在R中的箱形图中添加不同的百分位数 [英] Adding Different Percentiles in boxplots in R
本文介绍了在R中的箱形图中添加不同的百分位数的处理方法，对大家解决问题具有一定的参考价值，需要的朋友们下面随着小编来一起学习吧！
问题描述
JANWD< -read.csv（C：\\我的电脑 \\ MWBox2.csv）
JANWD.df< -data.frame（JANWD）
JANWD.sub< -subset（JANWD.df，MONTH< 2& weekend ==NO）
KeepCols< -c（Hour，Houston_Load）
HWD< - JANWD.sub [，KeepCols]
sd< -tapply （HWD $ Houston_Load，HWD $ Hour，sd）
表示< -tapply（HWD $ Houston_Load，HWD $ Hour，mean）
boxplot（Houston_Load〜Hour，data = HWD，xlab =WEEKDAY HOURS，ylab =MW Differnce，ylim = c（-10,20），smooth = TRUE，col =bisque，range = 0）
points（sd，pch = 22，col =blue）
points（means，pch = 23，col =red）
＃输出用于运行1月份boxplot的数据的子集休斯顿
str（HWD）
'data.frame'：504 obs。 2个变量：
`$小时：int 1 2 3 4 5 6 7 8 9 10 ...'
`$ Houston_Load：num 1.922 2.747 -2.389 0.515 1.922 ...'
＃原始数据的$ OUTPUT
str（JANWD）
'data.frame'：8783 obs。 9个变量：
$日期：因子w / 366个等级1/1/2012，1/10/2012，..：306 306 306 306 306 306 306 306 306 306 ...
`$ Hour：int 1 2 3 4 5 6 7 8 9 10 ...'
`$ MONTH：int 8 8 8 8 8 8 8 8 8 ...'
`$周末：因子W / 2级别NO，YES：1 1 1 1 1 1 1 1 1 1''
`$ TOTAL_LOAD：num 0.607 5.111 6.252 7.607 0.607 ...'
$ $ Houston_Load：num -2.389 0.515 1.922 2.747 -2.389 ...'
`$ North_Load：num 2.95 4.14 3.55 3.91 2.95 ...'
`$ South_Load：num -0.108 0.267 0.54 0.638 - 0.108 ...'
`$ West_Load：num 0.154 0.193 0.236 0.311 0.154 ...'
解决方案
以下是一种使用 quantile（）来计算相关百分位数的方法。我使用 rug（）添加标记。
set.seed 1）
X < - rnorm（200）
boxplot（X，yaxt =n）
##计算所需分位数
qntl < - quantile（X，probs = c（0.85，0.90））
##将它们作为左边的
rug（qntl，side = 2，col =blue ，lwd = 2）
##添加方框和轴
轴（2）
box（）
$ b 更新：为了响应OP提供 str（）输出，这里是一个类似于OP必须处理的数据的例子：
set.seed（1）## make reproducible
HWD< - data.frame（Hour = rep（0:23,10），
Houston_Load = rnorm（24 * 10））
现在假设您希望每个小时的第85百分位和第90百分位数的分位数？如果是这样，我们需要按照前面显示的 Hour 并通过 quantile（）进行计算。
quants < - sapply（split（HWD $ Houston_Load，list（HWD $ Hour）），
quantile，probs = c （0.85,0.9））
给出：
R> （HWD $ Houston_Load，列表（HWD $小时）），
+分位数，probs = c（0.85,0.9））
R>数量
0 1 2 3 4 5 6
85％0.3576510 0.8633506 1.581443 0.2264709 0.4164411 0.2864026 1.053742
90％0.6116363 0.9273008 2.109248 0.4218297 0.5554147 0.4474140 1.366114
7 8 9 10 11 12 13 14
85％0.5352211 0.5175485 1.790593 1.394988 0.7280584 0.8578999 1.437778 1.087101
90％0.8625322 0.5969672 1.830352 1.519262 0.9399476 1.1401877 1.763725 1.102516
15 16 17 18 19 20 21
85％0.6855288 0.4874499 0.5493679 0.9754414 1.095362 0.7936225 1.824002
90％0.8737872 0.6121487 0.6078405 1.0990935 1.233637 0.9431199 2.175961
22 23
85％1.058648 0.6950166
90％1.145783 0.8436541
现在我们可以在箱形图的x个位置绘制标记
boxplot（Houston_Load〜Hour，data = HWD，axes = FALSE）
xlocs< - 1： 24 ##在哪里绘制标记
tickl < - 0.15 ##所用标记的长度
y1 = quants [，i]，
x1 = rep（xlocs [i] + 0.15,2），y1 = quants [，i]，
col = c （xlab =Hour，ylab =Houston Load）
轴（1，at = xlocs， labels = xlocs - 1）
axis（2）
box（）
legend（bottomleft，legend = paste（c（0.85，0.90），quantile） ，
bty =n，lty =solid，lwd = 2，col = c（red，blue））
得到的数字应该如下所示：
I am failry new to R and recently used it to make some Boxplots. I also added the mean and standard deviation in my boxplot. I was wondering if i could add some kind of tick mark or circle in different percentile as well. Let's say if i want to mark the 85th, $ 90th percentile in each HOUR boxplot, is there a way to do this? My data consist of a year worth of loads in MW in each hour & My output consist of 24 boxplots for each hour for each month. I am doing each month at a time because i am not sure if there is a way to run all 96(Each month, weekday/weekend , for 4 different zones) boxplots at once. Thanks in advance for help.
JANWD <-read.csv("C:\\My Directory\\MWBox2.csv")
JANWD.df<-data.frame(JANWD)
JANWD.sub <-subset(JANWD.df, MONTH < 2 & weekend == "NO")
KeepCols <-c("Hour" , "Houston_Load")
HWD <- JANWD.sub[ ,KeepCols]
sd <-tapply(HWD$Houston_Load, HWD$Hour, sd)
means <-tapply(HWD$Houston_Load, HWD$Hour, mean)
boxplot(Houston_Load ~ Hour, data=HWD, xlab="WEEKDAY HOURS", ylab="MW Differnce", ylim= c(-10, 20), smooth=TRUE ,col ="bisque", range=0)
points(sd, pch = 22, col= "blue")
points(means, pch=23, col ="red")
#Output of the subset of data used to run boxplot for month january in Houston
str(HWD)
'data.frame': 504 obs. of 2 variables:
`$ Hour : int 1 2 3 4 5 6 7 8 9 10 ...'
`$ Houston_Load: num 1.922 2.747 -2.389 0.515 1.922 ...'
#OUTPUT of the original data
str(JANWD)
'data.frame': 8783 obs. of 9 variables:
$ Date : Factor w/ 366 levels "1/1/2012","1/10/2012",..: 306 306 306 306 306 306 306 306 306 306 ...
`$ Hour : int 1 2 3 4 5 6 7 8 9 10 ...'
` $ MONTH : int 8 8 8 8 8 8 8 8 8 8 ...'
`$ weekend : Factor w/ 2 levels "NO","YES": 1 1 1 1 1 1 1 1 1 1 ...'
`$ TOTAL_LOAD : num 0.607 5.111 6.252 7.607 0.607 ...'
`$ Houston_Load: num -2.389 0.515 1.922 2.747 -2.389 ...'
`$ North_Load : num 2.95 4.14 3.55 3.91 2.95 ...'
`$ South_Load : num -0.108 0.267 0.54 0.638 -0.108 ...'
`$ West_Load : num 0.154 0.193 0.236 0.311 0.154 ...'
解决方案
Here is one way, using quantile() to compute the relevant percentiles for you. I add the marks using rug().
set.seed(1)
X <- rnorm(200)
boxplot(X, yaxt = "n")
## compute the required quantiles
qntl <- quantile(X, probs = c(0.85, 0.90))
## add them as a rgu plot to the left hand side
rug(qntl, side = 2, col = "blue", lwd = 2)
## add the box and axes
axis(2)
box()
Update: In response to the OP providing str() output, here is an example similar to the data that the OP has to hand:
set.seed(1) ## make reproducible
HWD <- data.frame(Hour = rep(0:23, 10),
Houston_Load = rnorm(24*10))
Now get I presume you want ticks at 85th and 90th percentiles for each Hour? If so we need to split the data by Hour and compute via quantile() as I showed earlier:
quants <- sapply(split(HWD$Houston_Load, list(HWD$Hour)),
quantile, probs = c(0.85, 0.9))
which gives:
R> quants <- sapply(split(HWD$Houston_Load, list(HWD$Hour)),
+ quantile, probs = c(0.85, 0.9))
R> quants
0 1 2 3 4 5 6
85% 0.3576510 0.8633506 1.581443 0.2264709 0.4164411 0.2864026 1.053742
90% 0.6116363 0.9273008 2.109248 0.4218297 0.5554147 0.4474140 1.366114
7 8 9 10 11 12 13 14
85% 0.5352211 0.5175485 1.790593 1.394988 0.7280584 0.8578999 1.437778 1.087101
90% 0.8625322 0.5969672 1.830352 1.519262 0.9399476 1.1401877 1.763725 1.102516
15 16 17 18 19 20 21
85% 0.6855288 0.4874499 0.5493679 0.9754414 1.095362 0.7936225 1.824002
90% 0.8737872 0.6121487 0.6078405 1.0990935 1.233637 0.9431199 2.175961
22 23
85% 1.058648 0.6950166
90% 1.145783 0.8436541
Now we can draw marks at the x locations of the boxplots
boxplot(Houston_Load ~ Hour, data = HWD, axes = FALSE)
xlocs <- 1:24 ## where to draw marks
tickl <- 0.15 ## length of marks used
for(i in seq_len(ncol(quants))) {
segments(x0 = rep(xlocs[i] - 0.15, 2), y0 = quants[, i],
x1 = rep(xlocs[i] + 0.15, 2), y1 = quants[, i],
col = c("red", "blue"), lwd = 2)
}
title(xlab = "Hour", ylab = "Houston Load")
axis(1, at = xlocs, labels = xlocs - 1)
axis(2)
box()
legend("bottomleft", legend = paste(c("0.85", "0.90"), "quantile"),
bty = "n", lty = "solid", lwd = 2, col = c("red", "blue"))
The resulting figure should look like this:
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