Polygons are very similar to paths (as drawn by geom_path()
)
except that the start and end points are connected and the inside is
coloured by fill
. The group
aesthetic determines which cases
are connected together into a polygon.
geom_polygon(mapping = NULL, data = NULL, stat = "identity", position = "identity", ..., na.rm = FALSE, show.legend = NA, inherit.aes = TRUE)
mapping | Set of aesthetic mappings created by |
---|---|
data | The data to be displayed in this layer. There are three options: If A A |
stat | The statistical transformation to use on the data for this layer, as a string. |
position | Position adjustment, either as a string, or the result of a call to a position adjustment function. |
... | Other arguments passed on to |
na.rm | If |
show.legend | logical. Should this layer be included in the legends?
|
inherit.aes | If |
geom_polygon
understands the following aesthetics (required aesthetics are in bold):
x
y
alpha
colour
fill
group
linetype
size
Learn more about setting these aesthetics in vignette("ggplot2-specs")
geom_path()
for an unfilled polygon,
geom_ribbon()
for a polygon anchored on the x-axis
# When using geom_polygon, you will typically need two data frames: # one contains the coordinates of each polygon (positions), and the # other the values associated with each polygon (values). An id # variable links the two together ids <- factor(c("1.1", "2.1", "1.2", "2.2", "1.3", "2.3")) values <- data.frame( id = ids, value = c(3, 3.1, 3.1, 3.2, 3.15, 3.5) ) positions <- data.frame( id = rep(ids, each = 4), x = c(2, 1, 1.1, 2.2, 1, 0, 0.3, 1.1, 2.2, 1.1, 1.2, 2.5, 1.1, 0.3, 0.5, 1.2, 2.5, 1.2, 1.3, 2.7, 1.2, 0.5, 0.6, 1.3), y = c(-0.5, 0, 1, 0.5, 0, 0.5, 1.5, 1, 0.5, 1, 2.1, 1.7, 1, 1.5, 2.2, 2.1, 1.7, 2.1, 3.2, 2.8, 2.1, 2.2, 3.3, 3.2) ) # Currently we need to manually merge the two together datapoly <- merge(values, positions, by = c("id")) p <- ggplot(datapoly, aes(x = x, y = y)) + geom_polygon(aes(fill = value, group = id)) p# Which seems like a lot of work, but then it's easy to add on # other features in this coordinate system, e.g.: stream <- data.frame( x = cumsum(runif(50, max = 0.1)), y = cumsum(runif(50,max = 0.1)) ) p + geom_line(data = stream, colour = "grey30", size = 5)# And if the positions are in longitude and latitude, you can use # coord_map to produce different map projections.