Quality Control and Semantic Enrichment of Datasets

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Documentation for package ‘eHDPrep’ version 1.3.3

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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