# parcats 0.0.3 released

**R on datistics**, and kindly contributed to R-bloggers]. (You can report issue about the content on this page here)

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`parcats 0.0.3`

was released on CRAN. It is an htmlwidget providing bindings to the `plotly.js`

parcats trace, which is not supported by the `plotly`

R package. It also adds marginal histograms for numerical variables.

# Better {shiny} Support

It now integrates better into `shiny`

apps. There is a new function `parcats_demo()`

which let’s you interactively explore all the different parameters of `easyalluvial::alluvial_wide()`

and `parcats::parcats()`

. You can see how the alluvial plot and the derived interactive parcats widget look like with different parameters.

# Update {plotly.js}

In order to be 100% compatible with R `plotly`

. `plotly.js`

that is shipped with `parcats`

has been upgraded to `v2.5.1`

# Parcats from Alluvial Plot

suppressPackageStartupMessages(require(tidyverse)) suppressPackageStartupMessages(require(easyalluvial)) suppressPackageStartupMessages(require(parcats)) suppressPackageStartupMessages(require(parsnip)) p = alluvial_wide(mtcars2, max_variables = 5) parcats(p, marginal_histograms = TRUE, data_input = mtcars2)

# Partial Dependence Alluvial Plots

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 parallel categories diagrams we can increase the number of dimensions.

df <- select(mtcars2, -ids) m <- parsnip::rand_forest(mode = "regression") %>% parsnip::set_engine("randomForest") %>% parsnip::fit(disp ~ ., df) p <- alluvial_model_response_parsnip(m, df, degree = 4, method = "pdp") ## Getting partial dependence plot preditions. This can take a while. See easyalluvial::get_pdp_predictions() `Details` on how to use multiprocessing parcats(p, marginal_histograms = TRUE, imp = TRUE, data_input = df)

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