Simple descriptive tables

Overview

tbl_simple() produces a concise descriptive summary — mean ± SD for continuous variables and n (%) for categorical variables — without any group comparisons or statistical tests. It is the right choice for supplementary tables, sub-cohort descriptions, or any context where you need a clean summary without inference.

Basic usage

tbl_simple(
  clerk_example,
  output = "gt"
) |>
  clerk_render(title = "Sample characteristics")

	n	Summary
Sample characteristics
All variables
sex	300	Female: 193 (64.3%); Male: 107 (35.7%)
age	300	46.76 ± 14.15
hdl	300	46.81 ± 10.11
glucose	300	91.68 ± 24.65
bmi	300	26.15 ± 4.91
waist	300	92.07 ± 11.56
systolic_bp	300	123.25 ± 17.77
tmt_time	300	129.24 ± 64.98
verbal_fluency	300	14.98 ± 4.63
bdi	300	13.64 ± 9.26
panas_neg	300	20.98 ± 7.48
life_satisfaction	300	18.02 ± 4.23

Selecting variables

tbl_simple(
  clerk_example,
  vars   = c(age, hdl, glucose, bmi, waist),
  output = "gt"
) |>
  clerk_render(title = "Metabolic and anthropometric characteristics")

	n	Summary
Metabolic and anthropometric characteristics
All variables
age	300	46.76 ± 14.15
hdl	300	46.81 ± 10.11
glucose	300	91.68 ± 24.65
bmi	300	26.15 ± 4.91
waist	300	92.07 ± 11.56

Domain grouping

tbl_simple(
  clerk_example,
  domains = list(
    "Metabolic"      = c("hdl", "glucose", "bmi"),
    "Anthropometric" = c("waist", "systolic_bp"),
    "Cognitive"      = c("tmt_time", "verbal_fluency"),
    "Mental health"  = c("bdi", "panas_neg", "life_satisfaction")
  ),
  output = "gt"
) |>
  clerk_render(title = "Sample characteristics")
#> Warning in `[<-.factor`(`*tmp*`, is.na(tbl[["domain"]]), value = "Other"):
#> invalid factor level, NA generated

	n	Summary
Sample characteristics
NA
sex	300	Female: 193 (64.3%); Male: 107 (35.7%)
age	300	46.76 ± 14.15
Metabolic
hdl	300	46.81 ± 10.11
glucose	300	91.68 ± 24.65
bmi	300	26.15 ± 4.91
Anthropometric
waist	300	92.07 ± 11.56
systolic_bp	300	123.25 ± 17.77
Cognitive
tmt_time	300	129.24 ± 64.98
verbal_fluency	300	14.98 ± 4.63
Mental health
bdi	300	13.64 ± 9.26
panas_neg	300	20.98 ± 7.48
life_satisfaction	300	18.02 ± 4.23

Log-transformed variables

tbl_simple(
  clerk_example,
  domains  = list("Cognitive" = c("tmt_time", "verbal_fluency")),
  log_vars = "tmt_time",
  output   = "gt"
) |>
  clerk_render(
    title    = "Cognitive variables",
    footnote = "Values shown on raw scale."
  )
#> Warning in `[<-.factor`(`*tmp*`, is.na(tbl[["domain"]]), value = "Other"):
#> invalid factor level, NA generated

	n	Summary
Cognitive variables
NA
sex	300	Female: 193 (64.3%); Male: 107 (35.7%)
age	300	46.76 ± 14.15
hdl	300	46.81 ± 10.11
glucose	300	91.68 ± 24.65
bmi	300	26.15 ± 4.91
waist	300	92.07 ± 11.56
systolic_bp	300	123.25 ± 17.77
bdi	300	13.64 ± 9.26
panas_neg	300	20.98 ± 7.48
life_satisfaction	300	18.02 ± 4.23
Cognitive
tmt_time¹	300	129.24 ± 64.98
verbal_fluency	300	14.98 ± 4.63
¹ Log-transformed variables shown on raw scale.
Values shown on raw scale.

HTML output

tbl_simple(
  clerk_example,
  domains = list(
    "Metabolic"    = c("hdl", "glucose", "bmi"),
    "Mental health"= c("bdi", "panas_neg")
  ),
  output = "html"
) |>
  clerk_render()
#> Warning in `[<-.factor`(`*tmp*`, is.na(tbl[["domain"]]), value = "Other"):
#> invalid factor level, NA generated

When to use `tbl_simple()` vs `tbl_descriptive()`

Use tbl_simple() when there is no meaningful group comparison — for example a single-arm cohort, a subset table, or a supplementary overview. Use tbl_descriptive() when comparing two or more groups and you want statistical test columns alongside the summaries.