geom_bar {ggplot2} | R Documentation

## Bars, rectangles with bases on x-axis

### Description

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

### 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 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()

[Package ggplot2 version 2.1.0 Index]