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# 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]) %>% |
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 |
require(quantmod) | |
require(ggplot2) | |
require(reshape2) | |
# The standard definitions of boxplots are non-obvious to interpret for non-statisticians. | |
# A "the box is fifty percent, the line 95% and there you have 5% outlier points" is | |
# typically more easily swallowed by practitioners. | |
# I therefore define two functions which will change the boxplot appearance below. | |
myBoxPlotSummary <- function(x) { |