Quality Control and Semantic Enrichment of Datasets

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Documentation for package ‘eHDPrep’ version 1.3.3

• DESCRIPTION file.
• User guides, package vignettes and other documentation.
• Package NEWS.

Help Pages

apply_quality_ctrl	Apply quality control measures to a dataset
assess_completeness	Assess completeness of a dataset
assess_quality	Assess quality of a dataset
assume_var_classes	Assume variable classes in data
cellspec_lgl	Kable logical data highlighting
compare_completeness	Compare Completeness between Datasets
compare_info_content	Information Content Comparison Table
compare_info_content_plt	Information Content Comparison Plot
completeness_heatmap	Completeness Heatmap
count_compare	Compare unique values before and after data modification
discrete.mi	Calculate mutual information of a matrix of discrete values
distant_neg_val	Find highly distant value for data frame
edge_tbl_to_graph	Convert edge table to tidygraph graph
encode_as_num_mat	Convert data frame to numeric matrix
encode_binary_cats	Encode categorical variables as binary factors
encode_bin_cat_vec	Encode a categorical vector with binary categories
encode_cats	Encode categorical variables using one-hot encoding.
encode_genotypes	Encode genotype/SNP variables in data frame
encode_genotype_vec	Encode a genotype/SNP vector
encode_ordinals	Encode ordinal variables
entropy	Calculate Entropy of a Vector
exact.kde	Exact kernel density estimation
example_data	Example data for eHDPrep
example_edge_tbl	Example ontology as an edge table for semantic enrichment
example_mapping_file	Example mapping file for semantic enrichment
example_ontology	Example ontology as a network graph for semantic enrichment
export_dataset	Export data to delimited file
extract_freetext	Extract information from free text
identify_inconsistency	Identify inconsistencies in a dataset
import_dataset	Import data into 'R'
import_var_classes	Import corrected variable classes
information_content_contin	Calculate Information Content (Continuous Variable)
information_content_discrete	Calculate Information Content (Discrete Variable)
join_vars_to_ontol	Join Mapping Table to Ontology Network Graph
max_catchNAs	Find maximum of vector safely
mean_catchNAs	Find mean of vector safely
merge_cols	Merge columns in data frame
metavariable_agg	Aggregate Data by Metavariable
metavariable_info	Compute Metavariable Information
metavariable_variable_descendants	Extract metavariables' descendant variables
min_catchNAs	Find minimum of vector safely
mi_content_discrete	Calculate Mutual Information Content
mod_track	Data modification tracking
node_IC_zhou	Calculate Node Information Content (Zhou et al 2008 method)
normalize	Min max normalization
nums_to_NA	Replace numeric values in numeric columns with NA
onehot_vec	One hot encode a vector
ordinal_label_levels	Extract labels and levels of ordinal variables in a dataset
plot_completeness	Plot Completeness of a Dataset
prod_catchNAs	Find product of vector safely
report_var_mods	Track changes to dataset variables
review_quality_ctrl	Review Quality Control
row_completeness	Calculate Row Completeness in a Data Frame
semantic_enrichment	Semantic enrichment
skipgram_append	Append Skipgram Presence Variables to Dataset
skipgram_freq	Report Skipgram Frequency
skipgram_identify	Identify Neighbouring Words (Skipgrams) in a free-text vector
strings_to_NA	Replace values in non-numeric columns with NA
sum_catchNAs	Sum vector safely for semantic enrichment
validate_consistency_tbl	Validate internal consistency table
validate_mapping_tbl	Validate mapping table for semantic enrichment
validate_ontol_nw	Validate ontology network for semantic enrichment
variable.bw.kde	Variable bandwidth Kernel Density Estimation
variable_completeness	Calculate Variable Completeness in a Data Frame
variable_entropy	Calculate Entropy of Each Variable in Data Frame
warn_missing_dots	Missing dots warning
zero_entropy_variables	Identify variables with zero entropy