parcats {parcats} | R Documentation |
create plotly parallel categories diagram from alluvial plot
Description
creates an interactive parallel categories diagram from an 'easyalluvial' plot using the 'plotly.js' library
Usage
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 |
Details
most parameters are best left at default values
Value
htmlwidget
See Also
alluvial_wide
, alluvial_long
, alluvial_model_response
, alluvial_model_response_caret
Examples
library(easyalluvial)
# alluvial wide ---------------------------------
p = alluvial_wide(mtcars2, max_variables = 5)
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)
}