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easyalluvial

parcats requires an alluvial plot created with easyalluvial to create an interactive parrallel categories diagram.

• Data exploration with alluvial plots

• easyalluvial github page

Examples

suppressPackageStartupMessages(require(dplyr))
suppressPackageStartupMessages(require(easyalluvial))
suppressPackageStartupMessages(require(parcats))

Parcats from alluvial from data in wide format

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 = TRUE, data_input = mtcars2)

Parcats from model response alluvial

Machine Learning models operate in a multidimensional space and their response is hard to visualise. Model response and partial dependency plots attempt to visualise ML models in a two dimensional space. Using alluvial plots or parrallel categories diagrams we can increase the number of dimensions.

• Visualise model response with alluvial plots

Here we see the response of a random forest model if we vary the three variables with the highest importance while keeping all other features at their median/mode value.

df <- select(mtcars2, -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)