gtsummary (version 1.2.0)

tbl_summary: Create a table of summary statistics

Description

The tbl_summary function calculates descriptive statistics for continuous, categorical, and dichotomous variables. Review the tbl_summary vignette for detailed examples.

Usage

tbl_summary(data, by = NULL, label = NULL, statistic = NULL,
  digits = NULL, type = NULL, value = NULL, group = NULL,
  missing = c("ifany", "always", "no"), missing_text = "Unknown",
  sort = NULL, percent = c("column", "row", "cell"))

Value

A tbl_summary object

select helpers

Select helpers from the {tidyselect} package and {gtsummary} package are available to modify default behavior for groups of variables. For example, by default continuous variables are reported with the median and IQR. To change all continuous variables to mean and standard deviation use statistic = list(all_continuous() ~ "{mean} ({sd})").

All columns with class logical are displayed as dichotomous variables showing the proportion of events that are TRUE on a single row. To show both rows (i.e. a row for TRUE and a row for FALSE) use type = list(all_logical() ~ "categorical").

The select helpers are available for use in any argument that accepts a list of formulas (e.g. statistic, type, digits, value, sort, etc.)

statistic argument

The statistic argument specifies the statistics presented in the table. The input is a list of formulas that specify the statistics to report. For example, statistic = list("age" ~ "{mean} ({sd})") would report the mean and standard deviation for age; statistic = list(all_continuous() ~ "{mean} ({sd})") would report the mean and standard deviation for all continuous variables. A statistic name that appears between curly brackets will be replaced with the numeric statistic (see glue::glue).

For categorical variables the following statistics are available to display.

• {n} frequency

• {N} denominator, or cohort size

• {p} formatted percentage

For continuous variables the following statistics are available to display.

• {median} median

• {mean} mean

• {sd} standard deviation

• {var} variance

• {min} minimum

• {max} maximum

• {p##} any integer percentile, where ## is an integer from 0 to 100

• {foo} any function of the form foo(x) is accepted where x is a numeric vector

type argument

tbl_summary displays summary statistics for three types of data: continuous, categorical, and dichotomous. If the type is not specified, tbl_summary will do its best to guess the type. Dichotomous variables are categorical variables that are displayed on a single row in the output table, rather than one row per level of the variable. Variables coded as TRUE/FALSE, 0/1, or yes/no are assumed to be dichotomous, and the TRUE, 1, and yes rows will be displayed. Otherwise, the value to display must be specified in the value argument, e.g. value = list("varname" ~ "level to show")

Example Output

Example 1

Example 2

See Also

See tbl_summary vignette for detailed examples

Other tbl_summary tools: add_n, add_overall, add_p, add_q.tbl_summary, add_stat_label, bold_italicize_labels_levels, bold_p.tbl_summary, inline_text.tbl_summary, modify_header, sort_p.tbl_summary

Examples

# NOT RUN {
tbl_summary_ex1 <-
  trial %>%
  dplyr::select(age, grade, response) %>%
  tbl_summary()

tbl_summary_ex2 <-
  trial %>%
  dplyr::select(age, grade, response, trt) %>%
  tbl_summary(
    by = trt,
    label = list(vars(age) ~ "Patient Age"),
    statistic = list(all_continuous() ~ "{mean} ({sd})"),
    digits = list(vars(age) ~ c(0, 1))
  )
# }