qplot()函数

一年前就听说过ggplot,很多人都说ggplot强大,ggplot无所不能,从今天开始就让我们一起来见证一下这个神奇的R包。

首先要加载ggplot2:

1 if(!suppressWarnings(require('ggplot2'))){
2 install.packages('ggplot2')
3 require('ggplot2')
4 }

先简单介绍一下diamonds数据集,diamonds数据集包含了约54000颗钻石的价格和质量信息。有

克拉重量(carat),切工(cut),颜色(color),净度(clarity)——反应钻石质量的四个'C'

深度(depth),钻面宽度(table),x , y, z——五个物理指标

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" alt="" />

但这个数据集没有经过很好的整理,在展示钻石一些有趣的关系时,会显示出一些质量问题。所以同时使用另一个数据集dsmall:它是一个容量为100的随机样本。

1 set.seed(1410)#让样本可重复
2 dsmall<-diamonds[sample(nrow(diamonds),100),]
3 dsmall[1:5,]

(一)qplot(quick plot)语法:

qplot(x, y = NULL, ..., data, facets = NULL, 
      margins = FALSE,geom = "auto", stat = list(NULL),
      position = list(NULL), xlim = c(NA,NA), ylim = c(NA, NA), log = "", main = NULL,
      xlab = deparse(substitute(x)),
      ylab = deparse(substitute(y)), asp = NA)
注解:
x,y: 分别表示横坐标的值和纵坐标的值,比如书上直接用carat代表的x,price代表的y
... 为每个图层指定其他图形装饰属性,如颜色(colour)、形状(shape)、大小(size)等。
书上的colour=color,shape=cut,alpha=I(1/10)等
data: 数据集,比如书上用的data=diamonds
facets:图形/数据的分面,按数据进行分类,每一类做成一个图形,效果一页多图,默认一个图形
margins: 是否显示图形的边缘,默认不显示
geom: 图形的几何类型,如
geom='point'绘制散点图.设置了x和y时,默认为散点图
geom='smooth'将拟合一条平滑曲线(基于loess,gam,lm,rlm),图形展示了曲线和标准误(不想会标准误,se=FALSE)
geom='boxplot'可以绘制箱线胡须图,概括点的分布情况 x为属性变量,y为连续变量
geom='path'geom='line'绘制线条图
geom='histogram'绘制直方图,若有x参数时,默认为直方图
geom='freqploy'绘制频率多边形
geom='density'绘制密度图
geom='bar'绘制条形图
stat: 将数据统计与图形结合 (stat-statistic简写)
position: 调整图形位置
xlim与ylim: 指定x轴和y轴的范围
log: 横纵坐标对数转换
main: 添加标题
xlab与ylab: 添加x轴和y轴标签
(二)例子
钻石重量(carat)与价格(price)的散点图
钻石重量(carat)与体积(x*y*z)的散点图
钻石重量的对数【log(carat)】与价格的对数【log(price)】的散点图
 qplot(carat,price,data = diamonds)
qplot(carat,x*y*z,data = diamonds)
qplot(log(carat),log(price),data = diamonds)

aaarticlea/png;base64,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" alt="" />     aaarticlea/png;base64,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" alt="" />

aaarticlea/png;base64,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" alt="" />

 qplot(carat,price,data = dsmall,colour=color)#利用颜色分类
qplot(carat,price,data = dsmall,shape=cut)利用形状分类
qplot(carat,price,data = diamonds,alpha=I(/))
qplot(carat,price,data = diamonds,alpha=I(/))#alpha 越小越透明

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" 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参数调整:method和span
method='loess' 默认平滑算法,平滑程度由span参数(从0【很不平滑】到1【很平滑】)控制。适用于数据小于1000
method='gam' formula=y~s(x)基于mgcv包。对于大数据则用formula=y~s(x,bs='cs'),这是数据量超1000时默认选项
method='lm' formula=y~poly(x,2)或用formula=y~ns(x,2)基于splines包
method='rlm' 基于MASS包,使得结果对异常值不太敏感
 qplot(carat,price,data = dsmall,geom = c('point','smooth'),
method='loess',span=0.3)#绘制散点图+平滑曲线
 library(mgcv)
qplot(carat,price,data = diamonds,geom = c('point','smooth'),
method='gam',formula = y ~ s(x),span=0.8)
qplot(carat,price,data = diamonds,geom = c('point','smooth'),
method='gam',formula = y ~ s(x,bs='cs'),span=0.2)
 library(splines)
qplot(carat,price,data = dsmall,geom = c('point','smooth'),
method='lm',formula=y~poly(x,),span=0.5)
qplot(carat,price,data = dsmall,geom = c('point','smooth'),
method='lm',formula=y~ns(x,),span=)
  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alt="" /> 箱线图(geom='boxplot')和扰动点图(geom='jitter')
箱线图,用colour控制外框线的颜色,用fill填充颜色,用size调节线的粗细
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alt="" /> 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alt="" />
直方图和密度曲线图
直方图: binwidth参数通过设定组距来调节平滑度(切分位置同样可以通过参数breaks参数进行显示指定)
密度曲线图:adjust参数控制了曲线的平滑程度(adjust取值越大,曲线越平滑)
 qplot(carat,data = diamonds,geom = 'histogram',binwidth=0.1,
xlim = c(,),fill=color)
qplot(carat,data = diamonds,geom = 'density',binwidth=0.01,
xlim = c(,),fill=color)

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alt="" 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alt="" />

条形图

 qplot(color,data = diamonds,geom = 'bar',weight=carat)+
scale_y_continuous('carat')

aaarticlea/png;base64,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" alt="" />

时间序列的线条图(将点从左到右进行连接)和路径图(按照点在数据集中的顺序对其进行连接)

 a<-economics
a[:,]

aaarticlea/png;base64,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" alt="" />

 qplot(date,unemploy/pop,data = economics,geom = 'line')
#显示了失业率的变化
qplot(date,uempmed,data = economics,geom = 'line')
#失业星期数的中位数

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alt="" />

 

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