Package: huxtableone 0.5.0

Robert Challen

huxtableone: Descriptive Tables for Observational or Interventional Studies

Generating tabular summaries of data in a format suitable for reporting in journal articles is fiddly and slows down more detailed analysis. Comparing two populations with respect to an intervention, and reporting it is a task that can be largely automated.

Authors:Robert Challen [aut, cre]

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manual.pdf |manual.html✨
DESCRIPTION
card.svg |card.png
huxtableone/json (API)

# Install 'huxtableone' in R:

install.packages('huxtableone', repos = c('https://ai4ci.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/ai4ci/huxtableone/issues

Pkgdown/docs site:https://ai4ci.github.io

Datasets:

bad_test_cols - A list of columns for a test case
default.format - Default table layout functions
diamonds - A copy of the diamonds dataset
missing_diamonds - A copy of the diamonds dataset
mnar_two_class_1000 - Missing not at random 2 class 1000 items
multi_class_negative - A multi-class dataset with equal random samples in each class
one_class_test_100 - A single-class dataset with 100 items of random data
one_class_test_1000 - A single-class dataset with 1000 items of random data
test_cols - A list of columns for a test case
two_class_test - A two-class dataset with random data

On CRAN:

3.30 score 23 exports 39 dependencies

Last updated from:e58cbb3862 (on 0.5.0). Checks:9 OK. Indexed: yes.

Target	Result	Time
linux-devel-x86_64	OK	216
source / vignettes	OK	239
linux-release-x86_64	OK	212
macos-release-arm64	OK	112
macos-oldrel-arm64	OK	146
windows-devel	OK	125
windows-release	OK	114
windows-oldrel	OK	118
wasm-release	OK	162

Exports:%>%as_t1_shape as_t1_signif as_t1_summary as_vars compare_missing compare_outcomes compare_population count_table cut_integer describe_data describe_population explicit_na extract_comparison extract_units format_pvalue get_footer_text group_comparison label_extractor make_factors remove_missing set_labels set_units

Dependencies:assertthat backports base64enc binom broom cachem cli commonmark cpp11 digest dplyr fansi fastmap forcats generics glue htmltools huxtable jsonlite lifecycle magrittr memoise nortest pillar pkgconfig purrr pwr R6 rlang stringi stringr systemfonts tibble tidyr tidyselect utf8 vctrs withr xml2

huxtableone: Configuration

Rendered fromconfiguration.Rmdusingknitr::rmarkdown

Last update: 2025-02-03
Started: 2022-10-17

huxtableone: Getting started

Rendered fromhuxtableone.Rmdusingknitr::rmarkdown

Last update: 2025-02-03
Started: 2025-02-03

Citation

Development and contributors

Readme and manuals

Help Manual

Help page	Topics
Convert a 't1_summary' object to a 'huxtable'	as_huxtable.t1_shape
Convert a 't1_signif' S3 class to a huxtable	as_huxtable.t1_signif
Convert a 't1_summary' object to a 'huxtable'	as_huxtable.t1_summary
Summarise a data set	as_t1_shape
Compares the population against an intervention	as_t1_signif
Summarise a population	as_t1_summary
Reuse tidy-select syntax outside of a tidy-select function	as_vars
A list of columns for a test case	bad_test_cols
Compares missing data against an intervention in a summary table	compare_missing
Compares multiple outcomes against an intervention in a summary table	compare_outcomes
Compares the population against an intervention in a summary table	compare_population
Group data count and calculate proportions by column.	count_table
Cut and label an integer valued quantity	cut_integer
Default table layout functions	default.format
Describe the data types and consistence	describe_data
Describe the population in a summary table	describe_population
A copy of the diamonds dataset	diamonds
Make NA values in factor columns explicit	explicit_na
Get summary comparisons and statistics between variables as raw data.	extract_comparison
Extracts units set as dataframe column attributes	extract_units
Format a p-value	format_pvalue
Get footer text if available	get_footer_text
Extract one or more comparisons for inserting into text.	group_comparison
Extract labels from a dataframe column attributes	label_extractor
Convert discrete data to factors	make_factors
A copy of the diamonds dataset	missing_diamonds
Missing not at random 2 class 1000 items	mnar_two_class_1000
A multi-class dataset with equal random samples in each class	multi_class_negative
A single-class dataset with 100 items of random data	one_class_test_100
A single-class dataset with 1000 items of random data	one_class_test_1000
Remove variables that fail a missing data test from models	remove_missing
Set a label attribute	set_labels
Title	set_units
A list of columns for a test case	test_cols
A two-class dataset with random data	two_class_test