在part 1里我们了解了ggplot2的基本绘图功能qplot, 这次我们来看看另一个重要功能qplot
七个图形对象
- DATA FRAME 数据帧
- AESTHETIC MAPPINGS 外观图法
- GEOMS 几何对象
- FACETS 面板
- STAT 统计转换,比如bin, smooth
- SCALES ??
- COORDINATE SYSTEM 坐标系
ggplot2绘图分步显示
使用层来建立图形
- Plot the data 先对数据绘图
- Overlay a summary 再把数据概要覆盖在数据图形上面
- Metadata and annotation 最后是元数据和注释
qplot(displ, hwy, data=mpg, geom=c("point", "smooth"), facets=.~drv)
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分步解读画图步骤
>g <- ggplot(mpg,aes(displ, hwy))
> summary(g)
data: manufacturer, model, displ, year, cyl, trans, drv, cty, hwy, fl,
class [234x11]
mapping: x = ~displ, y = ~hwy
faceting: <ggproto object: Class FacetNull, Facet, gg>
compute_layout: function
draw_back: function
draw_front: function
draw_labels: function
draw_panels: function
finish_data: function
init_scales: function
map_data: function
params: list
setup_data: function
setup_params: function
shrink: TRUE
train_scales: function
vars: function
super: <ggproto object: Class FacetNull, Facet, gg>
从摘要中可以看出gg对象中包括:data(mpg), mapping(x,y), faceting(no)
> print(g)
# ggplot 不能用print打印gg对象
g + geom_point()
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# 增加平滑曲线
g + geom_point() + geom_smooth()
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# 改变平滑功能参数为线性回归
g + geom_point() + geom_smooth(method="lm")
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# 子面板分组绘图
g + geom_point() + geom_smooth(method="lm") + facet_grid(. ~ drv)
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增加注释
g + geom_point() + geom_smooth(method="lm") + facet_grid(.~drv) + ggtitle("Swirl Rules!")
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下面开始来改变图形的外观AESTHETIC, 设置散点的大小,颜色,透明度
g + geom_point(color="pink", size=4, alpha=1/2)
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按颜色分组,设置aes clor 到一个因子变量,实现不同颜色变量的分组显示
g + geom_point(aes(color = drv), size = 4, alpha = 1/2)
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设置标题和标签 (图略)
g + geom_point(aes(color = drv)) + labs(title="Swirl Rules!") + labs(x="Displacement", y="Hwy Mileage")
定制平滑线
g + geom_point(aes(color = drv),size=2,alpha=1/2) + geom_smooth(size=4,linetype=3,method="lm",se=FALSE)
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ggplot图层的复杂例子
> head(testdat)
x y
1 1 -1.13782391
2 2 0.57977757
3 3 -0.87085224
4 4 -0.39750827
5 5 0.08576791
6 6 0.92132965
# R原生画图
plot(myx, myy, type = "l", ylim = c(-3,3))
# ggplot 图层演示
g <- ggplot(testdat, aes(x = myx, y = myy))
# 对比原生绘图函数,离线点50,100会压缩整个图形
g + geom_line()
# 显示y轴数值范围,但是图形有中断
g + geom_line() + ylim(-3,3)
# 选择合适的坐标系,笛卡尔坐标,同时设置y轴范围
g + geom_line() + coord_cartesian(ylim=c(-3,3))
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继续以mpg数据集演示
> g <- ggplot(mpg,aes(x=displ,y=hwy,color=factor(year)))
...没有图形显示,因为没有定义图层
> g + geom_point()
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子面板分组显示
> g + geom_point() + facet_grid(drv ~ cyl, margins=TRUE)
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