Data Quality Assessment on the ISO/IEC 25012 Model

Documentation for package ‘datagrade’ version 0.2.0

analyze_correlation	Correlation matrix, optionally per category
analyze_distribution	Assess the distribution of every numeric column
analyze_missing_data_patterns	Summarise missing-data patterns
as.data.frame.dg_report	Coerce a quality report to a one-row data frame
calculate_consistency_score	Consistency score from multicollinearity
check_unique_identifiers	Identify columns that could serve as a unique key
clean_text_columns	Normalise text columns without destroying information
data_quality_assessment	Assess the quality of a data set
detect_data_types	Detect the semantic type of every column
detect_duplicates	Count duplicate rows
detect_outliers	Detect outliers in numeric columns
dg_assess	Assess the quality of a data set
dg_plots	Figures for a quality report
dg_save_plots	Save every report figure to a directory
dg_spec	Declare what the data is supposed to look like
dg_vif	Variance inflation factors
flag_anomalies_in_relationships	Flag rows whose variable relationships look anomalous
flag_redundant_columns	Flag redundant columns by pairwise correlation
impute_missing_values	Impute missing numeric values
plot.dg_report	Figures for a quality report
print.dg_report	Print a quality report
summary.dg_report	Tabular summary of a quality report
theme_dg	A recessive ggplot2 theme for quality-report figures
validate_dates	Check date columns for impossible and stale values