Adding Different Percentiles in boxplots in R - r

Question

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 ...'

Answer 1

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: