Tools for Data Diagnosis, Exploration, Transformation
Binning the Numeric Data
Optimal Binning for Scoring Modeling
Binning by recursive information gain ratio maximization
Compare categorical variables
Compare numerical variables
Compute the correlation coefficient between two variable
Cramer's V statistic
Compute descriptive statistic
Compute descriptive statistic
Diagnose data quality of variables
Diagnose data quality of variables in the DBMS
Diagnose data quality of categorical variables
Diagnose data quality of categorical variables in the DBMS
Diagnose data quality of numerical variables
Diagnose data quality of numerical variables in the DBMS
Diagnose outlier of numerical variables
Diagnose outlier of numerical variables in the DBMS
Reporting the information of data diagnosis
Reporting the information of data diagnosis for table of the DBMS
Reporting the information of data diagnosis
Reporting the information of data diagnosis for table of the DBMS
Diagnosis of level combinations of categorical variables
Reporting the information of data diagnosis with html
Reporting the information of data diagnosis for table of the DBMS with...
Deprecated functions in package dlookr
dlookr: Tools for Data Diagnosis, Exploration, Transformation
Generate paged HTML document
dlookr HTML template
Reporting the information of EDA
Reporting the information of EDA for table of the DBMS
Reporting the information of EDA
Reporting the information of EDA for table of the DBMS
Reporting the information of EDA with html
Reporting the information of EDA for table of the DBMS with html
Calculate the entropy
Extract bins from "bins"
Extract variable names or indices of a specific class
Finding variables including missing values
Finding variables including outliers
Finding skewed variables
Extracting a class of variables
Describe column of table in the DBMS
Finding Users Machine's OS
Finding percentile
Transform a numeric vector
Import Google Fonts
Impute Missing Values
Impute Outliers
Jensen-Shannon Divergence
Kullback-Leibler Divergence
Kurtosis of the data
Performs the Shapiro-Wilk test of normality
Performs the Shapiro-Wilk test of normality
Describe overview of data
Diagnose Performance Binned Variable
Visualize Distribution for a "bins" object
Visualize Information for an "compare_category" Object
Visualize Information for an "compare_numeric" Object
Visualize Information for an "correlate" Object
Visualize Information for an "imputation" Object
Visualize Distribution for an "infogain_bins" Object
Visualize Distribution for an "optimal_bins" Object
Visualize Information for an "overview" Object
Visualize Performance for an "performance_bin" Object
Visualize Information for an "pps" Object
Visualize Information for an "relate" Object
Visualize Information for an "transform" Object
Visualize Information for an "univar_category" Object
Visualize Information for an "univar_numeric" Object
Plot bar chart of categorical variables
Plot Box-Plot of numerical variables
Visualize correlation plot of numerical data
Visualize correlation plot of numerical data
Plot histogram of numerical variables
Combination chart for missing value
Plot the combination variables that is include missing value
Pareto chart for missing value
Plot distribution information of numerical data
Plot distribution information of numerical data
Plot outlier information of numerical data diagnosis
Plot outlier information of target_df
Plot outlier information of numerical data diagnosis in the DBMS
Plot Q-Q plot of numerical variables
Compute Predictive Power Score
Summarizing relate information
Relationship between target variable and variable of interest
Skewness of the data
Summarizing Binned Variable
Summarizing compare_category information
Summarizing compare_numeric information
Summarizing Correlation Coefficient
Summarizing imputation information
Summarizing Performance for Optimal Bins
Summarizing overview information
Summarizing Performance for Binned Variable
Summarizing Predictive Power Score
Summarizing transformation information
Summarizing univar_category information
Summarizing univar_numeric information
Target by one variables
Target by one column in the DBMS
Theil's U statistic
Data Transformations
Reporting the information of transformation
Reporting the information of transformation
Reporting the information of transformation with html
Statistic of univariate categorical variables
Statistic of univariate numerical variables
A collection of tools that support data diagnosis, exploration, and transformation. Data diagnostics provides information and visualization of missing values, outliers, and unique and negative values to help you understand the distribution and quality of your data. Data exploration provides information and visualization of the descriptive statistics of univariate variables, normality tests and outliers, correlation of two variables, and the relationship between the target variable and predictor. Data transformation supports binning for categorizing continuous variables, imputes missing values and outliers, and resolves skewness. And it creates automated reports that support these three tasks.
Useful links