Translating between qplot and Graphics Production Library (GPL)
The Grammar of Graphics uses two specifications. A concise format is used to caption figures, and a more detailed xml format stored on disk.
# The following example of the concise format is adapted from Figure 1.5, # page 13, of Leland Wilkinson's "The Grammar of Graphics." # Springer, 2nd edition, 2005. # DATA: source("demographics") # DATA: longitude, latitude = map(source("World")) # TRANS: bd = max(birth - death, 0) # COORD: project.mercator() # ELEMENT: point(position(lon * lat), size(bd), color(color.red)) # ELEMENT: polygon(position(longitude * latitude)) # This is relatively simple to adapt to the syntax of ggplot2: # ggplot() is used to specify the default data and default aesthetic mappings. # Data is provided as standard R data.frames existing in the global environment; # it does not need to be explicitly loaded. We also use a slightly # different world dataset, with columns lat and long. This lets us use the # same aesthetic mappings for both datasets. Layers can override the default # data and aesthetic mappings provided by the plot. # We replace TRANS with an explicit transformation by R code. # ELEMENTs are replaced with layers, which explicitly specify the data # source. Each geom has a default statistic which is used to transform the # data prior to plotting. For the geoms in this example, the default statistic # is the identity function. Fixed aesthetics (the colour red in this example) # are supplied as additional arguments to the layer, rather than as special # constants. # The SCALE component has been omitted from this example (so that the # defaults are used). In both the ggplot2 and GoG examples, scales are # defined by default. In ggplot you can override the defaults by adding a # scale object, e.g., scale colour or scale size. # COORD uses a slightly different format. In general, most of the components # specifications in ggplot are slightly different to those in GoG, in order to # be more familiar to R users. # Each component is added together with + to create the final plot. # Resulting ggplot2 code: # demographics <- transform(demographics, bd = pmax(birth - death, 0)) # p <- ggplot(demographic, aes(lon, lat)) # p <- p + geom_polyogon(data = world) # p <- p + geom_point(aes(size = bd), colour = "red") # p <- p + coord_map(projection = "mercator") # print(p)
Looks like there are no examples yet.