Bar charts
There are two types of bar charts: geom_bar() and geom_col().
geom_bar() makes the height of the bar proportional to the number of
cases in each group (or if the weight aesthetic is supplied, the sum
of the weights). If you want the heights of the bars to represent values
in the data, use geom_col() instead. geom_bar() uses stat_count() by
default: it counts the number of cases at each x position. geom_col()
uses stat_identity(): it leaves the data as is.
geom_bar( mapping = NULL, data = NULL, stat = "count", position = "stack", ..., width = NULL, na.rm = FALSE, orientation = NA, show.legend = NA, inherit.aes = TRUE ) geom_col( mapping = NULL, data = NULL, position = "stack", ..., width = NULL, na.rm = FALSE, show.legend = NA, inherit.aes = TRUE ) stat_count( mapping = NULL, data = NULL, geom = "bar", position = "stack", ..., width = NULL, na.rm = FALSE, orientation = NA, 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 |
position |
Position adjustment, either as a string, or the result of a call to a position adjustment function. |
... |
Other arguments passed on to |
width |
Bar width. By default, set to 90% of the resolution of the data. |
na.rm |
If |
orientation |
The orientation of the layer. The default ( |
show.legend |
logical. Should this layer be included in the legends?
|
inherit.aes |
If |
geom, stat |
Override the default connection between |
A bar chart uses height to represent a value, and so the base of the
bar must always be shown to produce a valid visual comparison.
Proceed with caution when using transformed scales with a bar chart.
It's important to always use a meaningful reference point for the base of the bar.
For example, for log transformations the reference point is 1. In fact, when
using a log scale, geom_bar() automatically places the base of the bar at 1.
Furthermore, never use stacked bars with a transformed scale, because scaling
happens before stacking. As a consequence, the height of bars will be wrong
when stacking occurs with a transformed scale.
By default, multiple bars occupying the same x position will be stacked
atop one another by position_stack(). If you want them to be dodged
side-to-side, use position_dodge() or position_dodge2(). Finally,
position_fill() shows relative proportions at each x by stacking the
bars and then standardising each bar to have the same height.
This geom treats each axis differently and, thus, can thus have two orientations. Often the orientation is easy to deduce from a combination of the given mappings and the types of positional scales in use. Thus, ggplot2 will by default try to guess which orientation the layer should have. Under rare circumstances, the orientation is ambiguous and guessing may fail. In that case the orientation can be specified directly using the orientation parameter, which can be either "x" or "y". The value gives the axis that the geom should run along, "x" being the default orientation you would expect for the geom.
geom_bar() 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_col() 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").
stat_count() understands the following aesthetics (required aesthetics are in bold):
x or y
group
weight
Learn more about setting these aesthetics in vignette("ggplot2-specs").
number of points in bin
groupwise proportion
geom_histogram() for continuous data,
position_dodge() and position_dodge2() for creating side-by-side
bar charts.
stat_bin(), which bins data in ranges and counts the
cases in each range. It differs from stat_count, which counts the
number of cases at each x position (without binning into ranges).
stat_bin() requires continuous x data, whereas
stat_count can be used for both discrete and continuous x data.
# geom_bar is designed to make it easy to create bar charts that show
# counts (or sums of weights)
g <- ggplot(mpg, aes(class))
# Number of cars in each class:
g + geom_bar()
# Total engine displacement of each class
g + geom_bar(aes(weight = displ))
# Map class to y instead to flip the orientation
ggplot(mpg) + geom_bar(aes(y = class))
# Bar charts are automatically stacked when multiple bars are placed
# at the same location. The order of the fill is designed to match
# the legend
g + geom_bar(aes(fill = drv))
# If you need to flip the order (because you've flipped the orientation)
# call position_stack() explicitly:
ggplot(mpg, aes(y = class)) +
geom_bar(aes(fill = drv), position = position_stack(reverse = TRUE)) +
theme(legend.position = "top")
# To show (e.g.) means, you need geom_col()
df <- data.frame(trt = c("a", "b", "c"), outcome = c(2.3, 1.9, 3.2))
ggplot(df, aes(trt, outcome)) +
geom_col()
# But geom_point() displays exactly the same information and doesn't
# require the y-axis to touch zero.
ggplot(df, aes(trt, outcome)) +
geom_point()
# You can also use geom_bar() with continuous data, in which case
# it will show counts at unique locations
df <- data.frame(x = rep(c(2.9, 3.1, 4.5), c(5, 10, 4)))
ggplot(df, aes(x)) + geom_bar()
# cf. a histogram of the same data
ggplot(df, aes(x)) + geom_histogram(binwidth = 0.5)Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.