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  "Title": "Tools for Data Diagnosis, Exploration, Transformation",
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  "Date": "2025-09-11",
  "Authors@R": "c(\nperson(\"Choonghyun\", \"Ryu\",, \"choonghyun.ryu@gmail.com\", role = c(\"aut\", \"cre\"))\n)",
  "Description": "A collection of tools that support data diagnosis,\nexploration, and transformation. Data diagnostics provides\ninformation and visualization of missing values, outliers, and\nunique and negative values to help you understand the\ndistribution and quality of your data. Data exploration\nprovides information and visualization of the descriptive\nstatistics of univariate variables, normality tests and\noutliers, correlation of two variables, and the relationship\nbetween the target variable and predictor. Data transformation\nsupports binning for categorizing continuous variables, imputes\nmissing values and outliers, and resolves skewness. And it\ncreates automated reports that support these three tasks.",
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    "dlookr_blue_paged",
    "dlookr_orange_paged",
    "dlookr_templ_html",
    "eda_paged_report",
    "eda_report",
    "eda_web_report",
    "entropy",
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    "get_transform",
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    "overview",
    "performance_bin",
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    "plot_na_pareto",
    "plot_normality",
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    "pps",
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    "transformation_report",
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      "object": "Carseats",
      "class": [
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        "CompPrice",
        "Income",
        "Advertising",
        "Population",
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        "ShelveLoc",
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        "Education",
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        "US"
      ],
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        "data.frame"
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        "carrier",
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        "hour",
        "minute",
        "time_hour"
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      "table": true,
      "tojson": true
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      "object": "heartfailure",
      "class": [
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        "cpk_enzyme",
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      "object": "jobchange",
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        "city_dev_index",
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        "relevent_experience",
        "enrolled_university",
        "education_level",
        "major_discipline",
        "experience",
        "company_size",
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        "training_hours",
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      "title": "dlookr: Tools for Data Diagnosis, Exploration, Transformation",
      "topics": [
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        "dlookr"
      ]
    },
    {
      "page": "binning",
      "title": "Binning the Numeric Data",
      "topics": [
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      ]
    },
    {
      "page": "binning_by",
      "title": "Optimal Binning for Scoring Modeling",
      "topics": [
        "binning_by"
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    {
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      "page": "Carseats",
      "title": "Sales of Child Car Seats",
      "topics": [
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      "topics": [
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        "compare_category.data.frame"
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      "page": "compare_numeric.data.frame",
      "title": "Compare numerical variables",
      "topics": [
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        "compare_numeric.data.frame"
      ]
    },
    {
      "page": "correlate.data.frame",
      "title": "Compute the correlation coefficient between two variable",
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        "correlate.data.frame",
        "correlate.grouped_df",
        "correlate.tbl_dbi"
      ]
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      "title": "Compute descriptive statistic",
      "topics": [
        "describe",
        "describe.data.frame",
        "describe.grouped_df"
      ]
    },
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      "title": "Compute descriptive statistic",
      "topics": [
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    },
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      "title": "Diagnose data quality of variables",
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        "diagnose.data.frame",
        "diagnose.grouped_df"
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    },
    {
      "page": "diagnose_category.data.frame",
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        "diagnose_category.data.frame",
        "diagnose_category.grouped_df"
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        "diagnose_numeric.data.frame",
        "diagnose_numeric.grouped_df"
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        "diagnose_outlier.grouped_df"
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        "diagnose_paged_report.data.frame"
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        "diagnose_report.data.frame"
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    {
      "page": "diagnose_report.tbl_dbi",
      "title": "Reporting the information of data diagnosis for table of the DBMS",
      "topics": [
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    },
    {
      "page": "diagnose_sparese.data.frame",
      "title": "Diagnosis of level combinations of categorical variables",
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        "diagnose_sparese.data.frame"
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    {
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      "title": "Reporting the information of data diagnosis with html",
      "topics": [
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        "diagnose_web_report.data.frame"
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    },
    {
      "page": "diagnose_web_report.tbl_dbi",
      "title": "Reporting the information of data diagnosis for table of the DBMS with html",
      "topics": [
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    },
    {
      "page": "diagnose.tbl_dbi",
      "title": "Diagnose data quality of variables in the DBMS",
      "topics": [
        "diagnose.tbl_dbi"
      ]
    },
    {
      "page": "dlookr_orange_paged",
      "title": "Generate paged HTML document",
      "topics": [
        "dlookr_blue_paged",
        "dlookr_orange_paged"
      ]
    },
    {
      "page": "dlookr_templ_html",
      "title": "dlookr HTML template",
      "topics": [
        "dlookr_templ_html"
      ]
    },
    {
      "page": "eda_paged_report.data.frame",
      "title": "Reporting the information of EDA",
      "topics": [
        "eda_paged_report",
        "eda_paged_report.data.frame"
      ]
    },
    {
      "page": "eda_paged_report.tbl_dbi",
      "title": "Reporting the information of EDA for table of the DBMS",
      "topics": [
        "eda_paged_report.tbl_dbi"
      ]
    },
    {
      "page": "eda_report.data.frame",
      "title": "Reporting the information of EDA",
      "topics": [
        "eda_report",
        "eda_report.data.frame"
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    },
    {
      "page": "eda_report.tbl_dbi",
      "title": "Reporting the information of EDA for table of the DBMS",
      "topics": [
        "eda_report.tbl_dbi"
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    },
    {
      "page": "eda_web_report.data.frame",
      "title": "Reporting the information of EDA with html",
      "topics": [
        "eda_web_report",
        "eda_web_report.data.frame"
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    },
    {
      "page": "eda_web_report.tbl_dbi",
      "title": "Reporting the information of EDA for table of the DBMS with html",
      "topics": [
        "eda_web_report.tbl_dbi"
      ]
    },
    {
      "page": "entropy",
      "title": "Calculate the entropy",
      "topics": [
        "entropy"
      ]
    },
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      "page": "extract.bins",
      "title": "Extract bins from \"bins\"",
      "topics": [
        "extract",
        "extract.bins"
      ]
    },
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      "page": "find_class",
      "title": "Extract variable names or indices of a specific class",
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    },
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      "page": "find_na",
      "title": "Finding variables including missing values",
      "topics": [
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    },
    {
      "page": "find_outliers",
      "title": "Finding variables including outliers",
      "topics": [
        "find_outliers"
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    },
    {
      "page": "find_skewness",
      "title": "Finding skewed variables",
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      "page": "flights",
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        "flights"
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      "page": "get_class",
      "title": "Extracting a class of variables",
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    {
      "page": "get_column_info",
      "title": "Describe column of table in the DBMS",
      "topics": [
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      "page": "get_os",
      "title": "Finding Users Machine's OS",
      "topics": [
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      "page": "import_google_font",
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      "title": "Impute Missing Values",
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      "page": "imputate_outlier",
      "title": "Impute Outliers",
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      "page": "jobchange",
      "title": "Job Change of Data Scientists",
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        "jobchange"
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      "page": "jsd",
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        "jsd"
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      "page": "kld",
      "title": "Kullback-Leibler Divergence",
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        "kld"
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      "page": "kurtosis",
      "title": "Kurtosis of the data",
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        "kurtosis"
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      "title": "Performs the Shapiro-Wilk test of normality",
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        "normality.data.frame",
        "normality.grouped_df"
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      "title": "Performs the Shapiro-Wilk test of normality",
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      "page": "overview",
      "title": "Describe overview of data",
      "topics": [
        "overview"
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    {
      "page": "performance_bin",
      "title": "Diagnose Performance Binned Variable",
      "topics": [
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      "page": "plot_bar_category.data.frame",
      "title": "Plot bar chart of categorical variables",
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        "plot_bar_category.grouped_df"
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    },
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      "page": "plot_box_numeric.data.frame",
      "title": "Plot Box-Plot of numerical variables",
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        "plot_box_numeric.grouped_df"
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      "page": "plot_hist_numeric.data.frame",
      "title": "Plot histogram of numerical variables",
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      "page": "plot_na_hclust",
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    {
      "page": "plot_na_intersect",
      "title": "Plot the combination variables that is include missing value",
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    },
    {
      "page": "plot_na_pareto",
      "title": "Pareto chart for missing value",
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      "page": "plot_normality.data.frame",
      "title": "Plot distribution information of numerical data",
      "topics": [
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        "plot_normality.data.frame",
        "plot_normality.grouped_df"
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    },
    {
      "page": "plot_normality.tbl_dbi",
      "title": "Plot distribution information of numerical data",
      "topics": [
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      "page": "plot_outlier.data.frame",
      "title": "Plot outlier information of numerical data diagnosis",
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    },
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      "page": "plot_outlier.target_df",
      "title": "Plot outlier information of target_df",
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      "page": "plot_outlier.tbl_dbi",
      "title": "Plot outlier information of numerical data diagnosis in the DBMS",
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      "page": "plot_qq_numeric.data.frame",
      "title": "Plot Q-Q plot of numerical variables",
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        "plot_qq_numeric.grouped_df"
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      "page": "plot.bins",
      "title": "Visualize Distribution for a \"bins\" object",
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      "title": "Visualize Information for an \"compare_category\" Object",
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      "page": "plot.optimal_bins",
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      "page": "plot.overview",
      "title": "Visualize Information for an \"overview\" Object",
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      "title": "Visualize Performance for an \"performance_bin\" Object",
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    {
      "page": "plot.pps",
      "title": "Visualize Information for an \"pps\" Object",
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      "page": "plot.relate",
      "title": "Visualize Information for an \"relate\" Object",
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      "page": "plot.transform",
      "title": "Visualize Information for an \"transform\" Object",
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      "title": "Compute Predictive Power Score",
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      "title": "Summarizing relate information",
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      "title": "Summarizing Binned Variable",
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      "title": "Summarizing Correlation Coefficient",
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      "title": "Summarizing imputation information",
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      "title": "Summarizing overview information",
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      "title": "Summarizing Performance for Binned Variable",
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      "page": "target_by.data.frame",
      "title": "Target by one variables",
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        "target_by.data.frame"
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      "page": "target_by.tbl_dbi",
      "title": "Target by one column in the DBMS",
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      "headings": [
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        "Supported data structures",
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        "Diagnosis of categorical variables with diagnose_category()",
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        "Visualization of outliers using plot_outlier()",
        "Visualization for missing values",
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        "visualize combination chart using plot_na_hclust()",
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        "Create a diagnostic report using diagnose_paged_report()",
        "Contents of static paged report",
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        "Diagnosing tables in DBMS",
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        "Diagnose data quality of variables in the DBMS",
        "Diagnose data quality of categorical variables in the DBMS",
        "Diagnose data quality of numerical variables in the DBMS",
        "Diagnose outlier of numerical variables in the DBMS",
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      "title": "Data Transformation",
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      "headings": [
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        "datasets",
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        "Imputation of missing values",
        "imputes the missing value with imputate_na()",
        "Collaboration with dplyr",
        "Impute outliers",
        "imputes the outliers with imputate_outlier()",
        "Standardization and Resolving Skewness",
        "Introduction to the use of transform()",
        "Standardization with transform()",
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        "Binning",
        "Binning of individual variables using binning()",
        "Optimal Binning with binning_by()",
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        "Create a dynamic report using transformation_web_report()",
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        "Create a static report using transformation_paged_report()",
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        "Some arguments for static paged report",
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      "title": "Exploratory Data Analysis",
      "author": "Choonghyun Ryu",
      "engine": "knitr::rmarkdown",
      "headings": [
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        "Supported data structures",
        "datasets",
        "Exploratory Data Analysis",
        "Univariate data EDA",
        "Calculating descriptive statistics using describe()",
        "Test of normality on numeric variables using normality()",
        "Visualization of normality of numerical variables using plot_normality()",
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        "Exploratory data analysis for tables in DBMS",
        "Preparing table data",
        "Calculating descriptive statistics of numerical column of table in the DBMS",
        "Test of normality on numeric columns using in the DBMS",
        "Normalization visualization of numerical column in the DBMS",
        "Compute the correlation coefficient between two columns of the table in DBMS",
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      "title": "Introduce dlookr",
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      "headings": [
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        "Supported data structures",
        "List of supported tasks of data analytics",
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      "created": "2020-09-02 03:38:11",
      "modified": "2023-12-29 23:18:47",
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