gg2density              package:ggplot              R Documentation

_G_r_o_b _f_u_n_c_t_i_o_n: _2_d _d_e_n_s_i_t_y

_D_e_s_c_r_i_p_t_i_o_n:

     Perform a 2D kernel density estimatation using 'kde2d' and

_U_s_a_g_e:

     gg2density(plot = .PLOT, aesthetics=list(), ..., data=plot$data)

_A_r_g_u_m_e_n_t_s:

    plot: the plot object to modify

aesthetics: named list of aesthetic mappings, see details for more
          information

     ...: other options, see details for more information

    data: data source, if not specified the plot default will be used

_D_e_t_a_i_l_s:

     This is another function useful for dealing with overplotting.

     Aesthetic mappings that this grob function understands:

        *  x: x position (required)

        *  y: y position (required)

     These can be specified in the plot defaults (see 'ggplot') or in
     the 'aesthetics' argument.  If you want to modify the position of
     the points or any axis options, you will need to add a position
     scale to the plot.  These functions start with 'ps', eg.
     'pscontinuous' or 'pscategorical'

     Other options:

        *  passed to 'ggcontour', see it for details

_A_u_t_h_o_r(_s):

     Hadley Wickham <h.wickham@gmail.com>

_S_e_e _A_l_s_o:

     'ggcontour', 'gghexagon' for another way of dealing with over
     plotting

_E_x_a_m_p_l_e_s:

     m <- ggpoint(ggplot(movies, aesthetics=list(y=length, x=rating)))
     dens <- MASS::kde2d(movies$rating, movies$length)
     densdf <- data.frame(expand.grid(rating = dens$x, length = dens$y), z=as.vector(dens$z))
     ggcontour(m, list(z=z), data=densdf)
     gg2density(m)
     # they don't look the same due to scaling effects on kde2d

