R: Compute density of data.

compute_density {ggvis} | R Documentation

## Compute density of data.

### Description

Compute density of data.

### Usage

    compute_density(x, x_var, w_var = NULL, kernel = "gaussian", trim = FALSE,
      n = 256L, na.rm = FALSE, ...)

### Arguments

`x` | Dataset (data frame, `grouped_df` or ggvis) object to work with.
`x_var, w_var` | Names of variables to use for x position, and for weights.
`kernel` | Smoothing kernel. See `density` for details.
`trim` | If `TRUE`, the default, density estimates are trimmed to the actual range of the data. If `FALSE`, they are extended by the default 3 bandwidths (as specified by the `cut` parameter to `density`).
`n` | Number of points (along x) to use in the density estimate.
`na.rm` | If `TRUE` missing values will be silently removed, otherwise they will be removed with a warning.
`...` | Additional arguments passed on to `density`.

### Value

A data frame with columns:

`pred_` | regularly spaced grid of `n` locations
`resp_` | density estimate

### Examples

    mtcars %>% compute_density(~mpg, n = 5)
    mtcars %>% group_by(cyl) %>% compute_density(~mpg, n = 5)
    mtcars %>% ggvis(~mpg) %>% compute_density(~mpg, n = 5) %>%
      layer_points(~pred_, ~resp_)

[Package ggvis version 0.4.3 Index]