We wished to visualize the monthly returns of the London ICE gasoil futures contract. To do so, we created a heatmap with R, using the ggplot2 library.

The original data were downloaded from the internet. They consisted on daily closing prices of the 1st nearby London ICE gasoil futures contract from April 1981 to October 2013. The monthly returns were computed as .

The R code below is straightforward. It can easily be modified to suit specific needs and serve as a basis for more advanced graphs. We also show the result.

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# Édouard Tallent @ TaGoMa.Tech, November 2013    #
# QuantCorner @ https://quantcorner.wordpress.com  #
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# Create a data object from the file object that actually is an object for the file containing the data
data <- read.table(file,sep=',',header=TRUE,na.strings="NA") # Create the data object from file

# Remove rows containing NAs
row.has.NA <- apply(data, 1, function(x){any(is.na(x))})
# sum(row.has.NA)
data <- data[!row.has.NA,]

# Create a xts object from the data object
require(xts)                                                            # Load xts
data <- as.xts(data[,2], order.by=as.Date(data[,1], format='%d/%m/%Y')) # Create the xts object

# Compute the monthly log return
require('quantmod')                                     # Load quantmod
ret <- monthlyReturn(data, type='log', leading=TRUE)    # Compute the monthly returns

# Create a matrix mat object from ret       
ret <- c(NA, NA, NA, ret, NA, NA)                               # The original time series nor starts on a Jan month nor ends on Dec    
mat <- matrix(ret*100, ncol=12, byrow=TRUE, dimnames=NULL)      # Create a R matrix
year <- seq(from=1981, to=2013, by=1)                           # Vector containing the years
month <- c('Jan','Feb','Mar', 'Apr','May','Jun','Jul','Aug','Sep','Oct','Nov','Dec')    # Vector containing the year months
colnames(mat) <- month  # Give columns names
rownames(mat) <- year   # Give rows names

# Transform the data in mat into a long series using the melt() function
require(reshape2)           # Load reshape2
molten <- melt(mat)         # Transform the series into a long series

# Define the color palette to be used in the hetmap
LtoM <-colorRampPalette(c('red', 'yellow' ))            # The spectrum of colors for the lowest returns
Mid <- "snow3"                                          # Snow3 is the color for the (approximatedly) median value
MtoH <-colorRampPalette(c('lightgreen', 'darkgreen'))   # The spectrum of colors for the highest values

# Plot the heatmap
require(ggplot2)                                                            # Load ggplot2      
hm <- ggplot(data=molten, aes(x=factor(Var2, levels=month.abb),
                y=Var1, fill=value)) + geom_raster()                        # Draw the heatmap using geom_raster()
hm <- hm + scale_fill_gradient2(low=LtoM(100), mid=Mid, high=MtoH(100))     # Colors, please!
hm <- hm + labs(fill='Return (%)')                                          # The legend
hm <- hm + geom_text(aes(label=paste(sprintf("%.1f %%", value))), size=4)   # Write the monthly returns in hte heatmap
hm <- hm + scale_y_continuous(breaks=1981:2013)                             # Deal with the y-axis
hm <- hm + xlab(label=NULL) +   ylab(label=NULL)                            # No axis names
hm <- hm + theme_bw()                                                       # No background grey grid
hm <- hm + theme(axis.text.x=element_text(size=10, hjust=0, vjust=0.4,
                angle=90))                                                  # Beautify the x-axis
hm <- hm + ggtitle(label='Monthly return - 1st nearby London ICE Gasoil futures contract')  # Add a title

print(hm)                                                                   # Print out the heatmap on the screen