**Learning R**, and kindly contributed to R-bloggers)

A post on FlowingData blog demonstrated how to quickly make a heatmap below using R base graphics.

This post shows how to achieve a very similar result using ggplot2.

## Data Import

FlowingData used last season’s NBA basketball statistics provided by databasebasketball.com, and the csv-file with the data can be downloaded directly from its website.

> nba <- read.csv("http://datasets.flowingdata.com/ppg2008.csv") |

The players are ordered by points scored, and the Name variable converted to a factor that ensures proper sorting of the plot.

> nba$Name <- with(nba, reorder(Name, PTS)) |

Whilst FlowingData uses `heatmap` function in the `stats`-package that requires the plotted values to be in matrix format, `ggplot2` operates with dataframes. For ease of processing, the dataframe is converted from wide format to a long format.

The game statistics have very different ranges, so to make them comparable all the individual statistics are rescaled.

> library(ggplot2) |

> nba.m <- melt(nba) > nba.m <- ddply(nba.m, .(variable), transform, + rescale = rescale(value)) |

## Plotting

There is no specific heatmap plotting function in `ggplot2`, but combining `geom_tile` with a smooth gradient fill does the job very well.

> (p <- ggplot(nba.m, aes(variable, Name)) + geom_tile(aes(fill = rescale), + colour = "white") + scale_fill_gradient(low = "white", + high = "steelblue")) |

A few finishing touches to the formatting, and the heatmap plot is ready for presentation.

> base_size <- 9 > p + theme_grey(base_size = base_size) + labs(x = "", + y = "") + scale_x_discrete(expand = c(0, 0)) + + scale_y_discrete(expand = c(0, 0)) + opts(legend.position = "none", + axis.ticks = theme_blank(), axis.text.x = theme_text(size = base_size * + 0.8, angle = 330, hjust = 0, colour = "grey50")) |

## Rescaling Update

In preparing the data for the above plot all the variables were rescaled so that they were between 0 and 1.

Jim rightly pointed out in the comments (and I did not initally get it) that the `heatmap`-function uses a different scaling method and therefore the plots are not identical. Below is an updated version of the heatmap which looks much more similar to the original.

> nba.s <- ddply(nba.m, .(variable), transform, + rescale = scale(value)) |

> last_plot() %+% nba.s |

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