# geom_bar

0th

Percentile

##### Bars, rectangles with bases on x-axis

There are two types of bar charts, determined by what is mapped to bar height. By default, geom_bar uses stat="count" which makes the height of the bar proportion 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 stat="identity" and map a variable to the y aesthetic.

stat_count counts the number of cases at each x position. If you want to bin the data in ranges, you should use stat_bin instead.

##### Usage
geom_bar(mapping = NULL, data = NULL, stat = "count", position = "stack", ..., width = NULL, binwidth = 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), removes missing values with a warning. If TRUE silently removes missing values.
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 maps the height of the bar to a variable, 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 x's occurring in the same place will be stacked atop one another by position_stack. If you want them to be dodged side-to-side, see position_dodge. Finally, position_fill shows relative proportions at each x by stacking the bars and then stretching or squashing to the same height.

##### Aesthetics

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

• x
• alpha
• colour
• fill
• linetype
• size

##### Computed variables

count
number of points in bin
prop
groupwise proportion

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.

• geom_bar
• stat_count
##### 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 stat = "identity"
df <- data.frame(trt = c("a", "b", "c"), outcome = c(2.3, 1.9, 3.2))
ggplot(df, aes(trt, outcome)) +
geom_bar(stat = "identity")
# But geom_point() display 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()


Documentation reproduced from package ggplot2, version 2.1.0, License: GPL-2

### Community examples

may.goddemon@gmail.com at Jan 2, 2019 ggplot2 v3.1.0

library(ggplot2) df=data.frame(M=rep(month.abb, times=2), value=c(sample(100:200, 12), sample(-200:-100,12)), col=rep(letters[1:2], each=12)) ggplot(df)+ geom_col(aes(x=M, y=value, fill=col))+ geom_text(aes(x=M, y=value*1.12, label=value))

nishi.niharika2992@gmail.com at Oct 15, 2020 ggplot2 v1.0.1

dt=data.frame(Country=c("India", "Japan","London", "NYC"), User=c(1200,3000,7000,600)) attach(dt) ggplot(dt, aes(x=User,y=Country)) + geom_bar()

nguyenthao_m@yahoo.com at Sep 10, 2017 ggplot2 v1.0.1

ggplot(diamonds, aes(x=clarity,fill=clarity)) + geom_bar()+ scale_fill_manual(values=c("#FF6666")) + theme(legend.position = 'none')+ theme_bw()