create plotly parallel categories diagram from alluvial plot

creates an interactive parallel categories diagram from an 'easyalluvial' plot using the 'plotly.js' library

parcats(
  p,
  marginal_histograms = TRUE,
  data_input = NULL,
  imp = TRUE,
  width = NULL,
  height = NULL,
  elementId = NULL,
  hoveron = "color",
  hoverinfo = "count+probability",
  arrangement = "perpendicular",
  bundlecolors = TRUE,
  sortpaths = "forward",
  labelfont = list(size = 24, color = "black"),
  tickfont = NULL,
  offset_marginal_histograms = 0.7,
  offset_imp = 0.9
)

Arguments

p

alluvial plot

marginal_histograms

logical, add marginal histograms, Default: TRUE

data_input

dataframe, data used to create alluvial plot, Default: NULL

imp

dataframe, with not more then two columns one of them numeric containing importance measures and one character or factor column containing corresponding variable names as found in training data.

width

integer, htmlwidget width in pixels, Default: NULL

height

integer, htmlwidget height in pixels, Default: NULL

elementId

, htmlwidget elementid, Default: NULL

hoveron

character, one of c('category', 'color', 'dimension'), Sets the hover interaction mode for the parcats diagram.', 'If `category`, hover interaction take place per category.', 'If `color`, hover interactions take place per color per category.', 'If `dimension`, hover interactions take place across all categories per dimension., Default: 'color'

hoverinfo

character, one of c('count', 'probability', 'count+probability') set info displayed on mouse hover Default: 'count+probability'

arrangement,

character, one of c('perpendicular', 'freeform', 'fixed') 'Sets the drag interaction mode for categories and dimensions.', 'If `perpendicular`, the categories can only move along a line perpendicular to the paths.', 'If `freeform`, the categories can freely move on the plane.', 'If `fixed`, the categories and dimensions are stationary.', Default: 'perpendicular'

bundlecolors

logical, 'Sort paths so that like colors are bundled together within each category.', Default: TRUE

sortpaths

character, one of c('forward', 'backward'), 'Sets the path sorting algorithm.', 'If `forward`, sort paths based on dimension categories from left to right.', Default: 'forward' 'If `backward`, sort paths based on dimensions categories from right to left.'

labelfont

list, 'Sets the font for the `dimension` labels.', Default: list(size = 24, color = 'black')

tickfont

list, Sets the font for the `category` labels.', Default: NULL

offset_marginal_histograms

double, height ratio reserved for parcats diagram, Default: 0.8

offset_imp

double, width ratio reserved for parcats diagram, Default: 0.9

Value

htmlwidget

Details

most parameters are best left at default values

See also

alluvial_wide , alluvial_long , alluvial_model_response , alluvial_model_response_caret

Examples

# \donttest{
library(easyalluvial)

# alluvial wide ---------------------------------
p = alluvial_wide(mtcars2, max_variables = 5)
#> Warning: There was 1 warning in `mutate()`.
#> ℹ In argument: `mpg = (function (f, na_level = "(Missing)") ...`.
#> Caused by warning:
#> ! `fct_explicit_na()` was deprecated in forcats 1.0.0.
#> ℹ Please use `fct_na_value_to_level()` instead.
#> ℹ The deprecated feature was likely used in the easyalluvial package.
#>   Please report the issue to the authors.

parcats(p, marginal_histograms = FALSE)


parcats(p, marginal_histograms = TRUE, data_input = mtcars2)


if(check_pkg_installed("randomForest", raise_error = FALSE)) {
  # alluvial for model response --------------------
  df = mtcars2[, ! names(mtcars2) %in% 'ids' ]
  m = randomForest::randomForest( disp ~ ., df)
  imp = m$importance
  dspace = get_data_space(df, imp, degree = 3)
  pred = predict(m, newdata = dspace)
  p = alluvial_model_response(pred, dspace, imp, degree = 3)

  parcats(p, marginal_histograms = TRUE, imp = TRUE, data_input = df)
}

# }