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  "Title": "Functions to Support Data Management and Processing Using the\nMaelstrom Research Approach",
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  "Description": "Functions to support data cleaning, evaluation, and\ndescription, developed for integration with Maelstrom Research\nsoftware tools. 'madshapR' provides functions primarily to\nevaluate and manipulate datasets and data dictionaries in\npreparation for data harmonization with the package 'Rmonize'\nand to facilitate integration and transfer between RStudio\nservers and secure Opal environments. 'madshapR' functions can\nbe used independently but are optimized in conjunction with\n‘Rmonize’ functions for streamlined and coherent harmonization\nprocessing.",
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  "Date/Publication": "2025-07-25 15:33:44 UTC",
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    "as_data_dict",
    "as_data_dict_mlstr",
    "as_data_dict_shape",
    "as_dataset",
    "as_dossier",
    "as_taxonomy",
    "as_valueType",
    "bookdown_open",
    "bookdown_render",
    "bookdown_template",
    "check_data_dict_categories",
    "check_data_dict_missing_categories",
    "check_data_dict_valueType",
    "check_data_dict_variables",
    "check_dataset_categories",
    "check_dataset_valueType",
    "check_dataset_variables",
    "check_name_standards",
    "col_id",
    "data_dict_apply",
    "data_dict_collapse",
    "data_dict_evaluate",
    "data_dict_expand",
    "data_dict_extract",
    "data_dict_filter",
    "data_dict_group_by",
    "data_dict_group_split",
    "data_dict_list_nest",
    "data_dict_match_dataset",
    "data_dict_pivot_longer",
    "data_dict_pivot_wider",
    "data_dict_trim_labels",
    "data_dict_ungroup",
    "data_dict_update",
    "data_extract",
    "dataset_cat_as_labels",
    "dataset_evaluate",
    "dataset_preprocess",
    "dataset_summarize",
    "dataset_visualize",
    "dataset_zap_data_dict",
    "dossier_create",
    "dossier_evaluate",
    "dossier_summarize",
    "drop_category",
    "first_label_get",
    "has_categories",
    "is_category",
    "is_data_dict",
    "is_data_dict_mlstr",
    "is_data_dict_shape",
    "is_dataset",
    "is_dossier",
    "is_taxonomy",
    "is_valueType",
    "madshapR_website",
    "summary_variables",
    "summary_variables_categorical",
    "summary_variables_date",
    "summary_variables_datetime",
    "summary_variables_numeric",
    "summary_variables_text",
    "typeof_convert_to_valueType",
    "valueType_adjust",
    "valueType_convert_to_typeof",
    "valueType_guess",
    "valueType_of",
    "valueType_self_adjust",
    "variable_visualize"
  ],
  "_datasets": [
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      "name": "color_palette_maelstrom",
      "title": "Built-in data frame of colors used in the graphs and charts.",
      "object": "color_palette_maelstrom",
      "class": [
        "tbl_df",
        "tbl",
        "data.frame"
      ],
      "fields": [
        "values",
        "color_palette"
      ],
      "rows": 54,
      "table": true,
      "tojson": true
    },
    {
      "name": "madshapR_examples",
      "title": "Built-in material allowing the user to test the package with example data",
      "object": "madshapR_examples",
      "class": [
        "list"
      ],
      "fields": [],
      "table": false,
      "tojson": true
    },
    {
      "name": "valueType_list",
      "title": "Built-in data frame of allowed valueType values",
      "object": "valueType_list",
      "class": [
        "tbl_df",
        "tbl",
        "data.frame"
      ],
      "fields": [
        "valueType",
        "typeof",
        "class",
        "explicit_class",
        "call",
        "toValueType",
        "genericType"
      ],
      "rows": 12,
      "table": true,
      "tojson": true
    }
  ],
  "_help": [
    {
      "page": "as_category",
      "title": "Validate and coerce any object as a categorical variable.",
      "topics": [
        "as_category"
      ]
    },
    {
      "page": "as_data_dict",
      "title": "Validate and coerce any object as a data dictionary",
      "topics": [
        "as_data_dict"
      ]
    },
    {
      "page": "as_data_dict_mlstr",
      "title": "Validate and coerce any object as an Opal data dictionary format",
      "topics": [
        "as_data_dict_mlstr"
      ]
    },
    {
      "page": "as_data_dict_shape",
      "title": "Validate and coerce any object as a workable data dictionary structure",
      "topics": [
        "as_data_dict_shape"
      ]
    },
    {
      "page": "as_dataset",
      "title": "Validate and coerce any object as a dataset",
      "topics": [
        "as_dataset"
      ]
    },
    {
      "page": "as_dossier",
      "title": "Validate and coerce any object as a dossier (list of dataset(s))",
      "topics": [
        "as_dossier"
      ]
    },
    {
      "page": "as_taxonomy",
      "title": "Validate and coerce any object as a taxonomy",
      "topics": [
        "as_taxonomy"
      ]
    },
    {
      "page": "as_valueType",
      "title": "Validate and coerce any object according to a given valueType",
      "topics": [
        "as_valueType"
      ]
    },
    {
      "page": "bookdown_open",
      "title": "Objects exported from other packages",
      "topics": [
        "bookdown_open"
      ]
    },
    {
      "page": "bookdown_render",
      "title": "Objects exported from other packages",
      "topics": [
        "bookdown_render"
      ]
    },
    {
      "page": "bookdown_template",
      "title": "Objects exported from other packages",
      "topics": [
        "bookdown_template"
      ]
    },
    {
      "page": "check_data_dict_categories",
      "title": "Assess a data dictionary for potential issues in categories",
      "topics": [
        "check_data_dict_categories"
      ]
    },
    {
      "page": "check_data_dict_missing_categories",
      "title": "Assess categorical variables for non-Boolean values in 'missing' column",
      "topics": [
        "check_data_dict_missing_categories"
      ]
    },
    {
      "page": "check_data_dict_valueType",
      "title": "Assess a data dictionary for non-valid valueType values",
      "topics": [
        "check_data_dict_valueType"
      ]
    },
    {
      "page": "check_data_dict_variables",
      "title": "Assess a data dictionary for potential issues in variables",
      "topics": [
        "check_data_dict_variables"
      ]
    },
    {
      "page": "check_dataset_categories",
      "title": "Assess a data dictionary and associated dataset for category differences",
      "topics": [
        "check_dataset_categories"
      ]
    },
    {
      "page": "check_dataset_valueType",
      "title": "Assess a data dictionary and associated dataset for valueType differences",
      "topics": [
        "check_dataset_valueType"
      ]
    },
    {
      "page": "check_dataset_variables",
      "title": "Assess a data dictionary and associated dataset for undeclared variables",
      "topics": [
        "check_dataset_variables"
      ]
    },
    {
      "page": "check_name_standards",
      "title": "Assess variable names in a data dictionary for non-standard formats",
      "topics": [
        "check_name_standards"
      ]
    },
    {
      "page": "col_id",
      "title": "Return the id column names(s) of a dataset",
      "topics": [
        "col_id"
      ]
    },
    {
      "page": "color_palette_maelstrom",
      "title": "Built-in data frame of colors used in the graphs and charts.",
      "topics": [
        "color_palette_maelstrom"
      ]
    },
    {
      "page": "data_dict_apply",
      "title": "Apply a data dictionary to a dataset",
      "topics": [
        "data_dict_apply"
      ]
    },
    {
      "page": "data_dict_collapse",
      "title": "Transform multi-row category column(s) to single rows and join to \"Variables\"",
      "topics": [
        "data_dict_collapse"
      ]
    },
    {
      "page": "data_dict_evaluate",
      "title": "Generate an assessment report for a data dictionary",
      "topics": [
        "data_dict_evaluate"
      ]
    },
    {
      "page": "data_dict_expand",
      "title": "Transform single-row category information to multiple rows as element",
      "topics": [
        "data_dict_expand"
      ]
    },
    {
      "page": "data_dict_extract",
      "title": "Generate a data dictionary from a dataset",
      "topics": [
        "data_dict_extract"
      ]
    },
    {
      "page": "data_dict_filter",
      "title": "Subset data dictionary by row values",
      "topics": [
        "data_dict_filter"
      ]
    },
    {
      "page": "data_dict_group_by",
      "title": "Group listed data dictionaries by specified column names",
      "topics": [
        "data_dict_group_by"
      ]
    },
    {
      "page": "data_dict_group_split",
      "title": "Split grouped data dictionaries into a named list",
      "topics": [
        "data_dict_group_split"
      ]
    },
    {
      "page": "data_dict_list_nest",
      "title": "Bind listed data dictionaries",
      "topics": [
        "data_dict_list_nest"
      ]
    },
    {
      "page": "data_dict_match_dataset",
      "title": "Inner join between a dataset and its associated data dictionary",
      "topics": [
        "data_dict_match_dataset"
      ]
    },
    {
      "page": "data_dict_pivot_longer",
      "title": "Transform column(s) of a data dictionary from wide format to long format",
      "topics": [
        "data_dict_pivot_longer"
      ]
    },
    {
      "page": "data_dict_pivot_wider",
      "title": "Transform column(s) of a data dictionary from long format to wide format",
      "topics": [
        "data_dict_pivot_wider"
      ]
    },
    {
      "page": "data_dict_trim_labels",
      "title": "Add shortened labels to data dictionary",
      "topics": [
        "data_dict_trim_labels"
      ]
    },
    {
      "page": "data_dict_ungroup",
      "title": "Ungroup data dictionary",
      "topics": [
        "data_dict_ungroup"
      ]
    },
    {
      "page": "data_dict_update",
      "title": "Update a data dictionary from a dataset",
      "topics": [
        "data_dict_update"
      ]
    },
    {
      "page": "data_extract",
      "title": "Create an empty dataset from a data dictionary",
      "topics": [
        "data_extract"
      ]
    },
    {
      "page": "dataset_cat_as_labels",
      "title": "Apply data dictionary category labels to the associated dataset variables",
      "topics": [
        "dataset_cat_as_labels"
      ]
    },
    {
      "page": "dataset_evaluate",
      "title": "Generate an assessment report for a dataset",
      "topics": [
        "dataset_evaluate"
      ]
    },
    {
      "page": "dataset_preprocess",
      "title": "Generate an evaluation of all variable values in a dataset",
      "topics": [
        "dataset_preprocess"
      ]
    },
    {
      "page": "dataset_summarize",
      "title": "Generate an assessment report and summary of a dataset",
      "topics": [
        "dataset_summarize"
      ]
    },
    {
      "page": "dataset_visualize",
      "title": "Generate a web-based visual report for a dataset",
      "topics": [
        "dataset_visualize"
      ]
    },
    {
      "page": "dataset_zap_data_dict",
      "title": "Remove labels (attributes) from a data frame, leaving its unlabelled columns",
      "topics": [
        "dataset_zap_data_dict"
      ]
    },
    {
      "page": "dossier_create",
      "title": "Generate a dossier from a list of one or more datasets",
      "topics": [
        "dossier_create"
      ]
    },
    {
      "page": "dossier_evaluate",
      "title": "Generate an assessment report of a dossier",
      "topics": [
        "dossier_evaluate"
      ]
    },
    {
      "page": "dossier_summarize",
      "title": "Generate an assessment report and summary of a dossier",
      "topics": [
        "dossier_summarize"
      ]
    },
    {
      "page": "drop_category",
      "title": "Validate and coerce any object as a non-categorical variable.",
      "topics": [
        "drop_category"
      ]
    },
    {
      "page": "first_label_get",
      "title": "Get the first label from a data dictionary",
      "topics": [
        "first_label_get"
      ]
    },
    {
      "page": "has_categories",
      "title": "Test if an object has categorical variables.",
      "topics": [
        "has_categories"
      ]
    },
    {
      "page": "is_category",
      "title": "Test and validate if an object is a categorical variable.",
      "topics": [
        "is_category"
      ]
    },
    {
      "page": "is_data_dict",
      "title": "Test if an object is a valid data dictionary",
      "topics": [
        "is_data_dict"
      ]
    },
    {
      "page": "is_data_dict_mlstr",
      "title": "Test if an object is a valid Maelstrom data dictionary",
      "topics": [
        "is_data_dict_mlstr"
      ]
    },
    {
      "page": "is_data_dict_shape",
      "title": "Test if an object is a workable data dictionary structure",
      "topics": [
        "is_data_dict_shape"
      ]
    },
    {
      "page": "is_dataset",
      "title": "Test if an object is a valid dataset",
      "topics": [
        "is_dataset"
      ]
    },
    {
      "page": "is_dossier",
      "title": "Test if an object is a valid dossier (list of dataset(s))",
      "topics": [
        "is_dossier"
      ]
    },
    {
      "page": "is_taxonomy",
      "title": "Test if an object is a valid taxonomy",
      "topics": [
        "is_taxonomy"
      ]
    },
    {
      "page": "is_valueType",
      "title": "Test if a character object is one of the valid valueType values",
      "topics": [
        "is_valueType"
      ]
    },
    {
      "page": "madshapR_examples",
      "title": "Built-in material allowing the user to test the package with example data",
      "topics": [
        "madshapR_examples"
      ]
    },
    {
      "page": "madshapR_website",
      "title": "Call to online documentation",
      "topics": [
        "madshapR_website"
      ]
    },
    {
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