R: Bin data along a continuous variable

compute_bin {ggvis} | R Documentation

## Bin data along a continuous variable

### Description

Bin data along a continuous variable

### Usage

    compute_bin(x, x_var, w_var = NULL, width = NULL, center = NULL,
      boundary = NULL, closed = c("right", "left"), pad = FALSE, binwidth)

### Arguments

`x` | Dataset-like object to bin. Built-in methods for data frames, grouped data frames and ggvis visualisations.
`x_var, w_var` | Names of x and weight variables. The x variable must be continuous.
`width` | The width of the bins. The default is `NULL`, which yields 30 bins that cover the range of the data. You should always override this value, exploring multiple widths to find the best to illustrate the stories in your data.
`center` | The center of one of the bins. Note that if center is above or below the range of the data, things will be shifted by an appropriate number of `width`s. To center on integers, for example, use `width=1` and `center=0`, even if `0` is outside the range of the data. At most one of `center` and `boundary` may be specified.
`boundary` | A boundary between two bins. As with `center`, things are shifted when `boundary` is outside the range of the data. For example, to center on integers, use `width = 1` and `boundary = 0.5`, even if `1` is outside the range of the data. At most one of `center` and `boundary` may be specified.
`closed` | One of `"right"` or `"left"` indicating whether right or left edges of bins are included in the bin.
`pad` | If `TRUE`, adds empty bins at either end of x. This ensures frequency polygons touch 0. Defaults to `FALSE`.
`binwidth` | Deprecated; use `width` instead.

### Value

A data frame with columns:

`count_` | the number of points
`x_` | mid-point of bin
`xmin_` | left boundary of bin
`xmax_` | right boundary of bin
`width_` | width of bin

### See Also

`compute_count` For counting cases at specific locations of a continuous variable. This is useful when the variable is continuous but the data is granular.

### Examples

    mtcars %>% compute_bin(~mpg)
    mtcars %>% compute_bin(~mpg, width = 10)
    mtcars %>% group_by(cyl) %>% compute_bin(~mpg, width = 10)

    # It doesn't matter whether you transform inside or outside of a vis
    mtcars %>% compute_bin(~mpg) %>% ggvis(~x_, ~count_) %>% layer_paths()
    mtcars %>% ggvis(~ x_, ~ count_) %>% compute_bin(~mpg) %>% layer_paths()

    # Missing values get own bin
    mtcars2 <- mtcars
    mtcars2$mpg[sample(32, 5)] <- NA
    mtcars2 %>% compute_bin(~mpg, width = 10)

    # But are currently silently dropped in histograms
    mtcars2 %>% ggvis() %>% layer_histograms(~mpg)

[Package ggvis version 0.4.3 Index]