geom_vline: Line, vertical.

Description

This geom allows you to annotate the plot with vertical lines (see geom_hline and geom_abline for other types of lines.

Usage

geom_vline(mapping = NULL, data = NULL, stat = "vline",
    position = "identity", show_guide = FALSE, ...)

Arguments

show_guide

should a legend be drawn? (defaults to FALSE)

mapping

The aesthetic mapping, usually constructed with aes or aes_string. Only needs to be set at the layer level if you are overriding the plot defaults.

data

A layer specific dataset - only needed if you want to override the plot defaults.

stat

The statistical transformation to use on the data for this layer.

position

The position adjustment to use for overlappling points on this layer

...

other arguments passed on to layer. This can include aesthetics whose values you want to set, not map. See layer for more details.

Aesthetics

[results=rd,stage=build]{ggplot2:::rd_aesthetics("geom", "vline")} # Fixed lines p <- ggplot(mtcars, aes(x = wt, y = mpg)) + geom_point() p + geom_vline(xintercept = 5) p + geom_vline(xintercept = 1:5) p + geom_vline(xintercept = 1:5, colour="green", linetype = "longdash") p + geom_vline(aes(xintercept = wt))

# With coordinate transforms p + geom_vline(aes(xintercept = wt)) + coord_equal() p + geom_vline(aes(xintercept = wt)) + coord_flip() p + geom_vline(aes(xintercept = wt)) + coord_polar()

p2 <- p + aes(colour = factor(cyl)) p2 + geom_vline(xintercept = 15)

# To display different lines in different facets, you need to # create a data frame. p <- qplot(mpg, wt, data=mtcars, facets = vs ~ am) vline.data <- data.frame(z = c(15, 20, 25, 30), vs = c(0, 0, 1, 1), am = c(0, 1, 0, 1)) p + geom_vline(aes(xintercept = z), vline.data)

geom_hline for horizontal lines, geom_abline for lines defined by a slope and intercept, geom_segment for a more general approach"

Details

There are two ways to use it. You can either specify the intercept of the line in the call to the geom, in which case the line will be in the same position in every panel. Alternatively, you can supply a different intercept for each panel using a data.frame. See the examples for the differences.