spreadout: Functions to prepare for categorical parallel coordinates,...

In PairViz: Visualization using Graph Traversal

Functions to prepare for categorical parallel coordinates, drawn by catpcp.

Description

factor_spreadout spreads out the data at each factor level. rater_spreadout spreads out the data at each rating level. The rater version is appropriate when the variables (factors) have all the same levels.

Usage

factor_spreadout(d)
rater_spreadout(d, levs, minspace = NULL,scale=FALSE)

Arguments

d	A data frame where each variable can be interpreted as a factor.
levs	The rating levels. Specifying this controls the order of rating levels on each axis.
minspace	The minimum amount of space between the bars.
scale	If scale=FALSE, the ith rater values are spreadout about the value i. If scale=TRUE, all values are scaled to 0-1.

Details

factor_spreadout spreads out the data at each factor level. It returns a list with two components. The first is data, containing the spreadout data, scaled to 0-1. The second is bars, which is a list whose ith component gives the bottom and top of the bars for the ith variable of d.

rater_spreadout spreads out the data at each rater level. It returns a list with two components. The first is data, containing the spreadout data. If scale=FALSE, the ith rater values are spreadout about the value i. If scale=TRUE, all values are scaled to 0-1. The second component is bars, which is a list whose ith component gives the bottom and top of the bars for the ith variable of d.

PairViz documentation built on Aug. 12, 2022, 5:06 p.m.