Formatting options • clerkR

Overview

All tbl_* constructors inherit their formatting defaults from clerk_options(). Defaults follow biomed/APA conventions and are loaded automatically when the package is attached — no setup required. You can inspect the current settings at any time:

clerk_options()

Options can be changed session-wide with a single call, or overridden per-table by passing the same arguments directly to any tbl_* function.

P-value style

The p_style option controls how p-values are displayed. Four styles are available:

Style	Example output	Use case
`"apa"`	`= 0.032`, `< 0.001`	APA manuscripts (default)
`"plain"`	`0.032`, `< 0.001`	Plain decimal
`"stars"`	`**`	Stars only, no numeric p
`"stars_p"`	`= 0.032 **`	Stars alongside numeric p

# APA style (default)
tbl_descriptive(clerk_example, group = sex, output = "gt") |>
  clerk_render(title = "APA style (default)")

	n	Overall	Female	Male	Statistic	p
APA style (default)

age	300	46.76 ± 14.15	46.42 ± 13.63	47.35 ± 15.09	t = -0.53	= 0.599
hdl	300	46.81 ± 10.11	48.32 ± 9.78	44.08 ± 10.18	t = 3.50	< 0.001
glucose	300	91.68 ± 24.65	92.61 ± 24.49	90.01 ± 24.96	t = 0.87	= 0.386
bmi	300	26.15 ± 4.91	26.43 ± 4.84	25.64 ± 5.01	t = 1.32	= 0.190
waist	300	92.07 ± 11.56	92.14 ± 11.32	91.93 ± 12.03	t = 0.15	= 0.879
systolic_bp	300	123.25 ± 17.77	120.36 ± 17.86	128.48 ± 16.45	t = -3.97	< 0.001
tmt_time	300	129.24 ± 64.98	132.81 ± 66.77	122.81 ± 61.40	t = 1.31	= 0.192
verbal_fluency	300	14.98 ± 4.63	15.08 ± 4.44	14.79 ± 4.97	t = 0.52	= 0.606
bdi	300	13.64 ± 9.26	15.07 ± 8.97	11.05 ± 9.26	t = 3.65	< 0.001
panas_neg	300	20.98 ± 7.48	22.50 ± 7.27	18.25 ± 7.09	t = 4.92	< 0.001
life_satisfaction	300	18.02 ± 4.23	17.40 ± 4.25	19.14 ± 3.99	t = -3.54	< 0.001


# Plain decimal
tbl_descriptive(clerk_example, group = sex, output = "gt",
                p_style = "plain") |>
  clerk_render(title = "Plain decimal")

	n	Overall	Female	Male	Statistic	p
Plain decimal

age	300	46.76 ± 14.15	46.42 ± 13.63	47.35 ± 15.09	t = -0.53	0.599
hdl	300	46.81 ± 10.11	48.32 ± 9.78	44.08 ± 10.18	t = 3.50	< 0.001
glucose	300	91.68 ± 24.65	92.61 ± 24.49	90.01 ± 24.96	t = 0.87	0.386
bmi	300	26.15 ± 4.91	26.43 ± 4.84	25.64 ± 5.01	t = 1.32	0.190
waist	300	92.07 ± 11.56	92.14 ± 11.32	91.93 ± 12.03	t = 0.15	0.879
systolic_bp	300	123.25 ± 17.77	120.36 ± 17.86	128.48 ± 16.45	t = -3.97	< 0.001
tmt_time	300	129.24 ± 64.98	132.81 ± 66.77	122.81 ± 61.40	t = 1.31	0.192
verbal_fluency	300	14.98 ± 4.63	15.08 ± 4.44	14.79 ± 4.97	t = 0.52	0.606
bdi	300	13.64 ± 9.26	15.07 ± 8.97	11.05 ± 9.26	t = 3.65	< 0.001
panas_neg	300	20.98 ± 7.48	22.50 ± 7.27	18.25 ± 7.09	t = 4.92	< 0.001
life_satisfaction	300	18.02 ± 4.23	17.40 ± 4.25	19.14 ± 3.99	t = -3.54	< 0.001

To set a different default for the whole session:

clerk_options(p_style = "plain")

Decimal places

Three separate options control decimal places for different value types:

Option	Default	Controls
`digits`	`2`	Continuous statistics (mean, SD, β, SE)
`r_digits`	`3`	Correlations and h² estimates
`p_digits`	`3`	P-values

tbl_descriptive(clerk_example, group = sex, output = "gt",
                digits = 1, p_digits = 2) |>
  clerk_render(title = "digits = 1, p_digits = 2")

	n	Overall	Female	Male	Statistic	p
digits = 1, p_digits = 2

age	300	46.8 ± 14.2	46.4 ± 13.6	47.4 ± 15.1	t = -0.53	= 0.60
hdl	300	46.8 ± 10.1	48.3 ± 9.8	44.1 ± 10.2	t = 3.50	< 0.00
glucose	300	91.7 ± 24.6	92.6 ± 24.5	90.0 ± 25.0	t = 0.87	= 0.39
bmi	300	26.1 ± 4.9	26.4 ± 4.8	25.6 ± 5.0	t = 1.32	= 0.19
waist	300	92.1 ± 11.6	92.1 ± 11.3	91.9 ± 12.0	t = 0.15	= 0.88
systolic_bp	300	123.3 ± 17.8	120.4 ± 17.9	128.5 ± 16.4	t = -3.97	< 0.00
tmt_time	300	129.2 ± 65.0	132.8 ± 66.8	122.8 ± 61.4	t = 1.31	= 0.19
verbal_fluency	300	15.0 ± 4.6	15.1 ± 4.4	14.8 ± 5.0	t = 0.52	= 0.61
bdi	300	13.6 ± 9.3	15.1 ± 9.0	11.0 ± 9.3	t = 3.65	< 0.00
panas_neg	300	21.0 ± 7.5	22.5 ± 7.3	18.3 ± 7.1	t = 4.92	< 0.00
life_satisfaction	300	18.0 ± 4.2	17.4 ± 4.2	19.1 ± 4.0	t = -3.54	< 0.00

The p_threshold option controls the value below which p-values are shown as < {threshold} rather than as a decimal. The default is 0.001:

# Per-call: show < 0.01 instead of < 0.001
tbl_descriptive(clerk_example, group = sex, output = "gt",
                p_threshold = 0.01) |>
  clerk_render(title = "p_threshold = 0.01")

	n	Overall	Female	Male	Statistic	p
p_threshold = 0.01

age	300	46.76 ± 14.15	46.42 ± 13.63	47.35 ± 15.09	t = -0.53	= 0.599
hdl	300	46.81 ± 10.11	48.32 ± 9.78	44.08 ± 10.18	t = 3.50	< 0.010
glucose	300	91.68 ± 24.65	92.61 ± 24.49	90.01 ± 24.96	t = 0.87	= 0.386
bmi	300	26.15 ± 4.91	26.43 ± 4.84	25.64 ± 5.01	t = 1.32	= 0.190
waist	300	92.07 ± 11.56	92.14 ± 11.32	91.93 ± 12.03	t = 0.15	= 0.879
systolic_bp	300	123.25 ± 17.77	120.36 ± 17.86	128.48 ± 16.45	t = -3.97	< 0.010
tmt_time	300	129.24 ± 64.98	132.81 ± 66.77	122.81 ± 61.40	t = 1.31	= 0.192
verbal_fluency	300	14.98 ± 4.63	15.08 ± 4.44	14.79 ± 4.97	t = 0.52	= 0.606
bdi	300	13.64 ± 9.26	15.07 ± 8.97	11.05 ± 9.26	t = 3.65	< 0.010
panas_neg	300	20.98 ± 7.48	22.50 ± 7.27	18.25 ± 7.09	t = 4.92	< 0.010
life_satisfaction	300	18.02 ± 4.23	17.40 ± 4.25	19.14 ± 3.99	t = -3.54	< 0.010

Significance stars

Stars are off by default. Enable them with stars = TRUE:

tbl_correlation(clerk_cor_example, fdr = TRUE, output = "gt",
                stars = TRUE) |>
  clerk_render(title = "With significance stars")

	Outcome	r	p	p (FDR)
With significance stars

hdl	tmt_time	+0.190	= 0.008**	= 0.032*
glucose	tmt_time	-0.229	= 0.398	ns
bmi	tmt_time	-0.086	= 0.013*	= 0.042*
waist	tmt_time	+0.279	= 0.002**	= 0.024*
systolic_bp	tmt_time	+0.195	= 0.379	ns
bdi	tmt_time	+0.096	= 0.109	ns
panas_neg	tmt_time	+0.157	= 0.006**	= 0.032*
life_satisfaction	tmt_time	+0.247	= 0.117	ns
hdl	verbal_fluency	+0.078	= 0.072	ns
glucose	verbal_fluency	+0.216	= 0.003**	= 0.024*
bmi	verbal_fluency	+0.022	= 0.207	ns
waist	verbal_fluency	+0.047	= 0.185	ns
systolic_bp	verbal_fluency	-0.105	= 0.077	ns
bdi	verbal_fluency	-0.176	= 0.070	ns
panas_neg	verbal_fluency	+0.232	= 0.117	ns
life_satisfaction	verbal_fluency	+0.201	= 0.234	ns
p (FDR): Benjamini-Hochberg false discovery rate correction applied.

The thresholds for *, **, and *** are set by star_thresholds (default c(0.05, 0.01, 0.001)). For stars-only output use p_style = "stars", or combine stars with numeric p-values using p_style = "stars_p":

tbl_regression(clerk_reg_example, output = "gt",
               p_style = "stars_p") |>
  clerk_render(title = "Stars alongside p-values")

	β	SE	95% CI	p
Stars alongside p-values

bmi	+0.02	0.02	[-0.02, +0.07]	0.012*
waist	-0.02	0.02	[-0.07, +0.03]	0.048*
systolic_bp	+0.01	0.01	[-0.01, +0.03]	0.210
bdi	+0.01	0.02	[-0.04, +0.05]	0.330
panas_neg	+0.01	0.02	[-0.02, +0.05]	0.041*
age	+0.00	0.01	[-0.03, +0.03]	0.610
sexMale	-0.04	0.02	[-0.08, -0.01]	0.003**

FDR display

When fdr = TRUE is passed to a tbl_* function, a p (FDR) column is added containing BH-adjusted p-values. Three options control how non-surviving results are displayed:

Option	Default	Effect
`fdr_ns`	`TRUE`	Replace non-surviving cells with `fdr_ns_label`
`fdr_alpha`	`0.05`	Alpha level applied to the BH-adjusted p-value
`fdr_ns_label`	`"ns"`	The label shown for non-surviving cells

tbl_descriptive(clerk_example, group = sex, fdr = TRUE, output = "gt") |>
  clerk_render(title = "FDR with ns label (default)")

	n	Overall	Female	Male	Statistic	p	p (FDR)
FDR with ns label (default)

age	300	46.76 ± 14.15	46.42 ± 13.63	47.35 ± 15.09	t = -0.53	= 0.599	ns
hdl	300	46.81 ± 10.11	48.32 ± 9.78	44.08 ± 10.18	t = 3.50	< 0.001	= 0.001
glucose	300	91.68 ± 24.65	92.61 ± 24.49	90.01 ± 24.96	t = 0.87	= 0.386	ns
bmi	300	26.15 ± 4.91	26.43 ± 4.84	25.64 ± 5.01	t = 1.32	= 0.190	ns
waist	300	92.07 ± 11.56	92.14 ± 11.32	91.93 ± 12.03	t = 0.15	= 0.879	ns
systolic_bp	300	123.25 ± 17.77	120.36 ± 17.86	128.48 ± 16.45	t = -3.97	< 0.001	< 0.001
tmt_time	300	129.24 ± 64.98	132.81 ± 66.77	122.81 ± 61.40	t = 1.31	= 0.192	ns
verbal_fluency	300	14.98 ± 4.63	15.08 ± 4.44	14.79 ± 4.97	t = 0.52	= 0.606	ns
bdi	300	13.64 ± 9.26	15.07 ± 8.97	11.05 ± 9.26	t = 3.65	< 0.001	= 0.001
panas_neg	300	20.98 ± 7.48	22.50 ± 7.27	18.25 ± 7.09	t = 4.92	< 0.001	< 0.001
life_satisfaction	300	18.02 ± 4.23	17.40 ± 4.25	19.14 ± 3.99	t = -3.54	< 0.001	= 0.001
p (FDR): Benjamini-Hochberg false discovery rate correction applied.

Disable the replacement to always show the numeric FDR value:

tbl_descriptive(clerk_example, group = sex, fdr = TRUE, output = "gt",
                fdr_ns = FALSE) |>
  clerk_render(title = "FDR numeric values always shown")

	n	Overall	Female	Male	Statistic	p	p (FDR)
FDR numeric values always shown

age	300	46.76 ± 14.15	46.42 ± 13.63	47.35 ± 15.09	t = -0.53	= 0.599	= 0.667
hdl	300	46.81 ± 10.11	48.32 ± 9.78	44.08 ± 10.18	t = 3.50	< 0.001	= 0.001
glucose	300	91.68 ± 24.65	92.61 ± 24.49	90.01 ± 24.96	t = 0.87	= 0.386	= 0.531
bmi	300	26.15 ± 4.91	26.43 ± 4.84	25.64 ± 5.01	t = 1.32	= 0.190	= 0.301
waist	300	92.07 ± 11.56	92.14 ± 11.32	91.93 ± 12.03	t = 0.15	= 0.879	= 0.879
systolic_bp	300	123.25 ± 17.77	120.36 ± 17.86	128.48 ± 16.45	t = -3.97	< 0.001	< 0.001
tmt_time	300	129.24 ± 64.98	132.81 ± 66.77	122.81 ± 61.40	t = 1.31	= 0.192	= 0.301
verbal_fluency	300	14.98 ± 4.63	15.08 ± 4.44	14.79 ± 4.97	t = 0.52	= 0.606	= 0.667
bdi	300	13.64 ± 9.26	15.07 ± 8.97	11.05 ± 9.26	t = 3.65	< 0.001	= 0.001
panas_neg	300	20.98 ± 7.48	22.50 ± 7.27	18.25 ± 7.09	t = 4.92	< 0.001	< 0.001
life_satisfaction	300	18.02 ± 4.23	17.40 ± 4.25	19.14 ± 3.99	t = -3.54	< 0.001	= 0.001
p (FDR): Benjamini-Hochberg false discovery rate correction applied.

Use a stricter survival threshold:

tbl_correlation(clerk_cor_example, fdr = TRUE, output = "gt",
                fdr_alpha = 0.01) |>
  clerk_render(title = "FDR survival at \u03b1 = 0.01")

	Outcome	r	p	p (FDR)
FDR survival at α = 0.01

hdl	tmt_time	+0.190	= 0.008	ns
glucose	tmt_time	-0.229	= 0.398	ns
bmi	tmt_time	-0.086	= 0.013	ns
waist	tmt_time	+0.279	= 0.002	ns
systolic_bp	tmt_time	+0.195	= 0.379	ns
bdi	tmt_time	+0.096	= 0.109	ns
panas_neg	tmt_time	+0.157	= 0.006	ns
life_satisfaction	tmt_time	+0.247	= 0.117	ns
hdl	verbal_fluency	+0.078	= 0.072	ns
glucose	verbal_fluency	+0.216	= 0.003	ns
bmi	verbal_fluency	+0.022	= 0.207	ns
waist	verbal_fluency	+0.047	= 0.185	ns
systolic_bp	verbal_fluency	-0.105	= 0.077	ns
bdi	verbal_fluency	-0.176	= 0.070	ns
panas_neg	verbal_fluency	+0.232	= 0.117	ns
life_satisfaction	verbal_fluency	+0.201	= 0.234	ns
p (FDR): Benjamini-Hochberg false discovery rate correction applied.

Domain labels for unassigned variables

When a domains list is supplied, variables not assigned to any domain receive a blank section header by default (domain_other = ""). Set it to a string to collect them under a named section:

tbl_descriptive(
  clerk_example,
  group        = sex,
  domains      = list("Metabolic" = c("hdl", "glucose", "bmi")),
  domain_other = "Other",
  output       = "gt"
) |>
  clerk_render(title = "Unassigned variables under 'Other'")

	n	Overall	Female	Male	Statistic	p
Unassigned variables under 'Other'
Metabolic
hdl	300	46.81 ± 10.11	48.32 ± 9.78	44.08 ± 10.18	t = 3.50	< 0.001
glucose	300	91.68 ± 24.65	92.61 ± 24.49	90.01 ± 24.96	t = 0.87	= 0.386
bmi	300	26.15 ± 4.91	26.43 ± 4.84	25.64 ± 5.01	t = 1.32	= 0.190
Other
age	300	46.76 ± 14.15	46.42 ± 13.63	47.35 ± 15.09	t = -0.53	= 0.599
waist	300	92.07 ± 11.56	92.14 ± 11.32	91.93 ± 12.03	t = 0.15	= 0.879
systolic_bp	300	123.25 ± 17.77	120.36 ± 17.86	128.48 ± 16.45	t = -3.97	< 0.001
tmt_time	300	129.24 ± 64.98	132.81 ± 66.77	122.81 ± 61.40	t = 1.31	= 0.192
verbal_fluency	300	14.98 ± 4.63	15.08 ± 4.44	14.79 ± 4.97	t = 0.52	= 0.606
bdi	300	13.64 ± 9.26	15.07 ± 8.97	11.05 ± 9.26	t = 3.65	< 0.001
panas_neg	300	20.98 ± 7.48	22.50 ± 7.27	18.25 ± 7.09	t = 4.92	< 0.001
life_satisfaction	300	18.02 ± 4.23	17.40 ± 4.25	19.14 ± 3.99	t = -3.54	< 0.001

Nested domains (sub-sections within a domain)

A domains entry can itself be a named list instead of a plain variable vector. This is useful when one domain naturally splits into repeated sub-sections — the most common case being the same measures collected at multiple timepoints. Here’s a small synthetic longitudinal example: three mental health measures, each collected at baseline and two follow-ups.

set.seed(1)
longitudinal_example <- data.frame(
  sex               = clerk_example$sex,
  bdi_bl            = clerk_example$bdi,
  panas_neg_bl      = clerk_example$panas_neg,
  life_sat_bl       = clerk_example$life_satisfaction,
  bdi_fu1           = clerk_example$bdi       + rnorm(300, -1, 3),
  panas_neg_fu1     = clerk_example$panas_neg + rnorm(300, -1, 3),
  life_sat_fu1      = clerk_example$life_satisfaction + rnorm(300, 1, 3),
  bdi_fu2           = clerk_example$bdi       + rnorm(300, -2, 3),
  panas_neg_fu2     = clerk_example$panas_neg + rnorm(300, -2, 3),
  life_sat_fu2      = clerk_example$life_satisfaction + rnorm(300, 2, 3)
)

Nest the three timepoints inside a single "Mental health" domain by supplying a named list instead of a character vector for that entry. Flat and nested entries can be mixed freely in the same domains list:

tbl_descriptive(
  longitudinal_example,
  group   = sex,
  domains = list(
    "Mental health" = list(
      "Baseline"    = c("bdi_bl",  "panas_neg_bl",  "life_sat_bl"),
      "Follow-up 1" = c("bdi_fu1", "panas_neg_fu1", "life_sat_fu1"),
      "Follow-up 2" = c("bdi_fu2", "panas_neg_fu2", "life_sat_fu2")
    )
  ),
  output = "gt"
) |>
  clerk_render(title = "Mental health by timepoint")

	n	Overall	Female	Male	Statistic	p
Mental health by timepoint
Mental health — Baseline
bdi_bl	300	13.64 ± 9.26	15.07 ± 8.97	11.05 ± 9.26	t = 3.65	< 0.001
panas_neg_bl	300	20.98 ± 7.48	22.50 ± 7.27	18.25 ± 7.09	t = 4.92	< 0.001
life_sat_bl	300	18.02 ± 4.23	17.40 ± 4.25	19.14 ± 3.99	t = -3.54	< 0.001
Mental health — Follow-up 1
bdi_fu1	300	12.74 ± 9.64	13.91 ± 9.34	10.62 ± 9.86	t = 2.81	= 0.005
panas_neg_fu1	300	19.95 ± 8.40	21.50 ± 8.28	17.15 ± 7.91	t = 4.49	< 0.001
life_sat_fu1	300	18.83 ± 5.45	18.34 ± 5.48	19.72 ± 5.31	t = -2.14	= 0.034
Mental health — Follow-up 2
bdi_fu2	300	11.48 ± 9.85	12.97 ± 9.80	8.79 ± 9.41	t = 3.63	< 0.001
panas_neg_fu2	300	19.13 ± 8.20	20.60 ± 8.14	16.48 ± 7.65	t = 4.37	< 0.001
life_sat_fu2	300	19.82 ± 5.20	19.24 ± 5.35	20.86 ± 4.78	t = -2.71	= 0.007

A gt/Word/PDF/LaTeX limitation, not a clerkR one: gt has no native support for two-level row-group headers (there’s a long-standing open feature request upstream for it). So for output = "gt" and output = "latex", a nested domain renders as one row group per sub-domain, labelled with a compound "Domain — Subdomain" string — you get the grouping, but not a shared header spanning all three sub-domains.

If you need the domain and its sub-domains to render as a genuine expandable/collapsible two-level hierarchy, use output = "html" instead. reactable does support real nested grouping, so the same domains list produces an actual tree — click “Mental health” to expand into its three timepoints:

tbl_descriptive(
  longitudinal_example,
  group   = sex,
  domains = list(
    "Mental health" = list(
      "Baseline"    = c("bdi_bl",  "panas_neg_bl",  "life_sat_bl"),
      "Follow-up 1" = c("bdi_fu1", "panas_neg_fu1", "life_sat_fu1"),
      "Follow-up 2" = c("bdi_fu2", "panas_neg_fu2", "life_sat_fu2")
    )
  ),
  output = "html"
) |>
  clerk_render(title = "Mental health by timepoint (expandable)")

Mental health by timepoint (expandable)

Row-group order always follows the order you write domains in — top to bottom, then sub-domain top to bottom within each — regardless of what order the variables happen to appear in the underlying data.

Custom footnotes

Two automatic footnotes already appear without you asking for them: a log-transform note for any log_vars, and an FDR source note whenever fdr = TRUE. On top of those, clerk_render() (and render_gt() / render_latex() directly) takes two further arguments for your own notes.

footnote — one or more blanket notes, shown below the whole table (pass a character vector for more than one):

tbl_descriptive(clerk_example, group = sex, output = "gt") |>
  clerk_render(
    title    = "Table 1",
    footnote = c(
      "Data collected 2024-2025.",
      "Missing data (< 5% per variable) excluded listwise."
    )
  )

	n	Overall	Female	Male	Statistic	p
Table 1

age	300	46.76 ± 14.15	46.42 ± 13.63	47.35 ± 15.09	t = -0.53	= 0.599
hdl	300	46.81 ± 10.11	48.32 ± 9.78	44.08 ± 10.18	t = 3.50	< 0.001
glucose	300	91.68 ± 24.65	92.61 ± 24.49	90.01 ± 24.96	t = 0.87	= 0.386
bmi	300	26.15 ± 4.91	26.43 ± 4.84	25.64 ± 5.01	t = 1.32	= 0.190
waist	300	92.07 ± 11.56	92.14 ± 11.32	91.93 ± 12.03	t = 0.15	= 0.879
systolic_bp	300	123.25 ± 17.77	120.36 ± 17.86	128.48 ± 16.45	t = -3.97	< 0.001
tmt_time	300	129.24 ± 64.98	132.81 ± 66.77	122.81 ± 61.40	t = 1.31	= 0.192
verbal_fluency	300	14.98 ± 4.63	15.08 ± 4.44	14.79 ± 4.97	t = 0.52	= 0.606
bdi	300	13.64 ± 9.26	15.07 ± 8.97	11.05 ± 9.26	t = 3.65	< 0.001
panas_neg	300	20.98 ± 7.48	22.50 ± 7.27	18.25 ± 7.09	t = 4.92	< 0.001
life_satisfaction	300	18.02 ± 4.23	17.40 ± 4.25	19.14 ± 3.99	t = -3.54	< 0.001
Data collected 2024-2025.
Missing data (< 5% per variable) excluded listwise.

footnotes — targeted notes attached to specific rows or columns, rather than the whole table. Each element is a list with a text string and either rows (variable names, matched against the table stub) or cols (column names):

tbl_descriptive(clerk_example, group = sex, output = "gt") |>
  clerk_render(
    title = "Table 1",
    footnotes = list(
      list(text = "Assessed via self-report questionnaire.",
           rows = c("bdi", "panas_neg")),
      list(text = "Age- and sex-adjusted.", cols = "overall")
    )
  )

	n	Overall¹	Female	Male	Statistic	p
Table 1

age	300	46.76 ± 14.15	46.42 ± 13.63	47.35 ± 15.09	t = -0.53	= 0.599
hdl	300	46.81 ± 10.11	48.32 ± 9.78	44.08 ± 10.18	t = 3.50	< 0.001
glucose	300	91.68 ± 24.65	92.61 ± 24.49	90.01 ± 24.96	t = 0.87	= 0.386
bmi	300	26.15 ± 4.91	26.43 ± 4.84	25.64 ± 5.01	t = 1.32	= 0.190
waist	300	92.07 ± 11.56	92.14 ± 11.32	91.93 ± 12.03	t = 0.15	= 0.879
systolic_bp	300	123.25 ± 17.77	120.36 ± 17.86	128.48 ± 16.45	t = -3.97	< 0.001
tmt_time	300	129.24 ± 64.98	132.81 ± 66.77	122.81 ± 61.40	t = 1.31	= 0.192
verbal_fluency	300	14.98 ± 4.63	15.08 ± 4.44	14.79 ± 4.97	t = 0.52	= 0.606
bdi²	300	13.64 ± 9.26	15.07 ± 8.97	11.05 ± 9.26	t = 3.65	< 0.001
panas_neg²	300	20.98 ± 7.48	22.50 ± 7.27	18.25 ± 7.09	t = 4.92	< 0.001
life_satisfaction	300	18.02 ± 4.23	17.40 ± 4.25	19.14 ± 3.99	t = -3.54	< 0.001
¹ Age- and sex-adjusted.
² Assessed via self-report questionnaire.

footnote and footnotes can be combined freely, and both stack with the automatic log-transform/FDR notes rather than replacing them.

For output = "html", reactable has no per-cell footnote-marker equivalent, so both footnote and any footnotes[[i]]$text are shown together as a plain note list underneath the widget rather than attached to specific cells.

Session-wide vs per-call options

Options set via clerk_options() apply to all subsequent tables in the session. Arguments passed directly to a tbl_* function override the session default for that call only:

# Change session default to plain style
clerk_options(p_style = "plain")

# This table uses plain (from session)
tbl_descriptive(clerk_example, group = sex, output = "gt") |>
  clerk_render(title = "Session default: plain")

	n	Overall	Female	Male	Statistic	p
Session default: plain

age	300	46.76 ± 14.15	46.42 ± 13.63	47.35 ± 15.09	t = -0.53	0.599
hdl	300	46.81 ± 10.11	48.32 ± 9.78	44.08 ± 10.18	t = 3.50	< 0.001
glucose	300	91.68 ± 24.65	92.61 ± 24.49	90.01 ± 24.96	t = 0.87	0.386
bmi	300	26.15 ± 4.91	26.43 ± 4.84	25.64 ± 5.01	t = 1.32	0.190
waist	300	92.07 ± 11.56	92.14 ± 11.32	91.93 ± 12.03	t = 0.15	0.879
systolic_bp	300	123.25 ± 17.77	120.36 ± 17.86	128.48 ± 16.45	t = -3.97	< 0.001
tmt_time	300	129.24 ± 64.98	132.81 ± 66.77	122.81 ± 61.40	t = 1.31	0.192
verbal_fluency	300	14.98 ± 4.63	15.08 ± 4.44	14.79 ± 4.97	t = 0.52	0.606
bdi	300	13.64 ± 9.26	15.07 ± 8.97	11.05 ± 9.26	t = 3.65	< 0.001
panas_neg	300	20.98 ± 7.48	22.50 ± 7.27	18.25 ± 7.09	t = 4.92	< 0.001
life_satisfaction	300	18.02 ± 4.23	17.40 ± 4.25	19.14 ± 3.99	t = -3.54	< 0.001


# This table overrides back to APA for this call only
tbl_descriptive(clerk_example, group = sex, output = "gt",
                p_style = "apa") |>
  clerk_render(title = "Per-call override: apa")

	n	Overall	Female	Male	Statistic	p
Per-call override: apa

age	300	46.76 ± 14.15	46.42 ± 13.63	47.35 ± 15.09	t = -0.53	= 0.599
hdl	300	46.81 ± 10.11	48.32 ± 9.78	44.08 ± 10.18	t = 3.50	< 0.001
glucose	300	91.68 ± 24.65	92.61 ± 24.49	90.01 ± 24.96	t = 0.87	= 0.386
bmi	300	26.15 ± 4.91	26.43 ± 4.84	25.64 ± 5.01	t = 1.32	= 0.190
waist	300	92.07 ± 11.56	92.14 ± 11.32	91.93 ± 12.03	t = 0.15	= 0.879
systolic_bp	300	123.25 ± 17.77	120.36 ± 17.86	128.48 ± 16.45	t = -3.97	< 0.001
tmt_time	300	129.24 ± 64.98	132.81 ± 66.77	122.81 ± 61.40	t = 1.31	= 0.192
verbal_fluency	300	14.98 ± 4.63	15.08 ± 4.44	14.79 ± 4.97	t = 0.52	= 0.606
bdi	300	13.64 ± 9.26	15.07 ± 8.97	11.05 ± 9.26	t = 3.65	< 0.001
panas_neg	300	20.98 ± 7.48	22.50 ± 7.27	18.25 ± 7.09	t = 4.92	< 0.001
life_satisfaction	300	18.02 ± 4.23	17.40 ± 4.25	19.14 ± 3.99	t = -3.54	< 0.001


# Reset to factory defaults
clerk_options(reset = TRUE)

Full option reference

clerk_options(
  digits          = 2,        # continuous stats decimal places
  r_digits        = 3,        # correlations / h² decimal places
  p_digits        = 3,        # p-value decimal places
  p_threshold     = 0.001,    # below this → "< 0.001"
  p_style         = "apa",    # "apa" | "plain" | "stars" | "stars_p"
  stars           = FALSE,    # append * ** ***
  star_thresholds = c(0.05, 0.01, 0.001),
  fdr_ns          = TRUE,     # replace non-surviving FDR with label
  fdr_alpha       = 0.05,     # applied to BH-adjusted p, not raw p
  fdr_ns_label    = "ns",     # label for non-surviving FDR cells
  domain_other    = ""        # label for unassigned variables
)