There are two types of bar charts: geom_bar makes the height of the bar proportional to the number of cases in each group (or if the weight aethetic 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, binwidth = NULL, na.rm = FALSE,
  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,
  show.legend = NA, inherit.aes = TRUE)

Arguments

mapping

Set of aesthetic mappings created by aes or aes_. If specified and inherit.aes = TRUE (the default), it is combined with the default mapping at the top level of the plot. You must supply mapping if there is no plot mapping.

data

The data to be displayed in this layer. There are three options:

If NULL, the default, the data is inherited from the plot data as specified in the call to ggplot.

A data.frame, or other object, will override the plot data. All objects will be fortified to produce a data frame. See fortify for which variables will be created.

A function will be called with a single argument, the plot data. The return value must be a data.frame., and will be used as the layer data.

position

Position adjustment, either as a string, or the result of a call to a position adjustment function.

...

other arguments passed on to layer. These are often aesthetics, used to set an aesthetic to a fixed value, like color = "red" or size = 3. They may also be parameters to the paired geom/stat.

width

Bar width. By default, set to 90% of the resolution of the data.

binwidth

geom_bar no longer has a binwidth argument - if you use it you'll get an warning telling to you use geom_histogram instead.

na.rm

If FALSE, the default, missing values are removed with a warning. If TRUE, missing values are silently removed.

show.legend

logical. Should this layer be included in the legends? NA, the default, includes if any aesthetics are mapped. FALSE never includes, and TRUE always includes.

inherit.aes

If FALSE, overrides the default aesthetics, rather than combining with them. This is most useful for helper functions that define both data and aesthetics and shouldn't inherit behaviour from the default plot specification, e.g. borders.

geom, stat

Override the default connection between geom_bar and stat_count.

Details

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. Naomi Robbins has a nice http://www.b-eye-network.com/view/index.php?cid=2468. This is why it doesn't make sense to use a log-scaled y axis with a bar chart.

By default, multiple bar 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. Finally, position_fill shows relative proportions at each x by stacking the bars and then standardising each bar to have the same height.

Aesthetics

geom_bar understands the following aesthetics (required aesthetics are in bold):

  • x

  • y

  • alpha

  • colour

  • fill

  • group

  • linetype

  • size

Computed variables

count

number of points in bin

prop

groupwise proportion

See also

geom_histogram for continuous data, position_dodge for creating side-by-side barcharts. 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.

Examples

# 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))
# To show (e.g.) means, you need geom_col() # And, even more succinctly with 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)
# Bar charts are automatically stacked when multiple bars are placed # at the same location g + geom_bar(aes(fill = drv))
# You can instead dodge, or fill them g + geom_bar(aes(fill = drv), position = "dodge")
g + geom_bar(aes(fill = drv), position = "fill")
# To change plot order of bars, change levels in underlying factor reorder_size <- function(x) { factor(x, levels = names(sort(table(x)))) } ggplot(mpg, aes(reorder_size(class))) + geom_bar()