{
  "_id": "6a1bf3391d7bb097a0a245b2",
  "Package": "easyalluvial",
  "Title": "Generate Alluvial Plots with a Single Line of Code",
  "Version": "0.4.0",
  "Authors@R": "person( \"Bjoern\", \"Koneswarakantha\", role = c(\"aut\",\"cre\"), email = \"datistics@gmail.com\", comment = c(ORCID = \"0000-0003-4585-7799\") )",
  "URL": "https://github.com/erblast/easyalluvial/,\nhttps://erblast.github.io/easyalluvial/",
  "BugReports": "https://github.com/erblast/easyalluvial/issues",
  "Description": "Alluvial plots are similar to sankey diagrams and\nvisualise categorical data over multiple dimensions as flows.\n(Rosvall M, Bergstrom CT (2010) Mapping Change in Large\nNetworks. PLoS ONE 5(1): e8694.\n<doi:10.1371/journal.pone.0008694> Their graphical grammar\nhowever is a bit more complex then that of a regular x/y plots.\nThe 'ggalluvial' package made a great job of translating that\ngrammar into 'ggplot2' syntax and gives you many options to\ntweak the appearance of an alluvial plot, however there still\nremains a multi-layered complexity that makes it difficult to\nuse 'ggalluvial' for explorative data analysis. 'easyalluvial'\nprovides a simple interface to this package that allows you to\nproduce a decent alluvial plot from any dataframe in either\nlong or wide format from a single line of code while also\nhandling continuous data. It is meant to allow a quick\nvisualisation of entire dataframes with a focus on different\ncolouring options that can make alluvial plots a great tool for\ndata exploration.",
  "License": "CC0",
  "Encoding": "UTF-8",
  "LazyData": "true",
  "RoxygenNote": "7.3.2",
  "Language": "en-US",
  "Config/pak/sysreqs": "libicu-dev",
  "Repository": "https://erblast.r-universe.dev",
  "Date/Publication": "2025-09-03 12:52:20 UTC",
  "RemoteUrl": "https://github.com/erblast/easyalluvial",
  "RemoteRef": "HEAD",
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  "NeedsCompilation": "no",
  "Packaged": {
    "Date": "2026-05-31 08:31:57 UTC",
    "User": "root"
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  "Author": "Bjoern Koneswarakantha [aut, cre] (ORCID:\n<https://orcid.org/0000-0003-4585-7799>)",
  "Maintainer": "Bjoern Koneswarakantha <datistics@gmail.com>",
  "MD5sum": "8c002c79592a0b2dbcaa249c8574f94b",
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    "committer": "GitHub <noreply@github.com>",
    "message": "Merge pull request #43 from erblast/reduce_build_size\n\nreduce build size",
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    "extra/contents.json",
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    "extra/NEWS.txt",
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    "extra/readme.md",
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  "_realowner": "erblast",
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    },
    {
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    },
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    },
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      "date": "2019-12-09"
    },
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    },
    {
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    },
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      "date": "2023-12-07"
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    "%>%",
    "add_imp_plot",
    "add_marginal_histograms",
    "alluvial_long",
    "alluvial_model_response",
    "alluvial_model_response_caret",
    "alluvial_model_response_parsnip",
    "alluvial_wide",
    "check_pkg_installed",
    "get_data_space",
    "get_pdp_predictions",
    "manip_bin_numerics",
    "manip_factor_2_numeric",
    "manip_get_ggplot_data",
    "palette_filter",
    "palette_increase_length",
    "palette_plot_intensity",
    "palette_plot_rgp",
    "palette_qualitative",
    "plot_all_hists",
    "plot_condensation",
    "plot_hist",
    "plot_imp",
    "tidy_imp"
  ],
  "_datasets": [
    {
      "name": "mtcars2",
      "title": "mtcars dataset with cyl, vs, am ,gear, carb as factor variables and car model names as id",
      "object": "mtcars2",
      "class": [
        "tbl_df",
        "tbl",
        "data.frame"
      ],
      "fields": [
        "mpg",
        "cyl",
        "disp",
        "hp",
        "drat",
        "wt",
        "qsec",
        "vs",
        "am",
        "gear",
        "carb",
        "ids"
      ],
      "rows": 32,
      "table": true,
      "tojson": true
    },
    {
      "name": "quarterly_flights",
      "title": "Quarterly mean arrival delay times for a set of 402 flights",
      "object": "quarterly_flights",
      "class": [
        "tbl_df",
        "tbl",
        "data.frame"
      ],
      "fields": [
        "tailnum",
        "carrier",
        "origin",
        "dest",
        "qu",
        "mean_arr_delay"
      ],
      "rows": 1608,
      "table": true,
      "tojson": true
    },
    {
      "name": "quarterly_sunspots",
      "title": "Quarterly mean relative sunspots number from 1749-1983",
      "object": "quarterly_sunspots",
      "class": [
        "tbl_df",
        "tbl",
        "data.frame"
      ],
      "fields": [
        "year",
        "qu",
        "spots",
        "mean_spots_per_year"
      ],
      "rows": 940,
      "table": true,
      "tojson": true
    },
    {
      "name": "titanic",
      "title": "titanic data set'",
      "object": "titanic",
      "class": [
        "tbl_df",
        "tbl",
        "data.frame"
      ],
      "fields": [
        "Survived",
        "Pclass",
        "Sex",
        "Age",
        "SibSp",
        "Parch",
        "Fare",
        "Cabin",
        "Embarked",
        "title"
      ],
      "rows": 891,
      "table": true,
      "tojson": true
    }
  ],
  "_help": [
    {
      "page": "add_imp_plot",
      "title": "add bar plot of important features to model response alluvial plot",
      "topics": [
        "add_imp_plot"
      ]
    },
    {
      "page": "add_marginal_histograms",
      "title": "add marginal histograms to alluvial plot",
      "topics": [
        "add_marginal_histograms"
      ]
    },
    {
      "page": "alluvial_long",
      "title": "alluvial plot of data in long format",
      "topics": [
        "alluvial_long"
      ]
    },
    {
      "page": "alluvial_model_response",
      "title": "create model response plot",
      "topics": [
        "alluvial_model_response"
      ]
    },
    {
      "page": "alluvial_model_response_caret",
      "title": "create model response plot for caret models",
      "topics": [
        "alluvial_model_response_caret"
      ]
    },
    {
      "page": "alluvial_model_response_parsnip",
      "title": "create model response plot for parsnip models",
      "topics": [
        "alluvial_model_response_parsnip"
      ]
    },
    {
      "page": "alluvial_wide",
      "title": "alluvial plot of data in wide format",
      "topics": [
        "alluvial_wide"
      ]
    },
    {
      "page": "check_pkg_installed",
      "title": "check if package is installed",
      "topics": [
        "check_pkg_installed"
      ]
    },
    {
      "page": "get_data_space",
      "title": "calculate data space",
      "topics": [
        "get_data_space"
      ]
    },
    {
      "page": "get_pdp_predictions",
      "title": "get predictions compatible with the partial dependence plotting method",
      "topics": [
        "get_pdp_predictions"
      ]
    },
    {
      "page": "get_pdp_predictions_seq",
      "title": "get predictions compatible with the partial dependence plotting method, sequential variant that only works for numeric predictions.",
      "topics": [
        "get_pdp_predictions_seq"
      ]
    },
    {
      "page": "manip_bin_numerics",
      "title": "bin numerical columns",
      "topics": [
        "manip_bin_numerics"
      ]
    },
    {
      "page": "manip_factor_2_numeric",
      "title": "converts factor to numeric preserving numeric levels and order in character levels.",
      "topics": [
        "manip_factor_2_numeric"
      ]
    },
    {
      "page": "manip_get_ggplot_data",
      "title": "Get ggplot data",
      "topics": [
        "manip_get_ggplot_data"
      ]
    },
    {
      "page": "mtcars2",
      "title": "mtcars dataset with cyl, vs, am ,gear, carb as factor variables and car model names as id",
      "topics": [
        "mtcars2"
      ]
    },
    {
      "page": "palette_filter",
      "title": "color filters for any vector of hex color values",
      "topics": [
        "palette_filter"
      ]
    },
    {
      "page": "palette_increase_length",
      "title": "increases length of palette by repeating colours",
      "topics": [
        "palette_increase_length"
      ]
    },
    {
      "page": "palette_plot_intensity",
      "title": "plot colour intensity of palette",
      "topics": [
        "palette_plot_intensity"
      ]
    },
    {
      "page": "palette_plot_rgp",
      "title": "plot rgb values of palette",
      "topics": [
        "palette_plot_rgp"
      ]
    },
    {
      "page": "palette_qualitative",
      "title": "compose palette from qualitative RColorBrewer palettes",
      "topics": [
        "palette_qualitative"
      ]
    },
    {
      "page": "plot_all_hists",
      "title": "plot marginal histograms of alluvial plot",
      "topics": [
        "plot_all_hists"
      ]
    },
    {
      "page": "plot_condensation",
      "title": "Plot dataframe condensation potential",
      "topics": [
        "plot_condensation"
      ]
    },
    {
      "page": "plot_hist",
      "title": "plot histogram of alluvial plot variable",
      "topics": [
        "plot_hist"
      ]
    },
    {
      "page": "plot_imp",
      "title": "plot feature importance",
      "topics": [
        "plot_imp"
      ]
    },
    {
      "page": "quarterly_flights",
      "title": "Quarterly mean arrival delay times for a set of 402 flights",
      "topics": [
        "quarterly_flights"
      ]
    },
    {
      "page": "quarterly_sunspots",
      "title": "Quarterly mean relative sunspots number from 1749-1983",
      "topics": [
        "quarterly_sunspots"
      ]
    },
    {
      "page": "tidy_imp",
      "title": "tidy up dataframe containing model feature importance",
      "topics": [
        "tidy_imp"
      ]
    },
    {
      "page": "titanic",
      "title": "titanic data set'",
      "topics": [
        "titanic"
      ]
    }
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