Package index • huxtableone

Table creators

Create tabular summaries of data in a simple manner.

describe_population(): Describe the population in a summary table

describe_data(): Describe the data types and consistence

compare_population(): Compares the population against an intervention in a summary table

compare_outcomes(): Compares multiple outcomes against an intervention in a summary table

group_comparison(): Extract one or more comparisons for inserting into text.

compare_missing(): Compares missing data against an intervention in a summary table

remove_missing(): Remove variables that fail a missing data test from models

count_table(): Group data count and calculate proportions by column.

extract_comparison(): Get summary comparisons and statistics between variables as raw data.

S3 functions

Create tabular summaries of data in a simple manner.

as_t1_shape(): Summarise a data set

as_t1_signif(): Compares the population against an intervention

as_t1_summary(): Summarise a population

as_huxtable(<t1_shape>): Convert a t1_summary object to a huxtable

as_huxtable(<t1_signif>): Convert a t1_signif S3 class to a huxtable

as_huxtable(<t1_summary>): Convert a t1_summary object to a huxtable

Supporting functions

Modify data for making tabular summaries, making missing data more explicit or by converting discrete data types to explicit factors.

make_factors(): Convert discrete data to factors

explicit_na(): Make NA values in factor columns explicit

get_footer_text(): Get footer text if available

format_pvalue(): Format a p-value

cut_integer(): Cut and label an integer valued quantity

label_extractor(): Extract labels from a dataframe column attributes

set_labels(): Set a label attribute

extract_units(): Extracts units set as dataframe column attributes

set_units(): Title

as_vars(): Reuse tidy-select syntax outside of a tidy-select function

Configuration

default.format: Default table layout functions

Test data

test_cols: A list of columns for a test case

bad_test_cols: A list of columns for a test case

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

two_class_test: A two-class dataset with random data