Bars charts

Description

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.

Usage

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

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 article on this topic. 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()