如何在箱线图上显示异常值

Question

如何在箱图中查看异常值的ID？

structure(list(pot = c(1L, 2L, 3L, 4L, 21L, 22L, 23L, 24L, 5L, 
6L, 7L, 8L, 25L, 26L, 27L, 28L, 9L, 10L, 11L, 12L, 29L, 30L, 
31L, 32L, 13L, 14L, 15L, 16L, 33L, 34L, 35L, 36L, 17L, 18L, 19L, 
20L, 37L, 38L, 39L, 40L, 41L, 42L, 43L, 44L, 61L, 62L, 63L, 64L, 
45L, 46L, 47L, 48L, 65L, 66L, 67L, 68L, 49L, 50L, 51L, 52L, 69L, 
70L, 71L, 72L, 53L, 54L, 55L, 56L, 73L, 74L, 75L, 76L, 57L, 58L, 
59L, 60L, 77L, 78L, 79L, 80L, 81L, 82L, 83L, 84L, 101L, 102L, 
103L, 104L, 85L, 86L, 87L, 88L, 105L, 106L, 107L, 108L, 89L, 
90L, 91L, 92L, 109L, 110L, 111L, 112L, 93L, 94L, 95L, 96L, 113L, 
114L, 115L, 116L, 97L, 98L, 99L, 100L, 117L, 118L, 119L, 120L, 
121L, 122L, 123L, 124L, 141L, 142L, 143L, 144L, 125L, 126L, 127L, 
128L, 145L, 146L, 147L, 148L, 129L, 130L, 131L, 132L, 149L, 150L, 
151L, 152L, 133L, 134L, 135L, 136L, 153L, 154L, 155L, 156L, 137L, 
138L, 139L, 140L, 157L, 158L, 159L, 160L), rep = c(1L, 2L, 3L, 
4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 
4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 
4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 
4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 
4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 
4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 
4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 
4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 
4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 
4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L), cultivar = structure(c(1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 2L, 
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L), .Label = c("Dinninup", 
"Riverina", "Seaton Park", "Yarloop"), class = "factor"), Waterlogging = structure(c(2L, 
2L, 2L, 2L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 2L, 
2L, 2L, 2L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 2L, 
2L, 2L, 2L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 2L, 
2L, 2L, 2L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 2L, 
2L, 2L, 2L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 2L, 
2L, 2L, 2L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 2L, 
2L, 2L, 2L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 2L, 
2L, 2L, 2L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 2L, 
2L, 2L, 2L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 2L, 
2L, 2L, 2L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L), .Label = c("Non-waterlogged", 
"Waterlogged"), class = "factor"), P = c(12.1, 12.1, 12.1, 12.1, 
12.1, 12.1, 12.1, 12.1, 15.17, 15.17, 15.17, 15.17, 15.17, 15.17, 
15.17, 15.17, 18.24, 18.24, 18.24, 18.24, 18.24, 18.24, 18.24, 
18.24, 24.39, 24.39, 24.39, 24.39, 24.39, 24.39, 24.39, 24.39, 
48.35, 48.35, 48.35, 48.35, 48.35, 48.35, 48.35, 48.35, 12.1, 
12.1, 12.1, 12.1, 12.1, 12.1, 12.1, 12.1, 15.17, 15.17, 15.17, 
15.17, 15.17, 15.17, 15.17, 15.17, 18.24, 18.24, 18.24, 18.24, 
18.24, 18.24, 18.24, 18.24, 24.39, 24.39, 24.39, 24.39, 24.39, 
24.39, 24.39, 24.39, 48.35, 48.35, 48.35, 48.35, 48.35, 48.35, 
48.35, 48.35, 12.1, 12.1, 12.1, 12.1, 12.1, 12.1, 12.1, 12.1, 
15.17, 15.17, 15.17, 15.17, 15.17, 15.17, 15.17, 15.17, 18.24, 
18.24, 18.24, 18.24, 18.24, 18.24, 18.24, 18.24, 24.39, 24.39, 
24.39, 24.39, 24.39, 24.39, 24.39, 24.39, 48.35, 48.35, 48.35, 
48.35, 48.35, 48.35, 48.35, 48.35, 12.1, 12.1, 12.1, 12.1, 12.1, 
12.1, 12.1, 12.1, 15.17, 15.17, 15.17, 15.17, 15.17, 15.17, 15.17, 
15.17, 18.24, 18.24, 18.24, 18.24, 18.24, 18.24, 18.24, 18.24, 
24.39, 24.39, 24.39, 24.39, 24.39, 24.39, 24.39, 24.39, 48.35, 
48.35, 48.35, 48.35, 48.35, 48.35, 48.35, 48.35), total = c(3.66, 
2.02, 1.59, 1.67, 2.12, 2.46, 1.79, 2.09, 2.03, 2.13, 1.83, 2.34, 
2.66, 2.2, 1.79, 1.97, 2.17, 2.44, 1.49, 2.19, 2.92, 2.43, 1.58, 
2.07, 2.48, 2.49, 1.69, 2.1, 2.38, 2.52, 2.41, 2.46, 2.22, 2.07, 
1.97, 2.3, 2.48, 3.16, 1.76, 2.38, 2.81, 2.64, 2.59, 3.28, 3.18, 
2.57, 2.9, 3, 2.38, 2.72, 2.58, 2.73, 3.06, 3.01, 3.01, 2.77, 
2.95, 2.36, 2.91, 2.38, 3.33, 3.19, 3.17, 3.16, 3.16, 3.2, 2.58, 
3.71, 3.11, 2.7, 2.92, 1.93, 2.95, 2.57, 2.68, 2.48, 3.34, 2.75, 
2.52, 1.88, 1.19, 0.57, 0.64, 0.66, 1.13, 1.28, 0.85, 0.96, 1.34, 
2.14, 0.63, 1.27, 1.13, 0.64, 1.21, 1.95, 1.11, 0.91, 0.75, 0.63, 
1.06, 1.07, 1.05, 0.8, 1.41, 1.13, 0.75, 0.89, 1.98, 1.27, 1.01, 
1, 1.16, 0.64, 0.64, 1.02, 1.03, 1.13, 0.79, 0.6, 3.88, 2.79, 
2.73, 2.77, 3.54, 2.05, 1.51, 1.88, 3.86, 3.13, 1.97, 3.46, 3.98, 
3.6, 2.12, 2.86, 2.95, 1.65, 1.94, 2.53, 2.21, 1.94, 2.05, 2.22, 
3, 3.28, 1.55, 3.85, 2.4, 2.1, 1.98, 1.81, 2.48, 1.66, 2.06, 
1.23, 3.75, 1.99, 1.67, 1.93)), class = "data.frame", row.names = c(NA, 
-160L))

boxplot(total~cultivar*as.factor(P),data=x)

这就是我的追求。...

我已经尝试了以下示例，但是不起作用。...

boxplot(total~cultivar*as.factor(P),data=x,id=list(n=Inf))

识别图上的异常值将使其更容易从分析中删除。由于某种原因，它不像我想的那么简单。该帖子要求我添加更多详细信息，但我认为足够。

Answer 1

您可以使用car软件包：

library(car)

Boxplot(total ~ cultivar*as.factor(P), id.method="y", data = x)

更新：

是否可以翻转car::Boxplot中的坐标？

为了应对挑战，我尝试了一些修改方法。毕竟，我能够 旋转 图，但是它不像ggplot2::coord_flip那样传统。在这里，我只是旋转情节。因此，标签仍保持其先前的对齐方式。我们可以走得更远，删除标签并重写它们，但这将破坏此解决方案的简单性。

library(car)
library(gridGraphics)

p <- Boxplot(total ~ cultivar*as.factor(P), id.method="y", data = x)

grab_grob <- function(){
  grid.echo()
  grid.grab()
}

g <- grab_grob()
grid.newpage()
pushViewport(viewport(width=0.5,angle=90))
grid.draw(g)

Answer 2

不幸的是，尽管boxplot确实返回了一个list结构，该结构提供了异常值（例如boxplot(..., plot=FALSE)$out）的值，但这在这里无济于事因为在其他组中有相等的值，而不是离群值。 （实际上，我发现使用$out总是有一定风险，除非它只是一组。）

但是您可以使用$stats获取晶须参数并自己查找所有内容。不幸的是，这不是单线的。

但是，首先，由于我不知道您所说的“ id”是什么意思，因此我将向数据中添加一些内容：

x$id <- seq_len(nrow(x))

base R

bp <- boxplot(total ~ cultivar * as.factor(P), data = x)
lims <- data.frame(nm = bp$names, t(bp$stats[c(1,5),]))
tmpx <- merge(transform(x, nm = paste(cultivar, as.factor(P), sep = ".")), lims, by = "nm", all.x = TRUE)
tmpx <- subset(tmpx, total < X1 | total > X2)
tmpx$xval <- match(tmpx$nm, bp$names)
text(total ~ xval, id, data = tmpx, adj = c(-0.5, 0.5))

在框线图上覆盖文本可能对您来说是个问题；您可以通过各种移动和/或翻转坐标来控制它。裁剪（此处未显示，但是当文本标签从绘图区域中消失时）也可能是一个问题，因此您可能需要手动控制绘图区域的限制。

dplyr

如果您喜欢tidyverse的数据收集方法，那么可以选择生成相同图的另一种方法。

library(dplyr)
bp <- boxplot(total ~ cultivar * as.factor(P), data = x)
x %>%
  mutate( nm = paste(cultivar, as.factor(P), sep = ".") ) %>%
  left_join(data.frame(nm = bp$names, t(bp$stats[c(1,5),]), stringsAsFactors = FALSE),
            by = "nm") %>%
  filter(total < X1 | total > X2) %>%
  mutate(xval = match(nm, bp$names)) %>%
  text(data = ., total ~ xval, as.character(id), adj = c(-0.5, 0.5))

（相同的情节。）

dplyr和ggplot2

library(dplyr)
library(ggplot2)
bp <- boxplot(total ~ cultivar * as.factor(P), data = x, plot = FALSE)
x %>%
  mutate( nm = paste(cultivar, as.factor(P), sep = ".") ) %>%
  left_join(data.frame(nm = bp$names, t(bp$stats[c(1,5),]), stringsAsFactors = FALSE),
            by = "nm") %>%
  mutate(outlier = total < X1 | total > X2) %>%
  ggplot(aes(interaction(cultivar, P), total)) +
  geom_boxplot() +
  geom_text(aes(label = id), hjust = -0.5, data = ~ filter(., outlier)) +
  coord_flip()

我选择翻转坐标，以便将所有标签都包含在内并显示出来，但是该方法不是必需的。我使用的一个技巧是data=函数的ggplot2参数可以采用一个表达式（我认为它是波浪符函数），该表达式允许就地对主数据集进行子集。这里我使用dplyr::filter，但是在这种情况下，如果您不使用subset，则使用base（dplyr R）同样容易。