Designs plots in terms of core structure.  See 'example(metaplot)'.
Primary arguments are (unquoted) column names; order and type (numeric or not)
dictate the resulting plot.  Specify any y variables, x variable, any groups variable,
and any conditioning variables to metaplot() to generate density plots, boxplots,
scatterplots, scatterplot matrices, or conditioned plots. Use multiplot() to arrange
plots in grids. Wherever present, scalar column attributes 'label' and 'guide' are
honored, producing fully annotated plots with minimal effort. Attribute 'guide'
is typically units, but may be encoded() to provide interpretations of categorical
values (see '?encode').  Utility unpack() transforms scalar column attributes to row
values and pack() does the reverse, supporting tool-neutral storage of metadata along
with primary data. The package supports customizable aesthetics such as such as reference
lines, unity lines, smooths, log transformation, and linear fits. Compact syntax and
integrated metadata promote workflow scalability.
encode (>= 0.3.2), lattice, magrittr, dplyr (>= 0.7.1), tidyr,
rlang

Designs plots in terms of core structure.  See 'example(metaplot)'.
Primary arguments are (unquoted) column names; order and type (numeric or not)
dictate the resulting plot.  Specify any y variables, x variable, any groups variable,
and any conditioning variables to metaplot() to generate density plots, boxplots,
mosaic plots, scatterplots, scatterplot matrices, or conditioned plots. Use multiplot()
to arrange plots in grids. Wherever present, scalar column attributes 'label' and 'guide'
are honored, producing fully annotated plots with minimal effort. Attribute 'guide'
is typically units, but may be encoded() to provide interpretations of categorical
values (see '?encode').  Utility unpack() transforms scalar column attributes to row
values and pack() does the reverse, supporting tool-neutral storage of metadata along
with primary data. The package supports customizable aesthetics such as such as reference
lines, unity lines, smooths, log transformation, and linear fits. Compact syntax and
integrated metadata promote workflow scalability.
encode (>= 0.3.2), lattice, magrittr, dplyr (>= 0.7.1), tidyr,
rlang, grid