R语言中如何利用ggplot2绘制qq图和boxplot图

矫情吗；* 2022-08-30 05:24 424阅读 0赞

### 文章目录 ###

*  绘制qq图
 *   *  函数介绍
     *   *  geom\_qq()
         *  参数介绍
         *  注意事项
     *  例子
 *  Using to explore the distribution of a variable
 *  绘制boxplot
 *   *  函数介绍
     *   *  参数介绍
     *  例子
     *  利用分位点绘制箱线图
 *  将qq图和箱线图进行融合
 *   *   *  函数介绍
         *  参数介绍
         *  注意事项
     *  例子

# 绘制qq图 #

在ggplot2中绘制qq图需要两步，`geom_qq()`将绘制样本分位点，`geom_qq_line()`将绘制标准正态线

## 函数介绍 ##

### geom\_qq() ###

geom_qq(
      mapping = NULL,
      data = NULL,
      geom = "point",
      position = "identity",
      ...,
      distribution = stats::qnorm,
      dparams = list(),
      na.rm = FALSE,
      show.legend = NA,
      inherit.aes = TRUE
    )

geom_qq_line(
      mapping = NULL,
      data = NULL,
      geom = "path",
      position = "identity",
      ...,
      distribution = stats::qnorm,
      dparams = list(),
      line.p = c(0.25, 0.75),
      fullrange = FALSE,
      na.rm = FALSE,
      show.legend = NA,
      inherit.aes = TRUE
    )

### 参数介绍 ###

> \*\*aes()\*\*中的映射参数必须包含sample，可选参数有group,x,y distribution  
> **Distribution** function to use, if x not specified  
> **dparams** Additional parameters passed on to distribution function.  
> **line.p** Vector of quantiles to use when fitting the Q-Q line, defaults defaults to c(.25, .75).  
> **fullrange** Should the q-q line span the full range of the plot, or just the data

### 注意事项 ###

\*\*aes()\*\*中的映射参数必须包含sample

## 例子 ##

# Using to explore the distribution of a variable #

ggplot(mtcars, aes(sample = mpg)) +
      stat_qq() +
      stat_qq_line()
    ggplot(mtcars, aes(sample = mpg, colour = factor(cyl))) +
      stat_qq() +
      stat_qq_line()

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzE4MDU1MTY3_size_16_color_FFFFFF_t_70]  
![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzE4MDU1MTY3_size_16_color_FFFFFF_t_70 1]

# 绘制boxplot #

## 函数介绍 ##

geom_boxplot(
      mapping = NULL,
      data = NULL,
      stat = "boxplot",
      position = "dodge2",
      ...,
      outlier.colour = NULL,
      outlier.color = NULL,
      outlier.fill = NULL,
      outlier.shape = 19,
      outlier.size = 1.5,
      outlier.stroke = 0.5,
      outlier.alpha = NULL,
      notch = FALSE,
      notchwidth = 0.5,
      varwidth = FALSE,
      na.rm = FALSE,
      orientation = NA,
      show.legend = NA,
      inherit.aes = TRUE
    )

### 参数介绍 ###

> **aes()可接收的参数有**：
> 
> x or y， 利用x将会是横向箱线图，y的是纵向
> 
> lower or xlower
> 
> upper or xupper
> 
> middle or xmiddle
> 
> ymin or xmin
> 
> ymax or xmax
> 
> alpha
> 
> colour
> 
> fill
> 
> group
> 
> linetype
> 
> shape
> 
> size
> 
> weight
> 
> **notch** If FALSE (default) make a standard box plot. If TRUE, make a notched box plot. Notches are used to compare groups; if the notches  
> of two boxes do not overlap, this suggests that the medians are  
> significantly different.
> 
> **notchwidth** For a notched box plot, width of the notch relative to the body (defaults to notchwidth = 0.5).
> 
> **varwidth** If FALSE (default) make a standard box plot. If TRUE, boxes are drawn with widths proportional to the square-roots of the  
> number of observations in the groups (possibly weighted, using the  
> weight aesthetic).

## 例子 ##

p <- ggplot(mpg, aes(x=class, y=hwy))
    p + geom_boxplot()

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzE4MDU1MTY3_size_16_color_FFFFFF_t_70 2]

ggplot(mpg, aes(x=hwy, y=class)) + geom_boxplot()

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzE4MDU1MTY3_size_16_color_FFFFFF_t_70 3]

p <- ggplot(mpg, aes(x=class, y=hwy))
    p + geom_boxplot(notch = TRUE,varwidth = TRUE,fill = "white", colour = "#3366FF")

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzE4MDU1MTY3_size_16_color_FFFFFF_t_70 4]

ggplot(diamonds, aes(carat, price)) +
      geom_boxplot(aes(group = cut_width(carat, 0.25)))

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzE4MDU1MTY3_size_16_color_FFFFFF_t_70 5]

p <- ggplot(mpg, aes(x=class, y=hwy))
    p + geom_boxplot(outlier.shape = NA) + geom_jitter(width = 0.2)

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzE4MDU1MTY3_size_16_color_FFFFFF_t_70 6]

## 利用分位点绘制箱线图 ##

y <- rnorm(100)
    df <- data.frame(
      x = 1,
      y0 = min(y),
      y25 = quantile(y, 0.25),
      y50 = median(y),
      y75 = quantile(y, 0.75),
      y100 = max(y)
    )
    ggplot(df, aes(x)) +
      geom_boxplot(
        aes(ymin = y0, lower = y25, middle = y50, upper = y75, ymax = y100),
        stat = "identity"
      )

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzE4MDU1MTY3_size_16_color_FFFFFF_t_70 7]

# 将qq图和箱线图进行融合 #

### 函数介绍 ###

该函数是来自于`qqboxplot`包，因此使用前需要安装

geom_qqboxplot(
      mapping = NULL,
      data = NULL,
      stat = "qqboxplot",
      position = "dodge2",
      ...,
      outlier.colour = NULL,
      outlier.color = NULL,
      outlier.fill = NULL,
      outlier.shape = 19,
      outlier.size = 1.5,
      outlier.stroke = 0.5,
      outlier.alpha = NULL,
      notch = FALSE,
      notchwidth = 0.5,
      varwidth = FALSE,
      na.rm = FALSE,
      show.legend = NA,
      inherit.aes = TRUE
    )

### 参数介绍 ###

> 大部分参数和geom\_qq()和geom\_boxplot()中的参数含义相同  
> **reference\_dist** 表示参数比较的标准分布名称，如果有参数需要有dparams  
> **compdata** 用于比较的标准样本数据，是个向量

### 注意事项 ###

aes()函数中的y不可缺

## 例子 ##

library(dplyr)
    library(ggplot2)
    library(qqboxplot)
    
    simulated_data=tibble(y=c(rnorm(1000, mean=2), rt(1000, 16), rt(500, 4), 
                              rt(1000, 8), rt(1000, 32)),
                          group=c(rep("normal, mean=2", 1000), 
                                  rep("t distribution, df=16", 1000), 
                                  rep("t distribution, df=4", 500), 
                                  rep("t distribution, df=8", 1000), 
                                  rep("t distribution, df=32", 1000)))
    p <- ggplot2::ggplot(simulated_data, ggplot2::aes(factor(group,
                                                             levels=c("normal, mean=2", "t distribution, df=32", "t distribution, df=16",
                                                                      "t distribution, df=8", "t distribution, df=4")), y=y))
    p + geom_qqboxplot()
    p + geom_qqboxplot(reference_dist = "norm")
    
    
    p + geom_qqboxplot(compdata = comparison_dataset)

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzE4MDU1MTY3_size_16_color_FFFFFF_t_70 8]  
![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzE4MDU1MTY3_size_16_color_FFFFFF_t_70 9]  
![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzE4MDU1MTY3_size_16_color_FFFFFF_t_70 10]

[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzE4MDU1MTY3_size_16_color_FFFFFF_t_70]: /images/20220829/c56b2fe6eb444a25844b6dd20e7efff6.png
[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzE4MDU1MTY3_size_16_color_FFFFFF_t_70 1]: /images/20220829/a88b92a9acf04292bdf5bc3b42959755.png
[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzE4MDU1MTY3_size_16_color_FFFFFF_t_70 2]: /images/20220829/9a9b239f648c44b9bee65e70615c38e7.png
[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzE4MDU1MTY3_size_16_color_FFFFFF_t_70 3]: /images/20220829/d15f54c74cac4b3490394867c050b138.png
[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzE4MDU1MTY3_size_16_color_FFFFFF_t_70 4]: /images/20220829/b3a18e9c5108491da3f10d27da95e4ee.png
[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzE4MDU1MTY3_size_16_color_FFFFFF_t_70 5]: /images/20220829/a0ce3a8545604d88ab65acebfd645be6.png
[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzE4MDU1MTY3_size_16_color_FFFFFF_t_70 6]: /images/20220829/061ae7191a1f4981b9058bb38e8cf7b9.png
[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzE4MDU1MTY3_size_16_color_FFFFFF_t_70 7]: /images/20220829/81efd782b0344957b57396221f41241e.png
[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzE4MDU1MTY3_size_16_color_FFFFFF_t_70 8]: /images/20220829/870a7d21e1cb44c7a01c59f434d81f83.png
[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzE4MDU1MTY3_size_16_color_FFFFFF_t_70 9]: /images/20220829/199d597700e84885960204a8977c0514.png
[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzE4MDU1MTY3_size_16_color_FFFFFF_t_70 10]: /images/20220829/d68dc3942d154968896f42006791169c.png