tidyverse time series heatmaps

margintale_blog_3.R

# An simple function to turn an xts time series
# into a ggplot calendar heatmap 
require(tidyverse)

# The core idea is to transform the data such that one can
# plot "Value" as a function of "WeekOfMonth" versus "DayOfWeek"
# and facet this Year versus Month

xts_heatmap <- function(x){
  data.frame(Date=as.Date(index(x)), x[,1]) %>%
    setNames(c("Date","Value")) %>%
    dplyr::mutate(
      Year=lubridate::year(Date),
      Month=lubridate::month(Date),
      # I use factors here to get plot ordering in the right order
      # without worrying about locale
      MonthTag=factor(Month,levels=as.character(1:12),
                      labels=c("Jan","Feb","Mar","Apr","May","Jun","Jul","Aug","Sep","Oct","Nov","Dec"),ordered=TRUE),
      # week start on Monday in my world
      Wday=lubridate::wday(Date,week_start=1),
      # the rev reverse here is just for the plotting order
      WdayTag=factor(Wday,levels=rev(1:7),labels=rev(c("Mon","Tue","Wed","Thu","Fri","Sat","Sun")),ordered=TRUE),
      Week=as.numeric(format(Date,"%W"))
    ) %>%
    # ok here we group by year and month and then calculate the week of the month 
    # we are currently in
    dplyr::group_by(Year,Month) %>% 
    dplyr::mutate(Wmonth=1+Week-min(Week)) %>% 
    dplyr::ungroup() %>% 
    ggplot(aes(x=Wmonth, y=WdayTag, fill = Value)) + 
    geom_tile(colour = "white") + 
    facet_grid(Year~MonthTag) + 
    scale_fill_gradient(low="red", high="yellow") +
    labs(x="Week of Month", y=NULL)
}
  
require(quantmod)
# Download some Data, e.g. the CBOE VIX 
quantmod::getSymbols("^VIX",src="yahoo")

# lets see
xts_heatmap(Cl(VIX)) + labs(title="Heatmap of VIX")
# ok