Data-Driven Plot Design

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. The user may choose between trellis and ggplot output. Compact syntax and integrated metadata promote workflow scalability.


Reference manual

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0.8.3 by Tim Bergsma, a year ago

Browse source code at

Authors: Tim Bergsma

Documentation:   PDF Manual  

GPL-3 license

Imports encode, lattice, magrittr, dplyr, tidyr, rlang, grid, gridExtra, gtable, ggplot2, scales

Suggests csv, nlme

Imported by nonmemica.

See at CRAN