first:
library("reshape2")
set.seed(42)
k = 9
x = diag(k)
x[upper.tri(x)] = runif(sum(1:(k - 1)), 0, 1)
x_melt = melt(x)
p4 = ggplot(x_melt, aes(x = Var1, y = Var2, fill = value)) +
geom_tile(colour = "black", size = 0.3) +
scale_x_continuous(expand = c(0, 0)) +
scale_y_continuous(expand = c(0, 0)) +
theme_bw() + theme(
legend.position = "none", plot.background = element_blank(),
axis.line = element_blank(), axis.ticks = element_blank(),
axis.text.x = element_blank(), axis.text.y = element_blank(),
axis.title.x = element_blank(), axis.title.y = element_blank(),
panel.background = element_blank(), panel.border = element_blank(),
panel.grid.major = element_blank(), panel.grid.minor = element_blank())
Plot the matrix with the 19 material design color palettes:
grid.arrange(
p4 + scale_fill_material("red"), p4 + scale_fill_material("pink"),
p4 + scale_fill_material("purple"), p4 + scale_fill_material("deep-purple"),
p4 + scale_fill_material("indigo"), p4 + scale_fill_material("blue"),
p4 + scale_fill_material("light-blue"), p4 + scale_fill_material("cyan"),
p4 + scale_fill_material("teal"), p4 + scale_fill_material("green"),
p4 + scale_fill_material("light-green"), p4 + scale_fill_material("lime"),
p4 + scale_fill_material("yellow"), p4 + scale_fill_material("amber"),
p4 + scale_fill_material("orange"), p4 + scale_fill_material("deep-orange"),
p4 + scale_fill_material("brown"), p4 + scale_fill_material("grey"),
p4 + scale_fill_material("blue-grey"),
ncol = 6)
From the figure above, we can see that even though an identical matrix was visualized by all plots, some palettes are more preferrable than the others because our eyes are more sensitive to the changes of their saturation levels.
4 Non-ggplot2 Graphics
To apply the color palettes in ggsci to other graphics systems (such as base graphics and lattice graphics), simply use the palette generator functions in the table above. For example:
对于非ggplot2的图形系统，ggsci还提供把调色板的颜色单独提取成颜色代码。
# 从NPC调色板中提取9个颜色
mypal = pal_npg("nrc", alpha = 0.7)(9)
mypal
#系统给出的NPC调色板中的9个颜色为：
## [1] "#E64B35B2" "#4DBBD5B2" "#00A087B2" "#3C5488B2" "#F39B7FB2" "#8491B4B2"
## [7] "#91D1C2B2" "#DC0000B2" "#7E6148B2"
#具体的颜色展示为：
library("scales")
show_col(mypal)
You will be able to use the generated hex color codes for such graphics systems accordingly. The transparent level of the entire palette is easily adjustable via the argument "alpha" in every generator or scale function.
5 Discussion
Please note some of the palettes might not be the best choice for certain purposes, such as color-blind safe, photocopy safe, or print friendly. If you do have such considerations, you might want to check out color palettes like ColorBrewer and viridis.
The color palettes in this package are solely created for research purposes. The authors are not responsible for the usage of such palettes.