作者：Li_Yuhui
四川大学研究生在读
本文是ggplot2_可视化_速成的第5节
第2节：coor_xxx()坐标系变换
第3节：scale_xxx()标度调整
第4节：facet_xxx()分面系统
第6节：themes主题系统
Guides图例与增加坐标轴
图例函数：
guide_colorbar()/guide_colourbar()用于连续变量的图例guide_legend()用于离散变量的图例,也可以用于连续变量guides()将_colorbar和_legend嵌套进去，方便映射，如guides(fill = guide_colorbar())
可以在scale_xxx()标度中指定guide类型，guide = "colorbar"或guide = “legend”
常用公共参数：
| 作用对象 | 参数 | 描述 |
|---|---|---|
| 整个图例 | direction | 指定图例箱体排列方向，"horizontal"水平排列,或"vertical"垂直排列 |
| reverse | 逻辑值，是否翻转图例顺序,默认从小到大自上而下，翻转后从小到大自下而上 | |
| order | 为数字，表示给图例编号，方便多个图例排列 | |
| 图例标题 | title | 指定标题名称 |
| title.position | 标题相对图例箱体的位置， 水平图例为"left"或"right", 垂直图例为"top"或"bottom" | |
| title.hjust | 为数字，指定图例标题水平位置偏移 | |
| title.vjust | 为数字，指定图例标题垂直位置偏移 | |
| 图例刻度标签 | label | 为逻辑值，是否显示图例刻度标签 |
| label.position | 指定图例刻度标签相对箱体位置, 水平图例为"left"或"right", 垂直图例为"top"或"bottom" | |
| label.hjust | 为数字，指定图例刻度标签水平位置偏移 | |
| label.vjust | 为数字，指定图例刻度标签垂直位置偏移 | |
| default.unit | 表示指定箱体尺寸单位，用grid::unit() |
guide_colorbar
**_colorbar()参数: **
| 作用对象 | 参数 | 描述 |
|---|---|---|
| 图例箱体 | barwidth | 指定箱体宽度，为数字或grid::unit()指定,默认单位为mm |
| barheight | 指定箱体高度，为数字或grid::unit()指定 |
|
| nbin | 指定分箱数，数字越大则渐变约平缓 | |
| raster | 逻辑值，表示是否将图例以删格形式呈现，不常用,栅格数据 | |
| 箱体边框 | frame.colour | 表示指定箱体边框颜色，默认无边框 |
| frame.linetype | 表示指定箱体边框线型 | |
| frame.linewidth | 表示指定箱体边框线宽 | |
| 刻度线 | ticks | 逻辑值，表示是否显示刻度线 |
| ticks.colour | 指定刻度线颜色 | |
| ticks.linewidth | 指定刻度线线宽 | |
| draw.ulim | 逻辑值，表示是否显示最大值(upper)刻度线 | |
| draw.llim | 逻辑值，表示是否显示最小值(low)刻度线 |
library(ggplot2)
library(reshape2)
df <- melt(outer(1:4, 1:4), varnames = c("X1", "X2"))
p1 <- ggplot(df, aes(X1, X2)) + geom_tile(aes(fill = value))
p2 <- p1 + geom_point(aes(size = value))
p1 + scale_fill_continuous(guide = "colorbar") # 默认形式
p1 + guides(fill = guide_colorbar()) # 具体映射
p1 + scale_fill_distiller(palette = "YlGn", direction = 1) +
guides(fill = guide_colorbar(title = "值", nbin = 100, # 指定图例名称，水平放置,增加分箱数为100
barwidth = 0.5, barheight = 10,# 指定图例箱体尺寸，宽为0.5mm,高为10mm
ticks.colour = "red", # 更改刻度线颜色
frame.colour = "blue",frame.linewidth = 0.5, # 增加箱体边框
draw.ulim = TRUE, draw.llim = TRUE # 显示最大，最小刻度线
))
p2 + scale_fill_continuous(guide = "colorbar") + scale_size(guide = "legend") # 在标度中控制图例
p2 + guides(fill = "colorbar", size = "legend") # 与上面结果一样
p2 + scale_fill_continuous(guide = guide_colorbar(direction = "horizontal")) +
scale_size(guide = guide_legend(direction = "vertical")) # 更改图例方向
guide_legend
_legend()参数：
| 作用对象 | 参数 | 描述 |
|---|---|---|
| 箱体尺寸 | key.width | 指定单个箱体宽度, 为数字或grid::unit()指定, 默认单位为mm |
| key.height | 指定单个箱体高度, 为数字或grid::unit()指定 |
|
| 分箱排列 | nrow | 为数字，表示指定图例箱体排列行数 |
| ncol | 为数字，表示指定图例箱体排列列数 | |
| byrow | 逻辑值，表示图例箱体是否按行排列，默认FALSE按列排 |
library(ggplot2)
library(reshape2)
df <- melt(outer(1:4, 1:4), varnames = c("X1", "X2"))
p1 <- ggplot(df, aes(X1, X2)) + geom_tile(aes(fill = value))
p2 <- p1 + geom_point(aes(size = value))
p1 + scale_fill_continuous(guide = guide_legend()) # 连续标度中设置离散图例
p1 + scale_fill_distiller(type = "qual", palette = "Set3") +
guides(fill = guide_legend(title = "左", title.position = "left", # 指定图例名称为"左", 位置为箱体的左边
key.width = 5, key.height = 10, nrow = 2, ncol = 2, byrow = TRUE # 修改箱体尺寸，并矩形排列，按行排
))
p1 + guides(fill = guide_legend(
title.theme = element_text(size = 15, face = "italic", colour = "red", angle = 0)) # 在图例中修改图例主题，一般在主题函数内修改
)
p1 + scale_fill_continuous(breaks = c(5, 10, 15),
labels = paste("long", c(5, 10, 15)),
guide = guide_legend(
direction = "horizontal", # 水平排列箱体
title.position = "top", # 图例标题置于顶部
label.position = "bottom", # 图例刻度标签置于底部
label.hjust = 0.5, # 刻度标签水平位置偏移
label.vjust = 1, # 刻度标签垂直位置偏移
label.theme = element_text(angle = 90) # 图例主题中修改刻度标签角度
)
)
guides多个图例
guides多个图例：
guides()内部嵌套guide_legend()和guide_colorbar(),添加一个映射参数，如：
guides(
colour = guide_colourbar(order = 1),
shape = guide_legend(order = 2),
size = guide_legend(order = 3)
)
library(ggplot2)
dat <- data.frame(x = 1:5, y = 1:5, p = 1:5, q = factor(1:5), r = factor(1:5))
p <- ggplot(dat, aes(x, y, colour = p, size = q, shape = r)) + geom_point()
p
p + guides(colour = guide_colorbar(), size = guide_legend(), shape = guide_legend()) # 默认按参数顺序排列多个图例
p + scale_colour_continuous(guide = "colorbar") +
scale_size_discrete(guide = "legend") +
scale_shape(guide = "legend") +
guides(colour = "none") # 删除一个图例
# 设定多个图例
ggplot(mpg, aes(displ, cty)) +
geom_point(aes(size = hwy, colour = cyl, shape = drv)) +
guides(
colour = guide_colourbar(order = 1), # order指定图例排列顺序
shape = guide_legend(order = 2),
size = guide_legend(order = 3)
)
多图例合并
library(ggplot2)
# 图例合并：
## 多个不同标度图例合并：
### 当图例类型一致，图例标题一致时，会自动合并图例
dat <- data.frame(x = 1:5, y = 1:5, p = 1:5, q = factor(1:5), r = factor(1:5))
p <- ggplot(dat, aes(x, y, colour = p, size = q, shape = r)) + geom_point()
p + guides(colour = guide_legend("这是图例标题"), size = guide_legend("这是图例标题"), shape = guide_legend("这是图例标题")) +
theme(legend.position = "bottom") # 主题函数中调节图例位置
## 多种几何对象图例合并：
### 若都是同一个变量映射的，且标度类型一致，标度标题相同，标度values等长，给标度新增labels参数，labels相同，则会自动合并图例
state1 <- c(rep(c(rep("N", 7), rep("Y", 7)), 2))
year <- rep(c(2003:2009), 4)
group1 <- c(rep("C", 14), rep("E", 14))
group2 <- paste(state1, group1, sep = "")
beta <- c(0.16,0.15,0.08,0.08,0.18,0.48,0.14,0.19,0.00,0.00,0.04,0.08,0.27,0.03,0.11,0.12,0.09,0.09,0.10,0.19,0.16,0.00,0.11,0.07,0.08,0.09,0.19,0.10)
lcl <- c(0.13,0.12,0.05,0.05,0.12,0.35,0.06,0.13,0.00,0.00,0.01,0.04,0.20,0.00,0.09,0.09,0.06,0.06,0.07,0.15,0.11,0.00,0.07,0.03,0.05,0.06,0.15,0.06)
ucl <- c(0.20,0.20,0.13,0.14,0.27,0.61,0.28,0.27,0.00,1.00,0.16,0.16,0.36,0.82,0.14,0.15,0.13,0.13,0.15,0.23,0.21,0.00,0.15,0.14,0.12,0.12,0.23,0.16)
data <- data.frame(state1,year,group1,group2,beta,lcl,ucl)
ggplot(data = data,aes(x= year, y = beta, colour = group2, shape = group2)) +
geom_point(size = 4) +
geom_errorbar(aes(ymin = lcl, ymax = ucl), colour = "black", width = 0.5) +
scale_colour_manual(name = "Treatment & State",
labels = c("Control, Non-F", "Control, Flwr", "Exclosure, Non-F", "Exclosure, Flwr"),
values = c("blue", "red", "blue", "red")) +
scale_shape_manual(name = "Treatment & State",
labels = c("Control, Non-F", "Control, Flwr", "Exclosure, Non-F", "Exclosure, Flwr"),
values = c(19, 19, 17, 17))
### 映射变量相同，在标度labs函数中设置相同的标度名称
ggplot(iris) +
aes(x = Sepal.Length, y = Sepal.Width,
color = Species, linetype = Species, shape = Species) +
geom_line() +
geom_point() +
labs(color = "Guide name", linetype = "Guide name", shape = "Guide name")
### 下一个例子
x <- seq(0, 10, by = 0.2)
y1 <- sin(x)
y2 <- cos(x)
y3 <- cos(x + pi / 4)
y4 <- sin(x + pi / 4)
df1 <- data.frame(x, y = y1, Type = as.factor("sin"), Method = as.factor("method1"))
df2 <- data.frame(x, y = y2, Type = as.factor("cos"), Method = as.factor("method1"))
df3 <- data.frame(x, y = y3, Type = as.factor("cos"), Method = as.factor("method2"))
df4 <- data.frame(x, y = y4, Type = as.factor("sin"), Method = as.factor("method2"))
df.merged <- rbind(df1, df2, df3, df4)
y5 <- sin(x - pi / 4)
df5 <- data.frame(x, y = y5, Type = as.factor("sin"), Method = as.factor("method3"))
df.merged <- rbind(df1, df2, df3, df4, df5)
df.merged$int <- paste(df.merged$Type, df.merged$Method, sep=".") # 给数据源新增一列变量
ggplot(df.merged, aes(x, y, colour = int, linetype=int, shape=int)) +
geom_line() +
geom_point() +
scale_colour_discrete("") +
scale_linetype_manual("", values=c(1,2,1,2,3)) +
scale_shape_manual("", values=c(17,17,16,16,15))
新增坐标轴
所有的新增坐标轴都是基于现有坐标轴变换而来的
sec_axis(trans = NULL, name = waiver(), breaks = waiver(), labels = waiver())dup_axis(trans = ~., name = derive(), breaks = derive(), labels = derive())derive()
参数解释：
- trans 表示指定变换公式
- name 表示指定新增坐标轴的名称
- breaks 表示指定新增坐标轴刻度点位置
- labels 表示指定新增坐标轴刻度标签
- derive 表示继承现有坐标轴，基本上就是复制，没有变换关系，如果有变换关系，还是用
sec_axis()吧
library(ggplot2)
p <- ggplot(mtcars, aes(cyl, mpg)) +
geom_point()
p + scale_y_continuous(sec.axis = sec_axis(~.+10)) # 在标度函数中新增第2个y轴，变换关系为：原y轴 + 10
p + scale_y_continuous("英里/每加仑", sec.axis = sec_axis(~.+10, name = "公里/L")) # 新增y轴，轴名称为：公里每升，原y轴为:英里/加仑
p + scale_y_continuous(sec.axis = ~.^2) # 变换关系为：平方
p + scale_y_continuous(sec.axis = ~.^2 * 3 + 4*.)# 变换关系为：3*y^2 + 4*y
