xts - how to subset on each day of the week - r

Question

I understand similar questions have been answered. My problem is I have a time series data for 2033 days on 15 minutes interval. I would like to plot the series for each day (Mon-Sun). For instance how an average Monday looks like.
I tried to subset by using .indexwday, but the series for the day starts at 13:00.
I am kind of novice, so please let me know if I need to provide additional details.
Sample data (xts)
2008-01-01 00:00:00 16
2008-01-01 00:15:00 56
2008-01-01 00:30:00 136
2008-01-01 00:45:00 170
2008-01-01 01:00:00 132
....
2013-07-25 22:30:00 95
2013-07-25 22:45:00 82
2013-07-25 23:00:00 66
2013-07-25 23:15:00 65
2013-07-25 23:30:00 66
2013-07-25 23:45:00 46
The plot below might make more sense what I want (This is the average of all Mondays)

Answer 1

Here's another solution, which does not depend on packages other than xts and zoo.
# example data
ix <- seq(as.POSIXct("2008-01-01"), as.POSIXct("2013-07-26"), by="15 min")
set.seed(21)
x <- xts(sample(200, length(ix), TRUE), ix)
# aggregate by 15-minute observations for each weekday
a <- lapply(split.default(x, format(index(x), "%A")), # split by weekday
function(x) aggregate(x, format(index(x), "%H:%M"), mean)) # aggregate by 15-min
# merge aggregated data into one zoo object, ordering columns
z <- do.call(merge, a)[,c("Sunday", "Monday", "Tuesday", "Wednesday", "Thursday", "Friday", "Saturday")]
# convert index to POSIXct to make plotting easier
index(z) <- as.POSIXct(index(z), format="%H:%M")
# plot
plot(z, type="l", nc=1, ylim=range(z), main="Average daily volume", las=1)
Setting ylim forces each plot to have the same y-axis range. Otherwise they would depend on each individual series, which may make them difficult to compare if the values vary greatly.

Answer 2

Try this:
#Get necessary packages
install.packages("lubridate")
install.packages("magrittr")
install.packages("dplyr")
install.packages("ggplot2")
install.packages("scales")
#Import packages
library(lubridate,warn=F)
library(dplyr,warn=F)
library(magrittr)
library(ggplot2,warn=F)
library(scales, warn=F)
#Getting the data
tstart = as.POSIXct('2008-01-01 00:00:00')
tend = as.POSIXct('2013-07-25 23:45:00')
ttimes <- seq(from = tstart,to=tend,by='15 mins')
tvals <- sample(seq(1,200),length(ttimes),T)
tsdata <- data.frame(Dates=ttimes,Vals=tvals)
tsdata <- tsdata %>% mutate(DayofWeek = wday(Dates,label=T), Hours = as.POSIXct(strftime(Dates,format="%H:%M:%S"),format="%H:%M:%S"))
#Pick a day at a time. I am using Mondays for this example.
tsdata_monday <- tsdata %>% filter(DayofWeek=='Mon') %>% group_by(Hours) %>% summarise(meanVals=mean(Vals)) %>% as.data.frame()
#Plotting the graph of mean values versus times for Monday:
ggplot(tsdata_monday) + aes(x=Hours,y=meanVals) + geom_line() + scale_x_datetime(breaks=date_breaks("4 hour"), labels=date_format("%H:%M"))
#If you want you can go ahead and plot all the days. But please keep in mind
#that this does not look good at all. Too many plots for the plot window to
#Display nicely.
alltsdata <- tsdata %>% group_by(DayofWeek, Hours) %>% summarise(MeanVals=mean(Vals)) %>% as.data.frame()
ggplot(alltsdata) + aes(x=Hours,y=MeanVals) + geom_line() + scale_x_datetime(breaks=date_breaks("4 hour"), labels=date_format("%H:%M")) + facet_grid(.~DayofWeek)
I recommend you plot one day at a time or use a for loop or one of the apply function variations to get the plots.
Also when filtering by day of the week, please keep in mind that the days are shortened as follows:
unique(tsdata$DayofWeek)
[1] Tues Wed Thurs Fri Sat Sun Mon
Hope it helps.

Answer 3

apply.daily does exactly what you want.( assumming your data is called d.xts and a xts-object)
apply.daily(d.xts,sum)
another solution would be using aggregate:
aggregate(d.xts,as.Date(index(d.xts)),sum)
Note that the answers are slightly different: apply.daily starts from start(d.xts) to end(d.xts) whereas aggregate goes by day from midnight to midnight.