# 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)Run the code above in your browser using DataCamp Workspace