分组时覆盖箱图中的下部,上部等 [英] Override lower, upper, etc. in boxplot while grouping
问题描述
默认情况下,对于 geom_boxplot
中的较低,中部和较高分位数,将考虑25%-,50%-和75%的分位数。这些是根据 y
计算得出的,但可以通过美学参数 lower
, upper手动设置
,中间
(还提供 x
, ymin
和 ymax
并设置 stat = identity
)。
但是,这样做会产生一些不良影响(请参见示例代码中的版本1):
- 参数
group
被忽略,因此在计算中会考虑列的所有值(例如,在计算每个组的最低分位数时) - 将所得的相同箱形图按
x
分组,并在组中重复出现的次数与数据中出现的特定组值相同(而不是 - 未绘制离群值
通过预先计算所需的值并将其存储在新的数据框中,则可以处理前两点(请参见示例代码中的版本2),而第三点则通过识别异常值并通过<$分别添加到图表中来进行固定c $ c> geom_point 。
是否存在更直接的方法来更改分位数,而不会产生这些不良影响?
示例代码:
set.seed(12)
#B中的随机数据,按A
u中的值1-4分组-data.frame(A = sample.int(4,100,replace = TRUE),B = rnorm(100))
#期望的参数
qymax <-0.9
qymin <-0.1
qmiddle <-0.5
qupper <-0.8
qlower <-0.2
版本1:A中每个值的重复箱形图,按A分组
ggplot(u,aes(x = A,y = B))+
geom_boxplot(aes(group = A,
lower =分位数(B ,qlower),
upper =分位数(B,qupper),
middle =分位数(B,qmiddle),
ymin =分位数(B,qymin),
ymax =分位数(B,qymax)),
stat = identity)
版本2:计算每个组首先使用参数。基本R解决方案
Bgrouped<-lapply(唯一(u $ A),函数(a)u $ B [u $ A == a])
.lower<-sapply(Bgrouped,function(x)分位数(x,qlower))
.upper<--sapply(Bgrouped,function(x)分位数,qupper))
.middle<-sapply(Bgrouped,function(x)分位数(x,qmiddle))
.ymin<-sapply(Bgrouped,function(x)分位数(x,qymin ))
.ymax<-sapply(Bgrouped,function(x)分位数(x,qymax))
u<-data.frame(A = unique(u $ A),
下部=。下部,
上部=。上部,
中间=。中部,
ymin = .ymin,
ymax = .ymax)
ggplot(u,aes(x = A))+
geom_boxplot(aes(lower = lower,upper = Upper,
Middle = Middle,ymin = ymin,ymax = ymax),
stat = identity)
没什么如果没有 lot ,我真的会做人们通常期望盒装图的最小/最大/盒装值对应于相同的分位数位置,但是可以做到。
使用的数据(添加了极端值以显示异常值):
set.seed(12)
u<-数据.frame(A = sample.int(4,100,replace = TRUE),B = rnorm(100))
u $ B [c(30,70,76)]<-c(4,-4 ,-5)
解决方案1 :您可以预先计算值无需绕过基本R路线,&在同一步骤中包含离群值的计算。我会在Hadley的tidyverse库中完全做到这一点,我发现它更整洁:
library(dplyr)
库( tidyr)
u%>%
group_by(A)%>%
summarise(较低=分位数(B,qlower),
较高=分位数(B ,qupper),
middle =分位数(B,qmiddle),
IQR = diff(c(lower,upper)),
ymin = max(quantile(B,qymin),lower- 1.5 * IQR),
ymax = min(分位数(B,qymax),上限+ 1.5 * IQR),
离群值= list(B [哪个(B>上限+ 1.5 * IQR |
B< lower-1.5 * IQR)]))%&%;%
ungroup()%&%;%
ggplot(aes(x = A))+
geom_boxplot(aes (较低=较低,较高=较高,
中间=中间,ymin = ymin,ymax = ymax),
stat = identity)+
geom_point(data =。%>%
过滤器(sapply(异常值,长度)> 0)%>%
s当选(A,离群值)%&%;%
unnest(),
aes(y = unlist(离群值)))
解决方案2 :您可以覆盖ggplot使用的实际分位数规格。 geom_boxplot()
的分位数的计算实际上在 StatBoxplot
的 compute_group()中
函数,在
请注意,更改定义时,影响环境中的每个ggplot对象。因此,如果您在定义更改之前之前创建了ggplot箱线图对象,则& 之后将其打印出来,箱线图将遵循新的定义。 (对于上面的并排比较,我必须立即将每个ggplot转换为grob对象,以保持差异。)
Per default, for the lower, middle and upper quantile in geom_boxplot
the 25%-, 50%-, and 75%-quantiles are considered. These are computed from y
, but can be set manually via the aesthetic arguments lower
, upper
, middle
(providing also x
, ymin
and ymax
and setting stat="identity"
).
However, doing so, several undesirable effects occur (cf. version 1 in the example code):
- The argument
group
is ignored, so all values of a column are considered in calculations (for instance when computing the lowest quantile for each group) - The resulting identical boxplots are grouped by
x
, and repeated within the group as often as the specific group value occurs in the data (instead of merging the boxes to a wider one) - outliers are not plotted
By pre-computing the desired values and storing them in a new data frame, one can handle the first two points (cf. version 2 in the example code), while the third point is fixed by identifying the outliers and adding them separately to the chart via geom_point
.
Is there a more straight forward way to have the quantiles changed, without having these undesired effects?
Example Code:
set.seed(12)
# Random data in B, grouped by values 1 to 4 in A
u <- data.frame(A = sample.int(4, 100, replace = TRUE), B = rnorm(100))
# Desired arguments
qymax <- 0.9
qymin <- 0.1
qmiddle <- 0.5
qupper <- 0.8
qlower <- 0.2
Version 1: Repeated boxplots per value in A, grouped by A
ggplot(u, aes(x = A, y = B)) +
geom_boxplot(aes(group=A,
lower = quantile(B, qlower),
upper = quantile(B, qupper),
middle = quantile(B, qmiddle),
ymin = quantile(B, qymin),
ymax = quantile(B, qymax) ),
stat="identity")
Version 2: Compute the arguments first for each group. Base R solution
Bgrouped <- lapply(unique(u$A), function(a) u$B[u$A == a])
.lower <- sapply(Bgrouped, function(x) quantile(x, qlower))
.upper <- sapply(Bgrouped, function(x) quantile(x, qupper))
.middle <- sapply(Bgrouped, function(x) quantile(x, qmiddle))
.ymin <- sapply(Bgrouped, function(x) quantile(x, qymin))
.ymax <- sapply(Bgrouped, function(x) quantile(x, qymax))
u <- data.frame(A = unique(u$A),
lower = .lower,
upper = .upper,
middle = .middle,
ymin = .ymin,
ymax = .ymax)
ggplot(u, aes(x = A)) +
geom_boxplot(aes(lower = lower, upper = upper,
middle = middle, ymin = ymin, ymax = ymax ),
stat="identity")
It's not something I'd really do without a lot of justification, as people typically expect the boxplot's min / max / box values to correspond to the same quantile positions, but it can be done.
Data used (with extreme values added to demonstrate outliers):
set.seed(12)
u <- data.frame(A = sample.int(4, 100, replace = TRUE), B = rnorm(100))
u$B[c(30, 70, 76)] <- c(4, -4, -5)
Solution 1: You can pre-compute the values without going by the base R route, & include calculations for outliers in the same step. I'd do it completely within Hadley's tidyverse libraries, which I find neater:
library(dplyr)
library(tidyr)
u %>%
group_by(A) %>%
summarise(lower = quantile(B, qlower),
upper = quantile(B, qupper),
middle = quantile(B, qmiddle),
IQR = diff(c(lower, upper)),
ymin = max(quantile(B, qymin), lower - 1.5 * IQR),
ymax = min(quantile(B, qymax), upper + 1.5 * IQR),
outliers = list(B[which(B > upper + 1.5 * IQR |
B < lower - 1.5 * IQR)])) %>%
ungroup() %>%
ggplot(aes(x = A)) +
geom_boxplot(aes(lower = lower, upper = upper,
middle = middle, ymin = ymin, ymax = ymax ),
stat="identity") +
geom_point(data = . %>%
filter(sapply(outliers, length) > 0) %>%
select(A, outliers) %>%
unnest(),
aes(y = unlist(outliers)))
Solution 2: You can override the actual quantile specifications used by ggplot. The calculations for geom_boxplot()
's quantiles are actually in StatBoxplot
's compute_group()
function, found here:
compute_group = function(data, scales, width = NULL, na.rm = FALSE, coef = 1.5) {
qs <- c(0, 0.25, 0.5, 0.75, 1)
if (!is.null(data$weight)) {
mod <- quantreg::rq(y ~ 1, weights = weight, data = data, tau = qs)
stats <- as.numeric(stats::coef(mod))
} else {
stats <- as.numeric(stats::quantile(data$y, qs))
}
... (omitted for space)
The qs
vector defines the quantile positions. It's not affected by parameters passed to compute_group()
, so the only way to change that is to change the definition for compute_group()
itself:
# save a copy of the original function, in case you need to revert
original.function <- environment(ggplot2::StatBoxplot$compute_group)$f
# define new function (only the first line for qs is changed, but you'll have to
# copy & paste the whole thing)
new.function <- function (data, scales, width = NULL, na.rm = FALSE, coef = 1.5) {
qs <- c(0.1, 0.2, 0.5, 0.8, 0.9)
if (!is.null(data$weight)) {
mod <- quantreg::rq(y ~ 1, weights = weight, data = data,
tau = qs)
stats <- as.numeric(stats::coef(mod))
}
else {
stats <- as.numeric(stats::quantile(data$y, qs))
}
names(stats) <- c("ymin", "lower", "middle", "upper", "ymax")
iqr <- diff(stats[c(2, 4)])
outliers <- data$y < (stats[2] - coef * iqr) | data$y > (stats[4] +
coef * iqr)
if (any(outliers)) {
stats[c(1, 5)] <- range(c(stats[2:4], data$y[!outliers]),
na.rm = TRUE)
}
if (length(unique(data$x)) > 1)
width <- diff(range(data$x)) * 0.9
df <- as.data.frame(as.list(stats))
df$outliers <- list(data$y[outliers])
if (is.null(data$weight)) {
n <- sum(!is.na(data$y))
}
else {
n <- sum(data$weight[!is.na(data$y) & !is.na(data$weight)])
}
df$notchupper <- df$middle + 1.58 * iqr/sqrt(n)
df$notchlower <- df$middle - 1.58 * iqr/sqrt(n)
df$x <- if (is.factor(data$x))
data$x[1]
else mean(range(data$x))
df$width <- width
df$relvarwidth <- sqrt(n)
df
}
Result:
# toggle between the two definitions
environment(StatBoxplot$compute_group)$f <- original.function
ggplot(u, aes(x = A, y = B, group = A)) +
geom_boxplot() +
ggtitle("original definition for calculated quantiles")
environment(StatBoxplot$compute_group)$f <- new.function
ggplot(u, aes(x = A, y = B, group = A)) +
geom_boxplot() +
ggtitle("new definition for calculated quantiles")
Do note that when you change the definition, it affects every ggplot object in your environment. So if you've created a ggplot boxplot object before the definition change, & print it out afterwards, the boxplot will follow the new definition. (For the side-by-side comparison above, I had to convert each ggplot to a grob object immediately, in order to preserve the difference.)
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