The data set description is a simple summary of the data formats, types and missingness
Usage
as_t1_shape(df, ..., label_fn = label_extractor(df), units = extract_units(df))Arguments
- df
a dataframe of individual observations. Grouping, if present, is ignored. (n.b. if you wanted to construct multiple summary tables a
dplyr::group_map()call could be used)- ...
the columns of variables we wish to summarise. This can be given as a
tidyselectspecification (seeutils::vignette("syntax", package = "tidyselect")), identifying the columns. Alternatively it can be given as a formula of the natureoutcome ~ intervention + covariate_1 + covariate_2 + ....which may be more convenient if you are going on to do a model fit. If the latter format the left hand side is ignored (outcomes are not usual in this kind of table).
- label_fn
(optional) a function for mapping a co-variate column name to printable label. This is by default a no-operation and the output table will contain the dataframe column names as labels. A simple alternative would be some form of dplyr::case_when lookup, or a string function such as stringr::str_to_sentence. (N.b. this function must be vectorised). Any value provided here will be overridden by the
options("huxtableone.labeller" = my_label_fn)which allows global setting of the labeller.- units
(optional) a named list of units, following a
c(<colname_1> = "<unit_1>", <colname_2> = "<unit_2>", ...)format. columns not present in this list are assumed to have no units. Units may be involved in the formatting of the summary output.
Examples
tmp = iris %>% as_t1_shape(
tidyselect::everything()
)