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R可视化09|ggplot2-图层图形语法 (1)
"pythonic生物人"的第107篇分享
前面介绍了ggplot2的图层（layers），接下来几篇会系统较深入的介绍ggplot2图层图形语法（the Grammar of Graphics），不纠结某一种图的具体绘制方法，不纠结某一个参数的具体设置，这些都交给帮助文档，只求理解图背后的原理，一通百通。
本文目录
1、ggplot2图层图形语法益处
2、图层图形语法一张图组成
图层（layer）介绍
ggplot2绘图模板
3、数据集（Data）
4、图像属性（aes）
1、ggplot2图层图形语法益处
The layered grammar is based on Wilkinson’s grammar of graphics , but adds a number of enhancements that help it to be more expressive and fit seamlessly into the R environment. It also encourages the use of graphics customised to a particular problem, rather than relying on specific chart types. The grammar makes it easier for you to iteratively update a plot, changing a single feature at a time. The grammar is also useful because it suggests the high-level aspects of a plot that can be changed, giving you a framework to think about graphics, and hopefully shortening the distance from mind to paper.
2、图层图形语法一张图组成
A default dataset and set of mappings from variables to aesthetics【数据集（data）和图像属性（aes）】. One or more layers, each composed of a geometric object, a statistical transformation, a position adjustment, and optionally, a dataset and aesthetic mappings【一或多个图层（layers）：每个图层由数据集（data），图像属性（aes），统计变换（stat），几何对象（geom）和位置调整（position adjustment）组成】. One scale for each aesthetic mapping【标度（scale）】. A coordinate system【坐标系（coord）】. The facetting specification【分面（facet）】.
以上各部分体现在图中如下。
图层（layer）介绍
图层一般由以下五部分构成
> layer
function (
geom = NULL, #绘制什么图，例如散点图：geom = "point",
stat = NULL, #默认为“identity”， histograms和smoothers图时常用
data = NULL, #指定数据集
mapping = NULL, #aes()函数
position = NULL, #设置多个图之间相对位置，防折叠、设置堆叠等
..........
)
{...............
}
了解layer函数会帮助更好理解图层图形语法的底层，不过一般绘图时，不会直接使用layer函数，而是使用阉割版的geom_系列函数添加图层，举个栗子。
library('gridExtra')
library('ggplot2')
options(repr.plot.width = 6, repr.plot.height = 3, repr.plot.res = 300)
#创建一个画布，包含坐标轴和数据信息
p1 <- ggplot(mpg, aes(displ, hwy))
#geom_系列函数添加散点图（geom_point()图层
p2 <- p1 + geom_point()+ggtitle('By geom_point()')
#geom_point()是捷径，实际上后台调用layer()函数以创建一个散点图图层
p3 <- p1 + layer(
mapping = NULL,
data = NULL,
geom = "point", #指定散点图
stat = "identity",
position = "identity"
)+ggtitle('By layer()')
p4 <- grid.arrange(p2,p3,nrow = 1)
ggsave("scale11.png", p4, width = 6, height = 3)
可以看出效果一模一样。
ggplot2绘图模板
3、数据集（Data）
ggplot2中对干净数据定义为，variables in the columns and observations in the rows【每一列为一个变量，每一行为一个观测】， 可以通过控制数据集以突出想要突出展示的数据。
library(dplyr)
options(repr.plot.width = 4.5, repr.plot.height = 3.5, repr.plot.res = 300)
mod <- loess(hwy ~ displ, data = mpg)
grid <- data_frame(displ = seq(min(mpg$displ), max(mpg$displ), length = 50))
grid$hwy <- predict(mod, newdata = grid)#loess变换构建新data grid
std_resid <- resid(mod) / mod$s
outlier <- filter(mpg, abs(std_resid) > 2)#划分离散点
ggplot(mpg, aes(displ, hwy)) +
geom_point() + #添加散点图图形
geom_line(data = grid) + #拟合曲线
geom_text(data = outlier, aes(label = model))#离散点添加文本
4、图像属性（aes）
设置Colour, size, shape等等。
在ggplot和在图层中设置aes的区别
library(dplyr)
class <- mpg %>%
group_by(class) %>%
summarise(n = n(), hwy = mean(hwy))
#以下四种方式按class绘制散点图效果一致。
#在ggplot中设置aes
ggplot(mpg, aes(displ, hwy, colour = class)) +
geom_point()
ggplot(mpg, aes(displ, hwy)) +
geom_point(aes(colour = class))
#在图层中设置aes
ggplot(mpg, aes(displ)) +
geom_point(aes(y = hwy, colour = class))
ggplot(mpg) +
geom_point(aes(displ, hwy, colour = class))
**aes内和外设置参数的区别 **
options(repr.plot.width = 6, repr.plot.height = 3.5, repr.plot.res = 300)
p1 <- ggplot(mpg, aes(cty, hwy)) +
geom_point(colour = "darkblue")
#if you want override the default size or colour, put the value outside of aes().
p2 <- ggplot(mpg, aes(cty, hwy)) +
geom_point(aes(colour = "darkblue"))
# If you want appearance to be governed by a variable, put the specification inside aes()
p3 <- ggplot(mpg, aes(cty, hwy)) +
geom_point(aes(colour = "darkblue")) +
scale_colour_identity()
p4 <- ggplot(mpg, aes(displ, hwy)) +
geom_point() +
geom_smooth(aes(colour = "loess"), method = "loess", se = FALSE) +
geom_smooth(aes(colour = "lm"), method = "lm", se = FALSE) +
labs(colour = "Method")
p5 <- grid.arrange(p1,p2,p3,p4,nrow = 2)
ggsave("scale11.png", p4, width = 6, height = 3)
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