透明标度
library(ggplot2)
diamonds1 <- diamonds[sample(nrow(diamonds),1000),]
##基本作图
p <- ggplot(diamonds1, aes(depth, price)) +
geom_point(aes(alpha = carat))
##更改透明标签的名字
p1 <- p + scale_alpha("new_legend")
##更改连续性透明的范围
p2 <- p + scale_alpha_continuous(range = c(0.4,0.8))
##离散型透明范围改变
p3 <- ggplot(diamonds1, aes(depth, price)) +
geom_point(aes(alpha = color)) + scale_alpha_manual("cccolr",
limits = levels(diamonds$color),
breaks = c("D","E","F","G","H"),
labels = c("D-d","E-e","F-f","G-g","H-h"),
values = c(0.15,0.2,0.3,0.4,0.5,0.6,0.75)
)
library(cowplot)
##
## Attaching package: 'cowplot'
## The following object is masked from 'package:ggplot2':
##
## ggsave
plot_grid(p,p1,p2,p3, ncol = 2)
颜色标度
颜色分为填充色(fill)和轮廓色(color)。所有的颜色都可以使用这两个来定义颜色 函数的常规参数 #### ColorBrewer配色系统 在介绍颜色标度之前，先介绍一个成熟的系统的颜色配色。这样好选择好看的颜色
library(RColorBrewer)
##展示所有的配色方案
display.brewer.all()
[图片上传中...(image-fe81f4-1554464628287-5)]
连续性颜色标度
mydata3 <- data.frame(
x = runif(100,0,100),
y = runif(100,0,100),
z = runif(100,0,100),
f = runif(100,0,100),
g = rep(LETTERS[1:5],each = 20)
)
p <- ggplot(mydata3, aes(x,y,color = z), shape = 19, size = 5) + geom_point()
##双色过渡渐变 scale_color_continuous() == scale_color_gradient()
p1 <- p + scale_color_continuous(name = "colors",##图例的名字
limits = c(0.5,150),##定义颜色分配范围
breaks = c(1,20,50,80), ##制定颜色的分配
labels = paste0(c(1,20,50,80), "G"), ##break的标签。但是长度要和breaks一样。
low = "blue", high = "red"
##na.value = "grey", ##定义缺失值的颜色
)
###三色过渡渐变
##三色渐变除了上面两色的参数就是多了一个颜色和中位颜色的设定
p2 <- p + scale_color_gradient2(low = "blue", mid = "black", high = "red", midpoint = median(mydata3$z)) ##函数提供了midpoint参数用来设置中点
###成熟的渐变颜色变化
p3 <- p + scale_color_distiller("distiller", ###改变图例的名字
type = "div", ##颜色使用的类型seq (连续性), div (发散性) or qual (定性)。这个可以不定义
palette = "RdYlGn", ##定义具体颜色配色
direction = -1, ##配色方案选择颜色的顺序，1位正向，-1位负向
values = c(0.2, 0.8)##制定颜色匹配的范围。例子中的代表整个数字的20%-80%进行标注颜色
)
plot_grid(p1,p2,p3, ncol = 3)
[图片上传中...(image-909c2d-1554464628286-1)]
离散型颜色标度
离散型标度一般用scale_color_discrete,由于定义颜色比较麻烦。用的少。一般离散型的都是用自己自定义颜色或者成熟的配色方案
p <- ggplot(mydata3, aes(x,y, color = g), shape = 19, size = 5) + geom_point()
p1 <- p + scale_color_discrete(name = "discrete",
limits = c("A","C","D","E"), ##定义颜色分类
breaks = c("A","D","E"),
labels = paste0(c("A","D","E"),"-W"),
l = 40, c = 30)
##成熟的配色方案
p2 <- p + scale_color_brewer(type = "seq", palette = 1, direction = -1)
p3 <- p + scale_colour_brewer(palette = "RdYlBu", direction = -1)
p4 <- p + scale_colour_brewer(palette = "Accent", direction = -1)
###自定义色盘。
p5 <- p + scale_colour_manual(
limits = LETTERS[1:4],
breaks = LETTERS[4:1],
labels = c("A_a","B_b","C_c","D_d"),
values = c("#F46D43","#D9F0D3","#1A9850","#006837")
)
plot_grid(p,p1,p2,p3,p4,p5, ncol = 2, nrow = 3)
## Warning: Removed 20 rows containing missing values (geom_point).
[图片上传中...(image-91e113-1554464628286-0)]
线和形状标度标度
ggplot里面有六种线条类型，其中分别为0=blank,1=solid,2=dashed,3=dotted,4=dotdash,5=longdash,6=twodash 一般而言linetype和shape都是自己手动自定的或者是离散的。没有连续性的线条
lct <- Sys.getlocale("LC_TIME")
Sys.setlocale("LC_TIME", "C")
## [1] "C"
mydata4 <- data.frame(
x = rep(seq(as.Date('2018-03-01'),as.Date('2018-03-10'),by = '1 day'),each = 5),
y = abs(rnorm(50,1000,500)),
z = rep(LETTERS[1:5],length = 50)
)
p <- ggplot(mydata4,aes(x,y)) +
geom_line(aes(linetype = z,colour = z),size = 1.5)
p1 <- p + scale_linetype_manual(
limits = LETTERS[1:5],
breaks = LETTERS[1:5],
labels = paste0(LETTERS[1:5],'_',letters[1:5]),
values = c('solid','dashed','dotted','dotdash','longdash')
)
plot_grid(p,p1,ncol = 1)
[图片上传中...(image-568667-1554464628287-4)]
##形状标度
p <- ggplot(mydata4,aes(x,y,colour = z)) +
geom_line(size = 1.5) +
geom_point(aes(shape = z),size = 5)
p2 <- p +scale_shape_manual(
limits = LETTERS[1:5],
breaks = LETTERS[1:5],
labels = paste0(LETTERS[1:5],'_',letters[1:5]),
values = c(0,1,2,5,6)
)
plot_grid(p,p2,ncol = 1)
[图片上传中...(image-e4a154-1554464628287-3)]
点的大小
mydata6 <- data.frame(
x = runif(100,1,100),
y = runif(100,1,100),
z = runif(100,1,100)
)
p <- ggplot(mydata6,aes(x,y,size = z)) +
geom_point()
p1 <- p + scale_radius()
##自定义大小
p2 <- p + scale_size_area(
limits = c(20,80),
breaks = seq(20,80,5),
labels = paste(seq(20,80,5),'$'))
plot_grid(p,p1,p2,ncol= 1)
## Warning: Removed 35 rows containing missing values (geom_point).
image.png
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