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

