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Summarise a population

Source: R/describe_population.R
as_t1_summary.Rd

The population description is a simple summary of the co-variates in a data set with no reference to outcome, and not comparing intervention (although it might contain intervention rates.) It will report summary statistics for continuous and counts for categorical data,

Usage

as_t1_summary(
  df,
  ...,
  label_fn = label_extractor(df),
  units = extract_units(df),
  override_type = list()
)

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 tidyselect specification (see utils::vignette("syntax", package = "tidyselect")), identifying the columns. Alternatively it can be given as a formula of the nature

outcome ~ 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("tableone.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.

override_type

(optional) a named list of data summary types. The default type for a column in a data set are calculated using heurisitics depending on the nature of the data (categorical or continuous), and result of normality tests. if you want to override this the options are "subtype_count","median_iqr","mean_sd","skipped" and you specify this on a column by column bases with a named list (e.g c("Petal.Width"="mean_sd")). Overriding the default does not check the type of data is correct for the summary type and will potentially cause errors if this is not done correctly.

Value

a t1_summary data frame.

Examples

tmp = iris %>% as_t1_summary(
  tidyselect::everything(),
  override_type = c(Petal.Length = "mean_sd", Petal.Width = "mean_sd")
)
#> mean_sd summary for Sepal.Length
#> mean_sd summary for Sepal.Width
#> mean_sd summary for Petal.Length
#> mean_sd summary for Petal.Width
#> subtype_count summary for Species