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
describe_data(
df,
...,
label_fn = label_extractor(df),
units = extract_units(df),
layout = "single",
font_size = getOption("tableone.font_size", 8),
font = getOption("tableone.font", "Arial"),
footer_text = NULL,
raw_output = FALSE
)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("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.- layout
(optional) various layouts are defined as default. As of this version of
tableonethey are "relaxed","compact","micro","simple","single","missing". The layouts can be customised using the optionsoptions("tableone.format_list"=list(...)"), and this is described in more detail in the vignettes.- font_size
(optional) the font size for the table in points
- font
(optional) the font family for the table (which will be matched to closest on your system)
- footer_text
any text that needs to be added at the end of the table, setting this to FALSE dsables the whole footer (as does
options("tableone.hide_footer"=TRUE)).- raw_output
return comparison as
t1_signifdataframe rather than formatted table
Examples
# Overriding the heuristics is possible:
iris %>% describe_data(tidyselect::everything())
#> ──────────────────────────────────────────────────────────────────────────────
#> Variable Values / Type Normally Unique Missing Default
#> Units distribu (%) (%) summary
#> ted
#> ──────────────────────────────────────────────────────────────────────────────
#> Sepal.Le continuo TRUE 23.3% 0.0% mean_sd
#> ngth us
#> Sepal.Wi continuo TRUE 15.3% 0.0% mean_sd
#> dth us
#> Petal.Le continuo FALSE 28.7% 0.0% median_i
#> ngth us qr
#> Petal.Wi continuo FALSE 14.7% 0.0% median_i
#> dth us qr
#> Species setosa, categori — 0.0% 0.0% subtype_
#> versicol cal count
#> or or
#> virginic
#> a
#> ──────────────────────────────────────────────────────────────────────────────
#> Normal distributions determined by the Anderson-Darling
#> test (P>0.005)
#>
#> Column names: Variable, Values / Units, 1, 2, 3, 4, 5, 6, 7
#>
#> 7/9 columns shown.
diamonds %>% dplyr::group_by(is_colored) %>% describe_data(tidyselect::everything())
#> ──────────────────────────────────────────────────────────────────────────────
#> Variable Values / Type Normally Unique Missing Default
#> Units distribu (%) (%) summary
#> ted
#> ──────────────────────────────────────────────────────────────────────────────
#> carat continuo FALSE 0.5% 0.0% median_i
#> us qr
#> z continuo FALSE 0.7% 0.0% median_i
#> us qr
#> cut Fair, ordered — 0.0% 0.0% subtype_
#> Good, count
#> Very
#> Good,
#> Premium
#> or Ideal
#> color D, E, F, ordered — 0.0% 0.0% subtype_
#> G, H, I count
#> or J
#> clarity I1, SI2, ordered — 0.0% 0.0% subtype_
#> SI1, count
#> VS2,
#> VS1,
#> VVS2,
#> VVS1 or
#> IF
#> depth continuo FALSE 0.3% 0.0% median_i
#> us qr
#> table continuo FALSE 0.2% 0.0% median_i
#> us qr
#> price continuo FALSE 21.5% 0.0% median_i
#> us qr
#> x continuo FALSE 1.0% 0.0% median_i
#> us qr
#> y continuo FALSE 1.0% 0.0% median_i
#> us qr
#> ──────────────────────────────────────────────────────────────────────────────
#> Normal distributions determined by the Anderson-Darling
#> test (P>0.005)
#>
#> Column names: Variable, Values / Units, 1, 2, 3, 4, 5, 6, 7
#>
#> 7/9 columns shown.