Package: eHDPrep 1.3.3.9000

Ian Overton

eHDPrep: Quality Control and Semantic Enrichment of Datasets

A tool for the preparation and enrichment of health datasets for analysis (Toner et al. (2023) <doi:10.1093/gigascience/giad030>). Provides functionality for assessing data quality and for improving the reliability and machine interpretability of a dataset. 'eHDPrep' also enables semantic enrichment of a dataset where metavariables are discovered from the relationships between input variables determined from user-provided ontologies.

Authors:Tom Toner [aut], Ian Overton [aut, cre]

eHDPrep_1.3.3.9000.tar.gz
eHDPrep_1.3.3.9000.zip(r-4.5)eHDPrep_1.3.3.9000.zip(r-4.4)eHDPrep_1.3.3.9000.zip(r-4.3)
eHDPrep_1.3.3.9000.tgz(r-4.4-any)eHDPrep_1.3.3.9000.tgz(r-4.3-any)
eHDPrep_1.3.3.9000.tar.gz(r-4.5-noble)eHDPrep_1.3.3.9000.tar.gz(r-4.4-noble)
eHDPrep_1.3.3.9000.tgz(r-4.4-emscripten)eHDPrep_1.3.3.9000.tgz(r-4.3-emscripten)
eHDPrep.pdf |eHDPrep.html✨
eHDPrep/json (API)
NEWS

# Install 'eHDPrep' in R:

install.packages('eHDPrep', repos = c('https://overton-group.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/overton-group/ehdprep/issues

Datasets:

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

On CRAN:

data-quality health-informatics semantic-enrichment

4.90 score 8 stars 8 scripts 228 downloads 46 exports 89 dependencies

Last updated 1 years agofrom:4a2f499bce. Checks:OK: 1 NOTE: 6. Indexed: yes.

Target	Result	Date
Doc / Vignettes	OK	Nov 12 2024
R-4.5-win	NOTE	Nov 12 2024
R-4.5-linux	NOTE	Nov 12 2024
R-4.4-win	NOTE	Nov 12 2024
R-4.4-mac	NOTE	Nov 12 2024
R-4.3-win	NOTE	Nov 12 2024
R-4.3-mac	NOTE	Nov 12 2024

Exports:apply_quality_ctrl assess_completeness assess_quality assume_var_classes compare_completeness compare_info_content compare_info_content_plt completeness_heatmap count_compare edge_tbl_to_graph encode_as_num_mat encode_binary_cats encode_cats encode_genotypes encode_ordinals entropy export_dataset extract_freetext identify_inconsistency import_dataset import_var_classes information_content_contin information_content_discrete join_vars_to_ontol merge_cols metavariable_agg metavariable_info metavariable_variable_descendants mi_content_discrete mod_track node_IC_zhou nums_to_NA ordinal_label_levels plot_completeness report_var_mods review_quality_ctrl row_completeness semantic_enrichment skipgram_append skipgram_freq skipgram_identify strings_to_NA validate_consistency_tbl variable_completeness variable_entropy zero_entropy_variables

Dependencies:base64enc BH bit bit64 bslib cachem cellranger cli clipr colorspace cpp11 crayon digest dplyr evaluate fansi farver fastmap fastmatch fontawesome forcats fs generics ggplot2 glue gtable highr hms htmltools igraph isoband ISOcodes jquerylib jsonlite kableExtra knitr labeling lattice lifecycle magrittr MASS Matrix memoise mgcv mime munsell nlme NLP pheatmap pillar pkgconfig prettyunits progress purrr quanteda R6 rappdirs RColorBrewer Rcpp readr readxl rematch rlang rmarkdown rstudioapi sass scales slam SnowballC stopwords stringi stringr svglite systemfonts tibble tidygraph tidyr tidyselect tinytex tm tzdb utf8 vctrs viridisLite vroom withr xfun xml2 yaml

'eHDPrep': an 'R' package for Electronic Health Data Quality Control and Semantic Enrichment

Tom Toner, Ian Overton

Rendered fromIntroduction_to_eHDPrep.Rmdusingknitr::rmarkdownon Nov 12 2024.

Last update: 2023-06-01
Started: 2022-07-26

Citation

Development and contributors

Readme and manuals

Help Manual

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