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"%%.numeric_with_unit" ] }, { "page": "pow-.numeric_with_unit", "title": "Operator caring for units", "topics": [ "^.numeric_with_unit" ] }, { "page": "plus-.numeric_with_unit", "title": "Operator caring for units", "topics": [ "+.numeric_with_unit" ] }, { "page": "cash-.dataquieR_resultset2", "title": "Access single results from a dataquieR_resultset2 report", "topics": [ "$.dataquieR_resultset2", "cash-.dataquieR_resultset2" ] }, { "page": "cash-set-.dataquieR_resultset2", "title": "Write single results from a dataquieR_resultset2 report", "topics": [ "$<-.dataquieR_resultset2", "cash-set-.dataquieR_resultset2.Rd" ] }, { "page": "acc_cat_distributions", "title": "Plots and checks for distributions for categorical variables", "topics": [ "acc_cat_distributions" ] }, { "page": "acc_distributions", "title": "Plots and checks for distributions", "topics": [ "acc_distributions" ] }, { "page": "acc_distributions_ecdf", "title": "ECDF plots for distribution checks", "topics": [ "acc_distributions_ecdf" ] }, { "page": "acc_distributions_loc", "title": "Plots and checks for distributions - Location", "topics": [ "acc_distributions_loc" ] }, { "page": "acc_distributions_only", "title": "Plots and checks for distributions - only", "topics": [ "acc_distributions_only" ] }, { "page": "acc_distributions_prop", "title": "Plots and checks for distributions - Proportion", "topics": [ "acc_distributions_prop" ] }, { "page": "acc_end_digits", "title": "Extension of acc_shape_or_scale to examine uniform distributions of end digits", "topics": [ "acc_end_digits" ] }, { "page": "acc_loess", "title": "Smoothes and plots adjusted longitudinal measurements and longitudinal trends from logistic regression models", "topics": [ "acc_loess" ] }, { "page": "acc_mahalanobis", "title": "Calculate and plot 'Mahalanobis' distances", "topics": [ "acc_mahalanobis" ] }, { "page": "acc_mahalanobis_ratio", "title": "Internal function only existing for technical reasons, planned to be removed in future releases", "topics": [ "acc_mahalanobis_ratio" ] }, { "page": "acc_margins", "title": "Estimate marginal means, see emmeans::emmeans", "topics": [ "acc_margins" ] }, { "page": "acc_multivariate_outlier", "title": "Calculate and plot Mahalanobis distances", "topics": [ "acc_multivariate_outlier" ] }, { "page": "acc_robust_univariate_outlier", "title": "Identify univariate outliers by four different approaches", "topics": [ "acc_robust_univariate_outlier" ] }, { "page": "acc_shape_or_scale", "title": "Compare observed versus expected distributions", "topics": [ "acc_shape_or_scale" ] }, { "page": "acc_univariate_outlier", "title": "Identify univariate outliers by four different approaches", "topics": [ "acc_univariate_outlier" ] }, { "page": "acc_varcomp", "title": "Utility function to compute model-based ICC depending on the (statistical) data type", "topics": [ "acc_varcomp" ] }, { "page": "as.character.dataquieR_translated", "title": "'as.character' implementation for the class 'dataquieR_translated'", "topics": [ "as.character.dataquieR_translated" ] }, { "page": "as.character.interval", "title": "'as.character' implementation for the class 'interval'", "topics": [ "as.character.interval" ] }, { "page": "as.data.frame.dataquieR_resultset", "title": "Convert a full 'dataquieR' report to a 'data.frame'", "topics": [ "as.data.frame.dataquieR_resultset" ] }, { "page": "as.list.dataquieR_resultset", "title": "Convert a full 'dataquieR' report to a 'list'", "topics": [ "as.list.dataquieR_resultset" ] }, { "page": "as.list.dataquieR_resultset2", "title": "inefficient way to convert a report to a list. try 'prep_set_backend()'", "topics": [ "as.list.dataquieR_resultset2" ] }, { "page": "ASSOCIATION_DIRECTION", "title": "Cross-item level metadata attribute name", "concept": [ "meta_data_cross" ], "topics": [ "ASSOCIATION_DIRECTION" ] }, { "page": "ASSOCIATION_FORM", "title": "Cross-item level metadata attribute name", "concept": [ "meta_data_cross" ], "topics": [ "ASSOCIATION_FORM" ] }, { "page": "ASSOCIATION_METRIC", "title": "Cross-item level metadata attribute name", "concept": [ "meta_data_cross" ], "topics": [ "ASSOCIATION_METRIC" ] }, { "page": "ASSOCIATION_RANGE", "title": "Cross-item level metadata attribute name", "concept": [ "meta_data_cross" ], "topics": [ "ASSOCIATION_RANGE" ] }, { "page": "CHECK_ID", "title": "Cross-item level metadata attribute name", "concept": [ "meta_data_cross" ], "topics": [ "CHECK_ID" ] }, { "page": "CHECK_LABEL", "title": "Cross-item level metadata attribute name", "concept": [ "meta_data_cross" ], "topics": [ "CHECK_LABEL" ] }, { "page": "check_table", "title": "Data frame with contradiction rules", "topics": [ "check_table" ] }, { "page": "CODE_CLASSES", "title": "types of value codes", "topics": [ "CODE_CLASSES" ] }, { "page": "CODE_LIST_TABLE", "title": "Default Name of the Table featuring Code Lists", "topics": [ "CODE_LIST_TABLE" ] }, { "page": "CODE_ORDER", "title": "Only existence is checked, order not yet used", "topics": [ "CODE_ORDER" ] }, { "page": "com_item_missingness", "title": "Summarize missingness columnwise (in variable)", "topics": [ "com_item_missingness" ] }, { "page": "com_qualified_item_missingness", "title": "Compute Indicators for Qualified Item Missingness", "topics": [ "com_qualified_item_missingness" ] }, { "page": "com_qualified_segment_missingness", "title": "Compute Indicators for Qualified Segment Missingness", "topics": [ "com_qualified_segment_missingness" ] }, { "page": "com_segment_missingness", "title": "Summarizes missingness for individuals in specific segments", "topics": [ "com_segment_missingness" ] }, { "page": "com_unit_missingness", "title": "Counts all individuals with no measurements at all", "topics": [ "com_unit_missingness" ] }, { "page": "COMPUTATION_RULE", "title": "Cross-item level metadata attribute name", "concept": [ "meta_data_computation" ], "topics": [ "COMPUTATION_RULE" ] }, { "page": "COMPUTED_VARIABLE_ROLES", "title": "'SSI' related Cross-item level metadata attribute names Computed Variable roles can be one of the following:", "concept": [ "SSI", "meta_data_cross" ], "topics": [ "COMPUTED_VARIABLE_ROLES" ] }, { "page": "con_contradictions", "title": "Checks user-defined contradictions in study data", "topics": [ "con_contradictions" ] }, { "page": "con_contradictions_redcap", "title": "Checks user-defined contradictions in study data", "topics": [ "con_contradictions_redcap" ] }, { "page": "con_inadmissible_categorical", "title": "Detects variable levels not specified in metadata", "topics": [ "con_inadmissible_categorical" ] }, { "page": "con_inadmissible_vocabulary", "title": "Detects variable levels not specified in standardized vocabulary", "topics": [ "con_inadmissible_vocabulary" ] }, { "page": "con_limit_deviations", "title": "Detects variable values exceeding limits defined in metadata", "topics": [ "con_limit_deviations" ] }, { "page": "contradiction_functions_descriptions", "title": "description of the contradiction functions", "topics": [ "contradiction_functions_descriptions" ] }, { "page": "CONTRADICTION_TERM", "title": "Cross-item level metadata attribute name", "concept": [ "meta_data_cross" ], "topics": [ "CONTRADICTION_TERM", "RULE" ] }, { "page": "CONTRADICTION_TYPE", "title": "Cross-item level metadata attribute name", "concept": [ "meta_data_cross" ], "topics": [ "CONTRADICTION_TYPE" ] }, { "page": "DATA_PREPARATION", "title": "Cross-item level metadata attribute name", "concept": [ "meta_data_cross" ], "topics": [ "DATA_PREPARATION" ] }, { "page": "DATA_TYPES", "title": "Data Types", "topics": [ "DATA_TYPES", "DATETIME", "datetime", "enum", "FLOAT", "float", "INTEGER", "integer", "numeric", "set", "STRING", "string", "TIME", "time", "variable", "variable list" ] }, { "page": "DATA_TYPES_OF_R_TYPE", "title": "All available data types, mapped from their respective R types", "topics": [ "DATA_TYPES_OF_R_TYPE" ] }, { "page": "dataquieR_resultset", "title": "Internal constructor for the internal class dataquieR_resultset.", "topics": [ "dataquieR_resultset" ] }, { "page": "dataquieR_resultset_verify", "title": "Verify an object of class dataquieR_resultset", "topics": [ "dataquieR_resultset_verify" ] }, { "page": "dataquieR_resultset2-class", "title": "Class dataquieR_resultset2.", "topics": [ ".dataquieR_resultset2", "dataquieR_resultset2", "dataquieR_resultset2-class" ] }, { "page": "dataquieR.acc_loess.exclude_constant_subgroups", "title": "Exclude subgroups with constant values from LOESS figure", "concept": [ "options" ], "topics": [ "dataquieR.acc_loess.exclude_constant_subgroups" ] }, { "page": "dataquieR.acc_loess.mark_time_points", "title": "Display time-points in LOESS plots", "concept": [ "options" ], "topics": [ "dataquieR.acc_loess.mark_time_points" ] }, { "page": "dataquieR.acc_loess.min_bw", "title": "Lower limit for the LOESS bandwidth", "concept": [ "options" ], "topics": [ "dataquieR.acc_loess.min_bw" ] }, { "page": "dataquieR.acc_loess.min_obs_in_subgroup", "title": "Minimum observations for a level to be included by 'acc_loess()'", "concept": [ "options" ], "topics": [ "dataquieR.acc_loess.min_obs_in_subgroup" ] }, { "page": "dataquieR.acc_loess.min_proportion", "title": "Lower limit for the proportion of cases or controls to create a smoothed time trend figure", "concept": [ "options" ], "topics": [ "dataquieR.acc_loess.min_proportion" ] }, { "page": "dataquieR.acc_loess.plot_format", "title": "default for Plot-Format in 'acc_loess()'", "concept": [ "options" ], "topics": [ "dataquieR.acc_loess.plot_format" ] }, { "page": "dataquieR.acc_loess.plot_observations", "title": "Display observations in LOESS plots", "concept": [ "options" ], "topics": [ "dataquieR.acc_loess.plot_observations" ] }, { "page": "dataquieR.acc_margins_num", "title": "Include number of observations for each level of the grouping variable in the 'margins' figure", "concept": [ "options" ], "topics": [ "dataquieR.acc_margins_num" ] }, { "page": "dataquieR.acc_margins_sort", "title": "Sort levels of the grouping variable in the 'margins' figures", "concept": [ "options" ], "topics": [ "dataquieR.acc_margins_sort" ] }, { "page": "dataquieR.acc_multivariate_outlier.scale", "title": "Apply min-max scaling in parallel coordinates figure to inspect multivariate outliers", "concept": [ "options" ], "topics": [ "dataquieR.acc_multivariate_outlier.scale" ] }, { "page": "dataquieR.acc_shape_or_scale_ci", "title": "Method for confidence intervals in 'acc_shape_or_scale()' and 'acc_end_digits()'", "concept": [ "options" ], "topics": [ "dataquieR.acc_shape_or_scale_ci" ] }, { "page": "dataquieR.applicability_problem", "title": "An exception class assigned for exceptions caused by trying to apply a non-applicable indicator function", "topics": [ "dataquieR.applicability_problem" ] }, { "page": "dataquieR.col_con_con_empirical", "title": "Color for empirical contradictions", "concept": [ "options" ], "topics": [ "dataquieR.col_con_con_empirical" ] }, { "page": "dataquieR.col_con_con_logical", "title": "Color for logical contradictions", "concept": [ "options" ], "topics": [ "dataquieR.col_con_con_logical" ] }, { "page": "dataquieR.CONDITIONS_LEVEL_TRHESHOLD", "title": "Log Level", "concept": [ "options" ], "topics": [ "dataquieR.CONDITIONS_LEVEL_TRHESHOLD" ] }, { "page": "dataquieR.CONDITIONS_WITH_STACKTRACE", "title": "Add stack-trace in condition messages (to be deprecated)", "concept": [ "options" ], "topics": [ "dataquieR.CONDITIONS_WITH_STACKTRACE" ] }, { "page": "dataquieR.convert_to_list_for_lapply", "title": "If report uses a 'storr' back-end, do not convert to base-list", "concept": [ "options" ], "topics": [ "dataquieR.convert_to_list_for_lapply" ] }, { "page": "dataquieR.debug", "title": "Call 'browser()' on errors", "concept": [ "options" ], "topics": [ "dataquieR.debug" ] }, { "page": "dataquieR.des_summary_hard_lim_remove", "title": "Removal of hard limits from data before calculating descriptive statistics.", "concept": [ "options" ], "topics": [ "dataquieR.des_summary_hard_lim_remove" ] }, { "page": "dataquieR.dontwrapresults", "title": "Disable automatic post-processing of 'dataquieR' function results", "concept": [ "options" ], "topics": [ "dataquieR.dontwrapresults" ] }, { "page": "dataquieR.droplevels_ReportSummaryTable", "title": "Show also unused levels in heatmaps", "concept": [ "options" ], "topics": [ "dataquieR.droplevels_ReportSummaryTable" ] }, { "page": "dataquieR.dt_adjust", "title": "character Adjust data types according to metadata", "concept": [ "options" ], "topics": [ "dataquieR.dt_adjust" ] }, { "page": "dataquieR.ELEMENT_MISSMATCH_CHECKTYPE", "title": "Metadata describes more than the current study data", "concept": [ "options" ], "topics": [ "dataquieR.ELEMENT_MISSMATCH_CHECKTYPE" ] }, { "page": "dataquieR.ERRORS_WITH_CALLER", "title": "Set caller for error conditions (to be deprecated)", "concept": [ "options" ], "topics": [ "dataquieR.ERRORS_WITH_CALLER" ] }, { "page": "dataquieR.fix_column_type_on_read", "title": "Try to avoid fallback to string columns when reading files", "concept": [ "options" ], "topics": [ "dataquieR.fix_column_type_on_read" ] }, { "page": "dataquieR.flip_mode", "title": "Flip-Mode to Use for figures", "concept": [ "options" ], "topics": [ "dataquieR.flip_mode" ] }, { "page": "dataquieR.force_item_specific_missing_codes", "title": "Converting MISSING_LIST/JUMP_LIST to a MISSING_LIST_TABLE create on list per item", "concept": [ "options" ], "topics": [ "dataquieR.force_item_specific_missing_codes" ] }, { "page": "dataquieR.force_label_col", "title": "Control, how the 'label_col' argument is used.", "concept": [ "options" ], "topics": [ "dataquieR.force_label_col" ] }, { "page": "dataquieR.GAM_for_LOESS", "title": "Enable to switch to a general additive model instead of LOESS", "concept": [ "options" ], "topics": [ "dataquieR.GAM_for_LOESS" ] }, { "page": "dataquieR.grading_formats", "title": "Name of the data.frame featuring a format for grading-values", "concept": [ "options" ], "topics": [ "dataquieR.grading_formats" ] }, { "page": "dataquieR.grading_rulesets", "title": "Name of the data.frame featuring GRADING_RULESET", "concept": [ "options" ], "topics": [ "dataquieR.grading_rulesets" ] }, { "page": "dataquieR.guess_character", "title": "For metadata guessing, try to guess DATA_TYPE from the data values", "concept": [ "options" ], "topics": [ "dataquieR.guess_character" ] }, { "page": "dataquieR.guess_missing_codes", "title": "Control, if 'dataquieR' tries to guess missing-codes from the study data in absence of metadata", "concept": [ "options" ], "topics": [ "dataquieR.guess_missing_codes" ] }, { "page": "dataquieR.ignore_empty_vars", "title": "character remove variables with only empty values", "concept": [ "options", "study_data_cache" ], "topics": [ "dataquieR.ignore_empty_vars" ] }, { "page": "dataquieR.intrinsic_applicability_problem", "title": "An exception class assigned for exceptions caused by trying to apply a non-applicable indicator function, which is not caused by deficient metadata", "topics": [ "dataquieR.intrinsic_applicability_problem" ] }, { "page": "dataquieR.lang", "title": "Language-Suffix for metadata Label-Columns", "concept": [ "options" ], "topics": [ "dataquieR.lang" ] }, { "page": "dataquieR.lazy_plots", "title": "character plots realized lazy", "concept": [ "options" ], "topics": [ "dataquieR.lazy_plots" ] }, { "page": "dataquieR.lazy_plots_cache", "title": "character cache realizations", "concept": [ "options" ], "topics": [ "dataquieR.lazy_plots_cache" ] }, { "page": "dataquieR.lazy_plots_gg_compatibility", "title": "character be as compatible with 'ggplot2' objects as possible", "concept": [ "options" ], "topics": [ "dataquieR.lazy_plots_gg_compatibility" ] }, { "page": "dataquieR.locale", "title": "character default language for type conversion", "concept": [ "options" ], "topics": [ "dataquieR.locale" ] }, { "page": "dataquieR.MAHALANOBIS_THRESHOLD", "title": "Default availability of Mahalanobis based multivariate outlier checks in reports", "concept": [ "options" ], "topics": [ "dataquieR.MAHALANOBIS_THRESHOLD" ] }, { "page": "dataquieR.max_cat_resp_var_levels_in_plot", "title": "Maximum number of levels of the categorical response variable shown individually in figures", "concept": [ "options" ], "topics": [ "dataquieR.max_cat_resp_var_levels_in_plot" ] }, { "page": "dataquieR.max_group_var_levels_in_plot", "title": "Maximum number of levels of the grouping variable shown individually in figures", "concept": [ "options" ], "topics": [ "dataquieR.max_group_var_levels_in_plot" ] }, { "page": "dataquieR.max_group_var_levels_with_violins", "title": "Maximum number of levels of the grouping variable shown with individual histograms ('violins') in 'margins' figures", "concept": [ "options" ], "topics": [ "dataquieR.max_group_var_levels_with_violins" ] }, { "page": "dataquieR.MAX_LABEL_LEN", "title": "Maximum length for variable labels LABEL", "concept": [ "options" ], "topics": [ "dataquieR.MAX_LABEL_LEN" ] }, { "page": "dataquieR.MAX_LONG_LABEL_LEN", "title": "Maximum length for long variable labels LONG_LABEL", "concept": [ "options" ], "topics": [ "dataquieR.MAX_LONG_LABEL_LEN" ] }, { "page": "dataquieR.MAX_VALUE_LABEL_LEN", "title": "Maximum length for value labels", "concept": [ "options" ], "topics": [ "dataquieR.MAX_VALUE_LABEL_LEN" ] }, { "page": "dataquieR.MESSAGES_WITH_CALLER", "title": "Set caller for message conditions (to be deprecated)", "concept": [ "options" ], "topics": [ "dataquieR.MESSAGES_WITH_CALLER" ] }, { "page": "dataquieR.min_obs_per_group_var_in_plot", "title": "Minimum number of observations per grouping variable that is required to include an individual level of the grouping variable in a figure", "concept": [ "options" ], "topics": [ "dataquieR.min_obs_per_group_var_in_plot" ] }, { "page": "dataquieR.min_time_points_for_cat_resp_var", "title": "Minimum number of data points to create a time course plot for an individual level of a categorical response variable", "concept": [ "options" ], "topics": [ "dataquieR.min_time_points_for_cat_resp_var" ] }, { "page": "dataquieR.MULTIVARIATE_OUTLIER_CHECK", "title": "Default availability of multivariate outlier checks in reports", "concept": [ "options" ], "topics": [ "dataquieR.MULTIVARIATE_OUTLIER_CHECK" ] }, { "page": "dataquieR.no_geom_count_in_bin", "title": "Suppress counts 'margins' figures for binary outcomes", "concept": [ "options" ], "topics": [ "dataquieR.no_geom_count_in_bin" ] }, { "page": "dataquieR.no_overall_in_bin", "title": "Suppress overall distribution in 'margins' figures for binary outcomes", "concept": [ "options" ], "topics": [ "dataquieR.no_overall_in_bin" ] }, { "page": "dataquieR.non_disclosure", "title": "Remove all observation-level-real-data from reports", "concept": [ "options" ], "topics": [ "dataquieR.non_disclosure" ] }, { "page": "dataquieR.old_factor_handling", "title": "character use the old handling of study data already featuring factors", "concept": [ "options" ], "topics": [ "dataquieR.old_factor_handling" ] }, { "page": "dataquieR.old_type_adjust", "title": "character use the old type conversion code (slower)", "concept": [ "options" ], "topics": [ "dataquieR.old_type_adjust" ] }, { "page": "dataquieR.precomputeStudyData", "title": "Pre-compute different curation levels of study data", "concept": [ "options", "study_data_cache" ], "topics": [ "dataquieR.precomputeStudyData" ] }, { "page": "dataquieR.print_block_load_factor", "title": "numeric", "concept": [ "options", "study_data_cache" ], "topics": [ "dataquieR.print_block_load_factor" ] }, { "page": "dataquieR.progress_fkt_default", "title": "function to call on progress increase", "concept": [ "options" ], "topics": [ "dataquieR.progress_fkt_default" ] }, { "page": "dataquieR.progress_msg_fkt_default", "title": "function to call on progress message update", "concept": [ "options" ], "topics": [ "dataquieR.progress_msg_fkt_default" ] }, { "page": "dataquieR.resume_checkpoint", "title": "If result already exists in a 'storr' back-end, re-use it", "concept": [ "options" ], "topics": [ "dataquieR.resume_checkpoint" ] }, { "page": "dataquieR.resume_print", "title": "If output folder is not empty, try to resume stopped 'print()'", "concept": [ "options" ], "topics": [ "dataquieR.resume_print" ] }, { "page": "dataquieR.scale_level_heuristics_control_binaryrecodelimit", "title": "Number of levels to consider a variable ordinal in absence of SCALE_LEVEL", "concept": [ "options" ], "topics": [ "dataquieR.scale_level_heuristics_control_binaryrecodelimit" ] }, { "page": "dataquieR.scale_level_heuristics_control_metriclevels", "title": "Number of levels to consider a variable metric in absence of SCALE_LEVEL", "concept": [ "options" ], "topics": [ "dataquieR.scale_level_heuristics_control_metriclevels" ] }, { "page": "dataquieR.study_data_cache_max", "title": "Maximum size of cache for curated study data", "concept": [ "options", "study_data_cache" ], "topics": [ "dataquieR.study_data_cache_max" ] }, { "page": "dataquieR.study_data_cache_metrics", "title": "Collect metrics on cache usage of study data cache", "concept": [ "options", "study_data_cache" ], "topics": [ "dataquieR.study_data_cache_metrics" ] }, { "page": "dataquieR.study_data_cache_metrics_env", "title": "environment for storing metrics on the study data cache", "concept": [ "options", "study_data_cache" ], "topics": [ "dataquieR.study_data_cache_metrics_env" ] }, { "page": "dataquieR.study_data_cache_metrics_env_default", "title": "Default space for some metrics during report computation", "concept": [ "study_data_cache" ], "topics": [ "dataquieR.study_data_cache_metrics_env_default" ] }, { "page": "dataquieR.study_data_cache_quick_fill", "title": "Control the pre-computation of curation levels of study data", "concept": [ "options", "study_data_cache" ], "topics": [ "dataquieR.study_data_cache_quick_fill" ] }, { "page": "dataquieR.study_data_colnames_case_sensitive", "title": "character Are column names in study data considered case-sensitive for mapping", "concept": [ "options" ], "topics": [ "dataquieR.study_data_colnames_case_sensitive" ] }, { "page": "dataquieR.testdebug", "title": "Disable all interactively used metadata-based function argument provision", "concept": [ "options" ], "topics": [ "dataquieR.testdebug" ] }, { "page": "dataquieR.traceback", "title": "Include full trace-back in captured conditions", "concept": [ "options" ], "topics": [ "dataquieR.traceback" ] }, { "page": "dataquieR.type_adjust_parallel", "title": "character try to do type adjustments in parallel only, if 'dq_report2()' was called with 'cores = 2' or higher.", "concept": [ "options" ], "topics": [ "dataquieR.type_adjust_parallel" ] }, { "page": "dataquieR.VALUE_LABELS_htmlescaped", "title": "Assume, all VALUE_LABELS are HTML escaped", "concept": [ "options" ], "topics": [ "dataquieR.VALUE_LABELS_htmlescaped" ] }, { "page": "dataquieR.WARNINGS_WITH_CALLER", "title": "Set caller for warning conditions (to be deprecated)", "concept": [ "options" ], "topics": [ "dataquieR.WARNINGS_WITH_CALLER" ] }, { "page": "des_scatterplot_matrix", "title": "Compute Pairwise Correlations", "topics": [ "des_scatterplot_matrix" ] }, { "page": "des_summary", "title": "Compute Descriptive Statistics", "topics": [ "des_summary" ] }, { "page": "des_summary_categorical", "title": "Compute Descriptive Statistics - categorical variables", "topics": [ "des_summary_categorical" ] }, { "page": "des_summary_continuous", "title": "Compute Descriptive Statistics - continuous variables", "topics": [ "des_summary_continuous" ] }, { "page": "DF_CODE", "title": "Data frame level metadata attribute name", "topics": [ "DF_CODE" ] }, { "page": "DF_ELEMENT_COUNT", "title": "Data frame level metadata attribute name", "topics": [ "DF_ELEMENT_COUNT" ] }, { "page": "DF_ID_REF_TABLE", "title": "Data frame level metadata attribute name", "topics": [ "DF_ID_REF_TABLE" ] }, { "page": "DF_ID_VARS", "title": "Data frame level metadata attribute name", "topics": [ "DF_ID_VARS" ] }, { "page": "DF_NAME", "title": "Data frame level metadata attribute name", "topics": [ "DF_NAME" ] }, { "page": "DF_RECORD_CHECK", "title": "Data frame level metadata attribute name", "topics": [ "DF_RECORD_CHECK" ] }, { "page": "DF_RECORD_COUNT", "title": "Data frame level metadata attribute name", "topics": [ "DF_RECORD_COUNT" ] }, { "page": "DF_UNIQUE_ID", "title": "Data frame level metadata attribute name", "topics": [ "DF_UNIQUE_ID" ] }, { "page": "DF_UNIQUE_ROWS", "title": "Data frame level metadata attribute name", "topics": [ "DF_UNIQUE_ROWS" ] }, { "page": "dim.dataquieR_resultset2", "title": "Get the dimensions of a 'dq_report2' result", "topics": [ "dim.dataquieR_resultset2" ] }, { "page": "dimensions", "title": "Names of DQ dimensions", "topics": [ "dimensions" ] }, { "page": "dimnames.dataquieR_resultset2", "title": "Names of a 'dataquieR' report object (v2.0)", "topics": [ "dimnames.dataquieR_resultset2" ] }, { "page": "dims", "title": "Dimension Titles for Prefixes", "topics": [ "dims" ] }, { "page": "DISTRIBUTIONS", "title": "All available probability distributions for acc_shape_or_scale", "topics": [ "DISTRIBUTIONS" ] }, { "page": "dq_report", "title": "Generate a full DQ report", "topics": [ "dq_report" ] }, { "page": "dq_report_by", "title": "Generate a stratified full DQ report", "topics": [ "dq_report_by" ] }, { "page": "dq_report2", "title": "Generate a full DQ report, v2", "topics": [ "dq_report2" ] }, { "page": "droplevels.ReportSummaryTable", "title": "Remove unused levels from 'ReportSummaryTable'", "topics": [ "droplevels.ReportSummaryTable" ] }, { "page": "dq_lazy_ggplot_methods", "title": "S3/S7 methods for lazy ggplot objects", "topics": [ "dq_lazy_ggplot_methods", "ggplotGrob.dq_lazy_ggplot", "ggplotGrob.dq_lazy_ggplot_s7", "ggplotly.dq_lazy_ggplot", "ggplotly.dq_lazy_ggplot_s7", "plotly_build.dq_lazy_ggplot", "plotly_build.dq_lazy_ggplot_s7" ] }, { "page": "GOLDSTANDARD", "title": "Cross-item level metadata attribute name", "concept": [ "meta_data_cross" ], "topics": [ "GOLDSTANDARD" ] }, { "page": "grid.draw.util_pairs_ggplot_panels", "title": "'grid.draw' method for 'util_pairs_ggplot_panels' objects", "topics": [ "grid.draw.util_pairs_ggplot_panels" ] }, { "page": "html_dependency_clipboard", "title": "HTML Dependency for report headers in 'clipboard'", "topics": [ "html_dependency_clipboard" ] }, { "page": "html_dependency_dataquieR", "title": "HTML Dependency for 'dataquieR'", "topics": [ "html_dependency_dataquieR" ] }, { "page": "html_dependency_jspdf", "title": "HTML dependency for 'jsPDF'", "topics": [ "html_dependency_jspdf" ] }, { "page": "html_dependency_report_dt", "title": "HTML Dependency for report headers in 'DT::datatable'", "topics": [ "html_dependency_report_dt" ] }, { "page": "html_dependency_tippy", "title": "HTML Dependency for 'tippy'", "topics": [ "html_dependency_tippy" ] }, { "page": "html_dependency_vert_dt", "title": "HTML Dependency for vertical headers in 'DT::datatable'", "topics": [ "html_dependency_vert_dt" ] }, { "page": "int_all_datastructure_dataframe", "title": "Wrapper function to check for studies data structure", "topics": [ "int_all_datastructure_dataframe" ] }, { "page": "int_all_datastructure_segment", "title": "Wrapper function to check for segment data structure", "topics": [ "int_all_datastructure_segment" ] }, { "page": "int_datatype_matrix", "title": "Check declared data types of metadata in study data", "topics": [ "int_datatype_matrix" ] }, { "page": "int_duplicate_content", "title": "Check for duplicated content", "topics": [ "int_duplicate_content" ] }, { "page": "int_duplicate_ids", "title": "Check for duplicated IDs", "topics": [ "int_duplicate_ids" ] }, { "page": "int_encoding_errors", "title": "Encoding Errors", "topics": [ "int_encoding_errors" ] }, { "page": "int_part_vars_structure", "title": "Detect Expected Observations", "topics": [ "int_part_vars_structure" ] }, { "page": "int_sts_element_dataframe", "title": "Determine missing and/or superfluous data elements", "topics": [ "int_sts_element_dataframe" ] }, { "page": "int_sts_element_segment", "title": "Checks for element set", "topics": [ "int_sts_element_segment" ] }, { "page": "int_unexp_elements", "title": "Check for unexpected data element count", "topics": [ "int_unexp_elements" ] }, { "page": "int_unexp_records_dataframe", "title": "Check for unexpected data record count at the data frame level", "topics": [ "int_unexp_records_dataframe" ] }, { "page": "int_unexp_records_segment", "title": "Check for unexpected data record count within segments", "topics": [ "int_unexp_records_segment" ] }, { "page": "int_unexp_records_set", "title": "Check for unexpected data record set", "topics": [ "int_unexp_records_set" ] }, { "page": "IRV", "title": "Cross-item level metadata attribute name", "concept": [ "SSI", "meta_data_cross" ], "topics": [ "IRV" ] }, { "page": "MAHALANOBIS_RATIO", "title": "Cross-item level metadata attribute name", "concept": [ "SSI", "meta_data_cross" ], "topics": [ "MAHALANOBIS_RATIO" ] }, { "page": "MAHALANOBIS_THRESHOLD", "title": "Cross-item level metadata attribute name", "concept": [ "meta_data_cross" ], "topics": [ "MAHALANOBIS_THRESHOLD" ] }, { "page": "MAXIMUM_LONG_STRING", "title": "Cross-item level metadata attribute name", "concept": [ "SSI", "meta_data_cross" ], "topics": [ "MAXIMUM_LONG_STRING" ] }, { "page": "meta_data", "title": "Data frame with metadata about the study data on variable level", "topics": [ "meta_data" ] }, { "page": "meta_data_computation", "title": "Well known columns on the 'item_computation_level' sheet", "concept": [ "meta_data_cross" ], "topics": [ "meta_data_computation" ] }, { "page": "meta_data_cross", "title": "Well known columns on the 'cross-item_level' sheet", "concept": [ "meta_data_cross" ], "topics": [ "meta_data_cross" ] }, { "page": "meta_data_dataframe", "title": "Well known columns on the 'meta_data_dataframe' sheet", "topics": [ "meta_data_dataframe" ] }, { "page": "meta_data_segment", "title": "Well known columns on the 'meta_data_segment' sheet", "topics": [ "meta_data_segment" ] }, { "page": "MISS_RESP", "title": "Cross-item level metadata attribute name", "concept": [ "SSI", "meta_data_cross" ], "topics": [ "MISS_RESP" ] }, { "page": "MISSING_CODE_RULES", "title": "Name of the sheet with rules to introduce missing codes in the pipeline", "topics": [ "MISSING_CODE_RULES" ] }, { "page": "MULTIVARIATE_OUTLIER_CHECK", "title": "Cross-item level metadata attribute name", "concept": [ "meta_data_cross" ], "topics": [ "MULTIVARIATE_OUTLIER_CHECK" ] }, { "page": "MULTIVARIATE_OUTLIER_CHECKTYPE", "title": "Cross-item level metadata attribute name", "concept": [ "meta_data_cross" ], "topics": [ "MULTIVARIATE_OUTLIER_CHECKTYPE" ] }, { "page": "names-set-.dataquieR_translated", "title": "'names' implementation for the class 'dataquieR_translated'", "topics": [ "names<-.dataquieR_translated" ] }, { "page": "nres", "title": "return the number of result slots in a report", "topics": [ "nres" ] }, { "page": "pipeline_recursive_result", "title": "Convert a pipeline result data frame to named encapsulated lists", "topics": [ "pipeline_recursive_result" ] }, { "page": "pipeline_vectorized", "title": "Call (nearly) one \"Accuracy\" function with many parameterizations at once automatically", "topics": [ "pipeline_vectorized" ] }, { "page": "plot.dataquieR_summary", "title": "Plot a 'dataquieR' summary", "topics": [ "plot.dataquieR_summary" ] }, { "page": "prep_acc_distributions_with_ecdf", "title": "Utility function to plot a combined figure for distribution checks", "topics": [ "prep_acc_distributions_with_ecdf" ] }, { "page": "prep_add_cause_label_df", "title": "Convert missing codes in metadata format v1.0 and a missing-cause-table to v2.0 missing list / jump list assignments", "topics": [ "prep_add_cause_label_df" ] }, { "page": "prep_add_computed_variables", "title": "Insert missing codes for 'NA's based on rules", "topics": [ "prep_add_computed_variables" ] }, { "page": "prep_add_data_frames", "title": "Add data frames to the pre-loaded / cache data frame environment", "concept": [ "data-frame-cache" ], "topics": [ "prep_add_data_frames" ] }, { "page": "prep_add_missing_codes", "title": "Insert missing codes for 'NA's based on rules", "topics": [ "prep_add_missing_codes" ] }, { "page": "prep_add_to_meta", "title": "Support function to augment metadata during data quality reporting", "topics": [ "prep_add_to_meta" ] }, { "page": "prep_apply_coding", "title": "Re-Code labels with their respective codes according to the 'meta_data'", "topics": [ "prep_apply_coding" ] }, { "page": "prep_check_for_dataquieR_updates", "title": "Check for package updates", "topics": [ "prep_check_for_dataquieR_updates" ] }, { "page": "prep_check_meta_data_dataframe", "title": "Verify and normalize metadata on data frame level", "topics": [ "prep_check_meta_data_dataframe" ] }, { "page": "prep_check_meta_data_segment", "title": "Verify and normalize metadata on segment level", "topics": [ "prep_check_meta_data_segment" ] }, { "page": "prep_check_meta_names", "title": "Checks the validity of metadata w.r.t. the provided column names", "topics": [ "prep_check_meta_names" ] }, { "page": "prep_clean_labels", "title": "Support function to scan variable labels for applicability", "topics": [ "prep_clean_labels" ] }, { "page": "prep_combine_report_summaries", "title": "Combine two report summaries", "concept": [ "summary_functions" ], "topics": [ "prep_combine_report_summaries" ] }, { "page": "prep_compare_meta_with_study", "title": "Verify item-level metadata", "topics": [ "prep_compare_meta_with_study" ] }, { "page": "prep_create_meta", "title": "Support function to create data.frames of metadata", "topics": [ "prep_create_meta" ] }, { "page": "prep_create_meta_data_file", "title": "Instantiate a new metadata file", "topics": [ "prep_create_meta_data_file" ] }, { "page": "prep_create_storr_factory", "title": "Create a factory function for 'storr' objects for backing a dataquieR_resultset2", "topics": [ "prep_create_storr_factory" ] }, { "page": "prep_datatype_from_data", "title": "Get data types from data", "topics": [ "prep_datatype_from_data" ] }, { "page": "prep_deparse_assignments", "title": "Convert two vectors from a code-value-table to a key-value list", "topics": [ "prep_deparse_assignments" ] }, { "page": "prep_deregister_progress_hook", "title": "De-register a hook function for progresses in computation/rendering", "topics": [ "prep_deregister_progress_hook" ] }, { "page": "prep_dq_data_type_of", "title": "Get the dataquieR 'DATA_TYPE' of 'x'", "topics": [ "prep_dq_data_type_of" ] }, { "page": "prep_expand_codes", "title": "Expand code labels across variables", "topics": [ "prep_expand_codes" ] }, { "page": "prep_extract_cause_label_df", "title": "Extract all missing/jump codes from metadata and export a cause-label-data-frame", "topics": [ "prep_extract_cause_label_df" ] }, { "page": "prep_extract_classes_by_functions", "title": "Extract old function based summary from data quality results", "concept": [ "summary_functions" ], "topics": [ "prep_extract_classes_by_functions" ] }, { "page": "prep_extract_summary", "title": "Extract summary from data quality results", "concept": [ "summary_functions" ], "topics": [ "prep_extract_summary" ] }, { "page": "prep_extract_summary.dataquieR_result", "title": "Extract report summary from reports", "concept": [ "summary_functions" ], "topics": [ "prep_extract_summary.dataquieR_result" ] }, { "page": "prep_extract_summary.dataquieR_resultset2", "title": "Extract report summary from reports", "concept": [ "summary_functions" ], "topics": [ "prep_extract_summary.dataquieR_resultset2" ] }, { "page": "prep_fix_meta_id_dups", "title": "Fix metadata duplicates", "topics": [ "prep_fix_meta_id_dups" ] }, { "page": "prep_get_data_frame", "title": "Read data from files/URLs", "concept": [ "data-frame-cache" ], "topics": [ "prep_get_data_frame" ] }, { "page": "prep_get_labels", "title": "Fetch a label for a variable based on its purpose", "topics": [ "prep_get_labels" ] }, { "page": "prep_get_study_data_segment", "title": "Get data frame for a given segment", "topics": [ "prep_get_study_data_segment" ] }, { "page": "prep_get_user_name", "title": "Return the logged-in User's Full Name", "topics": [ "prep_get_user_name" ] }, { "page": "prep_guess_encoding", "title": "Guess encoding of text or text files", "topics": [ "prep_guess_encoding" ] }, { "page": "prep_is_translated", "title": "Detect if an object is a 'dataquieR_translated' object", "topics": [ "prep_is_translated" ] }, { "page": "prep_link_escape", "title": "Prepare a label as part of a link for 'RMD' files", "topics": [ "prep_link_escape" ] }, { "page": "prep_list_dataframes", "title": "List Loaded Data Frames", "concept": [ "data-frame-cache" ], "topics": [ "prep_list_dataframes" ] }, { "page": "prep_list_voc", "title": "All valid voc: vocabularies", "topics": [ "prep_list_voc" ] }, { "page": "prep_load_folder_with_metadata", "title": "Pre-load a folder with named (usually more than) one table(s)", "concept": [ "data-frame-cache" ], "topics": [ "prep_load_folder_with_metadata" ] }, { "page": "prep_load_report", "title": "Load a 'dq_report2'", "topics": [ "prep_load_report" ] }, { "page": "prep_load_report_from_backend", "title": "Load a report from a back-end", "topics": [ "prep_load_report_from_backend" ] }, { "page": "prep_load_workbook_like_file", "title": "Pre-load a file with named (usually more than) one table(s)", "concept": [ "data-frame-cache" ], "topics": [ "prep_load_workbook_like_file" ] }, { "page": "prep_map_labels", "title": "Support function to allocate labels to variables", "topics": [ "prep_map_labels" ] }, { "page": "prep_merge_study_data", "title": "Merge a list of study data frames to one (sparse) study data frame", "topics": [ "prep_merge_study_data" ] }, { "page": "prep_meta_data_v1_to_item_level_meta_data", "title": "Convert item-level metadata from v1.0 to v2.0", "topics": [ "prep_meta_data_v1_to_item_level_meta_data" ] }, { "page": "prep_min_obs_level", "title": "Support function to identify the levels of a process variable with minimum number of observations", "topics": [ "prep_min_obs_level" ] }, { "page": "prep_open_in_excel", "title": "Open a data frame in Excel", "topics": [ "prep_open_in_excel" ] }, { "page": "prep_pmap", "title": "Support function for a parallel 'pmap'", "topics": [ "prep_pmap" ] }, { "page": "prep_prepare_dataframes", "title": "Prepare and verify study data with metadata", "topics": [ "prep_prepare_dataframes" ] }, { "page": "prep_purge_data_frame_cache", "title": "Clear data frame cache", "concept": [ "data-frame-cache" ], "topics": [ "prep_purge_data_frame_cache" ] }, { "page": "prep_realize_ggplot", "title": "Materialize a lazy 'ggplot'", "topics": [ "prep_realize_ggplot" ] }, { "page": "prep_register_progress_hook", "title": "Register a hook function for progresses in computation/rendering", "topics": [ "prep_register_progress_hook" ] }, { "page": "prep_remove_from_cache", "title": "Remove a specified element from the data frame cache", "concept": [ "data-frame-cache" ], "topics": [ "prep_remove_from_cache" ] }, { "page": "prep_render_pie_chart_from_summaryclasses_ggplot2", "title": "Create a 'ggplot2' pie chart", "concept": [ "summary_functions" ], "topics": [ "prep_render_pie_chart_from_summaryclasses_ggplot2" ] }, { "page": "prep_render_pie_chart_from_summaryclasses_plotly", "title": "Create a 'plotly' pie chart", "concept": [ "summary_functions" ], "topics": [ "prep_render_pie_chart_from_summaryclasses_plotly" ] }, { "page": "prep_robust_guess_data_type", "title": "Guess the data type of a vector", "topics": [ "prep_robust_guess_data_type" ] }, { "page": "prep_save_report", "title": "Save a 'dq_report2'", "topics": [ "prep_save_report" ] }, { "page": "prep_scalelevel_from_data_and_metadata", "title": "Heuristics to amend a SCALE_LEVEL column and a UNIT column in the metadata", "topics": [ "prep_scalelevel_from_data_and_metadata" ] }, { "page": "prep_set_backend", "title": "Change the back-end of a report", "topics": [ "prep_set_backend" ] }, { "page": "prep_study2meta", "title": "Guess a metadata data frame from study data.", "topics": [ "prep_study2meta" ] }, { "page": "prep_summary_to_classes", "title": "Classify metrics from a report summary table", "concept": [ "summary_functions" ], "topics": [ "prep_summary_to_classes" ] }, { "page": "prep_title_escape", "title": "Prepare a label as part of a title text for 'RMD' files", "topics": [ "prep_title_escape" ] }, { "page": "prep_undisclose", "title": "Remove data disclosing details", "topics": [ "prep_undisclose" ] }, { "page": "prep_unsplit_val_tabs", "title": "Combine all missing and value lists to one big table", "topics": [ "prep_unsplit_val_tabs" ] }, { "page": "prep_valuelabels_from_data", "title": "Get value labels from data", "topics": [ "prep_valuelabels_from_data" ] }, { "page": "print.dataquieR_result", "title": "Print a dataquieR result returned by dq_report2", "topics": [ "dataquieR_result", "print.dataquieR_result" ] }, { "page": "print.dataquieR_resultset", "title": "Generate a RMarkdown-based report from a dataquieR report", "topics": [ "print.dataquieR_resultset" ] }, { "page": "print.dataquieR_resultset2", "title": "Generate a HTML-based report from a dataquieR report", "topics": [ "print.dataquieR_resultset2" ] }, { "page": "print.dataquieR_summary", "title": "Print a 'dataquieR' summary", "topics": [ "print.dataquieR_summary" ] }, { "page": "print.dataquieR_translated", "title": "'print' implementation for the class 'dataquieR_translated'", "topics": [ "print.dataquieR_translated" ] }, { "page": "print.DataSlot", "title": "Print a 'DataSlot' object", "topics": [ "print.DataSlot" ] }, { "page": "print.interval", "title": "print implementation for the class 'interval'", "topics": [ "print.interval" ] }, { "page": "print.list", "title": "print a list of 'dataquieR_result' objects", "topics": [ "print.list" ] }, { "page": "print.master_result", "title": "Print a 'master_result' object", "topics": [ "print.master_result" ] }, { "page": "print.numeric_with_unit", "title": "Print a number with unit", "topics": [ "print.numeric_with_unit" ] }, { "page": "print.ReportSummaryTable", "title": "print implementation for the class 'ReportSummaryTable'", "topics": [ "print.ReportSummaryTable" ] }, { "page": "print.Slot", "title": "Print a 'Slot' object", "topics": [ "print.Slot" ] }, { "page": "print.StudyDataSlot", "title": "Print a 'StudyDataSlot' object", "topics": [ "print.StudyDataSlot" ] }, { "page": "print.TableSlot", "title": "Print a 'TableSlot' object", "topics": [ "print.TableSlot" ] }, { "page": "print.util_pairs_ggplot_panels", "title": "Print method for 'util_pairs_ggplot_panels' objects", "topics": [ "print.util_pairs_ggplot_panels" ] }, { "page": "pro_applicability_matrix", "title": "Check applicability of DQ functions on study data", "topics": [ "pro_applicability_matrix" ] }, { "page": "progress_init_fkt", "title": "function to call on progress initialization", "concept": [ "options" ], "topics": [ "progress_init_fkt" ] }, { "page": "rbind.ReportSummaryTable", "title": "Combine 'ReportSummaryTable' outputs", "topics": [ "rbind.ReportSummaryTable" ] }, { "page": "REL_VAL", "title": "Cross-item level metadata attribute name", "concept": [ "meta_data_cross" ], "topics": [ "REL_VAL" ] }, { "page": "RELCOMPL_SPEED", "title": "Cross-item level metadata attribute name", "concept": [ "SSI", "meta_data_cross" ], "topics": [ "RELCOMPL_SPEED" ] }, { "page": "resnames", "title": "Return names of result slots (e.g., 3rd dimension of dataquieR results)", "topics": [ "resnames" ] }, { "page": "resnames.dataquieR_resultset2", "title": "Return names of result slots (e.g., 3rd dimension of dataquieR results)", "topics": [ "resnames.dataquieR_resultset2" ] }, { "page": "RESPT_PER_ITEM", "title": "Cross-item level metadata attribute name", "concept": [ "SSI", "meta_data_cross" ], "topics": [ "RESPT_PER_ITEM" ] }, { "page": "SCALE_ACRONYM", "title": "Cross-item level metadata attribute name TODO", "concept": [ "meta_data_cross" ], "topics": [ "SCALE_ACRONYM" ] }, { "page": "SCALE_LEVELS", "title": "Scale Levels", "topics": [ "SCALE_LEVELS" ] }, { "page": "SCALE_NAME", "title": "Cross-item level metadata attribute name TODO", "concept": [ "meta_data_cross" ], "topics": [ "SCALE_NAME" ] }, { "page": "SEGMENT_ID_REF_TABLE", "title": "Segment level metadata attribute name", "topics": [ "SEGMENT_ID_REF_TABLE" ] }, { "page": "SEGMENT_ID_TABLE", "title": "Deprecated segment level metadata attribute name", "topics": [ "SEGMENT_ID_TABLE" ] }, { "page": "SEGMENT_ID_VARS", "title": "Segment level metadata attribute name", "topics": [ "SEGMENT_ID_VARS" ] }, { "page": "SEGMENT_MISS", "title": "Segment level metadata attribute name", "topics": [ "SEGMENT_MISS" ] }, { "page": "SEGMENT_PART_VARS", "title": "Segment level metadata attribute name", "topics": [ "SEGMENT_PART_VARS" ] }, { "page": "SEGMENT_RECORD_CHECK", "title": "Segment level metadata attribute name", "topics": [ "SEGMENT_RECORD_CHECK" ] }, { "page": "SEGMENT_RECORD_COUNT", "title": "Segment level metadata attribute name", "topics": [ "SEGMENT_RECORD_COUNT" ] }, { "page": "SEGMENT_UNIQUE_ID", "title": "Segment level metadata attribute name", "topics": [ "SEGMENT_UNIQUE_ID" ] }, { "page": "SEGMENT_UNIQUE_ROWS", "title": "Segment level metadata attribute name", "topics": [ "SEGMENT_UNIQUE_ROWS" ] }, { "page": "SPLIT_CHAR", "title": "Character used by default as a separator in metadata such as missing codes", "topics": [ "SPLIT_CHAR" ] }, { "page": "study_data", "title": "Data frame with the study data whose quality is being assessed", "topics": [ "study_data" ] }, { "page": "summary.dataquieR_resultset", "title": "Summarize a dataquieR report", "topics": [ "summary.dataquieR_resultset" ] }, { "page": "summary.dataquieR_resultset2", "title": "Generate a report summary table", "topics": [ "summary.dataquieR_resultset2" ] }, { "page": "to_basic.GeomPointrangeRobust", "title": "Internally used point-range", "topics": [ "to_basic.GeomPointrangeRobust" ] }, { "page": "TOTRESPT", "title": "Cross-item level metadata attribute name", "concept": [ "SSI", "meta_data_cross" ], "topics": [ "TOTRESPT" ] }, { "page": "UNIT_IS_COUNT", "title": "Is a unit a count according to 'units::valid_udunits()'", "concept": [ "UNITS" ], "topics": [ "UNIT_IS_COUNT" ] }, { "page": "UNIT_PREFIX_FACTORS", "title": "Factors related to unit prefixes 'units::valid_udunits_prefixes()'", "concept": [ "UNITS" ], "topics": [ "UNIT_PREFIX_FACTORS" ] }, { "page": "UNIT_PREFIXES", "title": "Valid unit prefixes according to 'units::valid_udunits_prefixes()'", "concept": [ "UNITS" ], "topics": [ "UNIT_PREFIXES" ] }, { "page": "UNIT_SOURCES", "title": "Maturity stage of a unit according to 'units::valid_udunits()'", "concept": [ "UNITS" ], "topics": [ "UNIT_SOURCES" ] }, { "page": "UNITS", "title": "Valid unit symbols according to 'units::valid_udunits()'", "concept": [ "UNITS" ], "topics": [ "UNITS" ] }, { "page": "value-slash-missing-lists", "title": "Data frame with labels for missing- and jump-codes #' Metadata about value and missing codes", "topics": [ "cause_label_df", "CODE_CLASS", "CODE_INTERPRET", "CODE_LABEL", "CODE_VALUE", "missing_matchtable", "value/missing-lists", "value_label_table" ] }, { "page": "VARATT_REQUIRE_LEVELS", "title": "Requirement levels of certain metadata columns", "topics": [ 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