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Themes for gtsummary

Source: R/gtsummary_themes.R
gtsummary_themes.Rd

Additional themes for tables generated with gtsummary.

Usage

theme_gtsummary_prop_n(
  prop_stat = "{p}% ({n})",
  prop_digits = 1,
  mean_sd = FALSE,
  cont_digits = 1,
  set_theme = TRUE
)

theme_gtsummary_fisher_simulate_p(set_theme = TRUE)

theme_gtsummary_unweighted_n(
  n_unweighted_prefix = "",
  n_unweighted_suffix = " obs.",
  prop_digits = 1,
  mean_sd = FALSE,
  cont_digits = 1,
  overall_string = NULL,
  set_theme = TRUE
)

theme_gtsummary_bold_labels(set_theme = TRUE)

Arguments

prop_stat

(character)
Statistics to display for categorical variables (see gtsummary::tbl_summary()).

prop_digits

(non-negative integer)
Define the number of decimals to display for proportions.

mean_sd

(scalar logical)
Also, set default summary statistics to mean and standard deviation in gtsummary::tbl_summary(). Default is FALSE.

cont_digits

(non-negative integer)
Define the number of decimals to display for continuous variables.

set_theme

(scalar logical)
Logical indicating whether to set the theme. Default is TRUE. When FALSE the named list of theme elements is returned invisibly

n_unweighted_prefix, n_unweighted_suffix

(character)
Prefix and suffix displayed before and after the unweighted number of observations.

overall_string

(character)
Optional string to name the overall column.

Details

theme_gtsummary_prop_n() displays, by default, proportions before the number of observations (between brackets). This function cannot be used simultaneously with gtsummary::theme_gtsummary_mean_sd(), but you can use the mean_sd = TRUE option of theme_gtsummary_prop_n(). theme_gtsummary_prop_n() also modifies default method for gtsummary::add_ci.tbl_summary() ("wilson" for categorical variables, "t.test", i.e. mean confidence interval, for continuous variables if mean_sd = TRUE, "wilcox.test", i.e. confidence interval of the pseudomedian, for continuous variables if mean_sd = FALSE). Finally, theme_gtsummary_prop_n() also modifies default tests for gtsummary::add_p.tbl_summary() for continuous variables if mean_sd = TRUE ("t.test" for comparing 2 groups, or "oneway.test" for 3 groups or more). If mean_sd = FALSE, the default tests for continuous variables remain "wilcox.test" (2 groups) or "kruskal.test" (3 groups or more). For categorical variables, "chisq.test.no.correct" and "fisher.test" are used by default. See theme_gtsummary_fisher_simulate_p() to change the default test for categorical variables.

theme_gtsummary_fisher_simulate_p() modify the default test used for categorical variables by Fisher test, with computation of p-values by Monte Carlo simulation in larger than 2×2 tables.

theme_gtsummary_unweighted_n() modifies default values of tables returned by gtsummary::tbl_svysummary() and displays the unweighted number of observations instead of the weighted n. theme_gtsummary_unweighted_n() also modifies default method for gtsummary::add_ci.tbl_svysummary() ("svyprop.logit" for categorical variables, "svymean", i.e. mean confidence interval, for continuous variables if mean_sd = TRUE, "svymedian.mean", i.e. confidence interval of the median, for continuous variables if mean_sd = FALSE). Finally, theme_gtsummary_unweighted_n() also modifies default tests for gtsummary::add_p.tbl_svysummary() for continuous variables if mean_sd = TRUE (svyttest_oneway which calls survey::svyttest() for comparing 2 means and svyoneway() for comparing 3 means or more). If mean_sd = FALSE, the default tests for continuous variables remain "svy.wilcox.test" which used a designed-based Wilcoxon test (2 groups) or Kruskal-Wallis test (3 groups or more). For categorical variables, "svy.chisq.test"is used by default.

theme_gtsummary_bold_labels() applies automatically gtsummary::bold_labels() to all tables generated with gtsummary.

Examples

# \donttest{
library(gtsummary)

trial |>
  tbl_summary(include = c(grade, age), by = trt) |>
  add_p()



  
Characteristic
      Drug A

N = 981		
      Drug B

N = 1021		
      p-value2
    
Grade




0.9
    I
35 (36%)
33 (32%)

    II
32 (33%)
36 (35%)

    III
31 (32%)
33 (32%)

Age
46 (37, 60)
48 (39, 56)
0.7
    Unknown
7
4

1 n (%); Median (Q1, Q3)
    
2 Pearson’s Chi-squared test; Wilcoxon rank sum test
    

theme_gtsummary_prop_n(mean_sd = TRUE)
theme_gtsummary_fisher_simulate_p()
theme_gtsummary_bold_labels()
trial |>
  tbl_summary(include = c(grade, age), by = trt) |>
  add_p()



  
Characteristic
      Drug A

N = 981		
      Drug B

N = 1021		
      p-value2
    
Grade




>0.9
    I
35.7% (35)
32.4% (33)

    II
32.7% (32)
35.3% (36)

    III
31.6% (31)
32.4% (33)

Age
47.0 (14.7)
47.4 (14.0)
0.8
    Unknown
7
4

1 % (n); Mean (SD)
    
2 Fisher’s Exact Test for Count Data with simulated p-value
(based on 2000 replicates); Welch Two Sample t-test
    
# }
# \donttest{
data("api", package = "survey")
apistrat$both[1:5] <- NA
apistrat |>
  srvyr::as_survey(strata = stype, weights = pw) |>
  tbl_svysummary(include = c(stype, both), by = awards) |>
  add_overall()



  
Characteristic
      Overall

N = 6,1941		
      No

N = 2,2361		
      Yes

N = 3,9581		
    
stype





    E
71.4% (4,421)
53.4% (1,194)
81.5% (3,227)
    H
12.2% (755)
23.0% (513)
6.1% (242)
    M
16.4% (1,018)
23.7% (529)
12.3% (489)
both
64.0% (3,825)
0.0% (0)
100.0% (3,825)
    Unknown
221
88
133
1 % (n)
    

theme_gtsummary_unweighted_n()
apistrat |>
  srvyr::as_survey(strata = stype, weights = pw) |>
  tbl_svysummary(include = c(stype, both), by = awards) |>
  add_overall()



  
Characteristic
      Overall
(200 obs.)1
      No
(87 obs.)1
      Yes
(113 obs.)1
    
stype





    E
71.4% (100 obs.)
53.4% (27 obs.)
81.5% (73 obs.)
    H
12.2% (50 obs.)
23.0% (34 obs.)
6.1% (16 obs.)
    M
16.4% (50 obs.)
23.7% (26 obs.)
12.3% (24 obs.)
both
64.0% (110 obs.)
0.0% (0 obs.)
100.0% (110 obs.)
    Unknown
5 obs.
2 obs.
3 obs.
1 % (n (unweighted) obs.)
    
# }

gtsummary::reset_gtsummary_theme()