Generate Alluvial Plots with a Single Line of Code

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Documentation for package ‘easyalluvial’ version 0.3.2

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add_imp_plot add bar plot of important features to model response alluvial plot
add_marginal_histograms add marginal histograms to alluvial plot
alluvial_long alluvial plot of data in long format
alluvial_model_response create model response plot
alluvial_model_response_caret create model response plot for caret models
alluvial_model_response_parsnip create model response plot for parsnip models
alluvial_wide alluvial plot of data in wide format
check_pkg_installed check if package is installed
get_data_space calculate data space
get_pdp_predictions get predictions compatible with the partial dependence plotting method
get_pdp_predictions_seq get predictions compatible with the partial dependence plotting method, sequential variant that only works for numeric predictions.
manip_bin_numerics bin numerical columns
manip_factor_2_numeric converts factor to numeric preserving numeric levels and order in character levels.
mtcars2 mtcars dataset with cyl, vs, am ,gear, carb as factor variables and car model names as id
palette_filter color filters for any vector of hex color values
palette_increase_length increases length of palette by repeating colours
palette_plot_intensity plot colour intensity of palette
palette_plot_rgp plot rgb values of palette
palette_qualitative compose palette from qualitative RColorBrewer palettes
plot_all_hists plot marginal histograms of alluvial plot
plot_condensation Plot dataframe condensation potential
plot_hist plot histogram of alluvial plot variable
plot_imp plot feature importance
quarterly_flights Quarterly mean arrival delay times for a set of 402 flights
quarterly_sunspots Quarterly mean relative sunspots number from 1749-1983
tidy_imp tidy up dataframe containing model feature importance
titanic titanic data set'