ggplot2 Time Series Heatmaps

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How do you easily get beautiful calendar heatmaps of time series in ggplot2? E.g:
From MarginTale
I was impressed by the lattice-based  implementation from Paul Bleicher of Humedica, which you can find referenced in http://blog.revolutionanalytics.com/2009/11/charting-time-series-as-calendar-heat-maps-in-r.html. Then, when other blogs like http://timelyportfolio.blogspot.com/2012/04/piggybacking-and-hopefully-publicizing.html picked up the topic, I decided to try a ggplot2 implementation. In a comment to the above Revolution Analytics post, Hadley already presented a quick ggplot rendition, upon which I build here.

How do you attack the problem? Looking at the example output above:
  1. We facet_grid by “months” and “years” 
  2. The data itself is plotted by “week of month” and “day of week” and coloured according to the value of interest
So, given a time series we just have to fiddle with time indexes to create a data.frame containing the time series as well as per observation the corresponding “month”, “year”, “week of month”, “day of week”. The rest is then a one-liner of code with Hadley’s wonderful ggplot2 system.

The following code contains step by step comments:

require(quantmod)
require(ggplot2)
require(reshape2)
require(plyr)
require(scales)
# Download some Data, e.g. the CBOE VIX
getSymbols("^VIX",src="yahoo")
# Make a dataframe
dat<-data.frame(date=index(VIX),VIX)
# We will facet by year ~ month, and each subgraph will
# show week-of-month versus weekday
# the year is simple
dat$year<-as.numeric(as.POSIXlt(dat$date)$year+1900)
# the month too
dat$month<-as.numeric(as.POSIXlt(dat$date)$mon+1)
# but turn months into ordered facors to control the appearance/ordering in the presentation
dat$monthf<-factor(dat$month,levels=as.character(1:12),labels=c("Jan","Feb","Mar","Apr","May","Jun","Jul","Aug","Sep","Oct","Nov","Dec"),ordered=TRUE)
# the day of week is again easily found
dat$weekday = as.POSIXlt(dat$date)$wday
# again turn into factors to control appearance/abbreviation and ordering
# I use the reverse function rev here to order the week top down in the graph
# you can cut it out to reverse week order
dat$weekdayf<-factor(dat$weekday,levels=rev(1:7),labels=rev(c("Mon","Tue","Wed","Thu","Fri","Sat","Sun")),ordered=TRUE)
# the monthweek part is a bit trickier
# first a factor which cuts the data into month chunks
dat$yearmonth<-as.yearmon(dat$date)
dat$yearmonthf<-factor(dat$yearmonth)
# then find the "week of year" for each day
dat$week <- as.numeric(format(dat$date,"%W"))
# and now for each monthblock we normalize the week to start at 1
dat<-ddply(dat,.(yearmonthf),transform,monthweek=1+week-min(week))
# Now for the plot
P<- ggplot(dat, aes(monthweek, weekdayf, fill = VIX.Close)) +
geom_tile(colour = "white") + facet_grid(year~monthf) + scale_fill_gradient(low="red", high="yellow") +
opts(title = "Time-Series Calendar Heatmap") + xlab("Week of Month") + ylab("")
P

It should be easy to wrap into a function and I hope its useful.

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