Usage guidance

Introduction

DescrTab2 is the replacement of the DescrTab package. It supports a variety of different customization options and can be used in .Rmd files in conjunction with knitr.

Preamble settings

DescrTab2 works in your R-console, as well as in .Rmd documents corresponding to output formats of the type pdf_documument, html_document and word_document. It even supports YAML-headers with multiple output formats! For example, if your YAML-header looks like the example below, DescrTab2 should automagically detect the output format depending on the rendering option you choose from the dropdown menue (the arrow next to the “Knit” button on the top menue bar).

---
title: "DescrTab2 tutorial"
output:
  word_document: default
  pdf_document: default
  html_document: default
---

Required LaTeX packages should be loaded automatically as well when rendering as a pdf.

Getting started

Make sure you include the DescrTab2 library by typing

library(DescrTab2)

somewhere in the document before you use it. You are now ready to go!

We will use two tidyverse libraries for data manipulation and a the following dataset for instructive purposes:

library(dplyr, warn.conflicts = FALSE)
library(forcats)
set.seed(123)
dat <- iris[, c("Species", "Sepal.Length")] %>%
  mutate(animal = c("Mammal", "Fish") %>% rep(75) %>% factor()) %>%
  mutate(food = c("fries", "wedges") %>% sample(150, TRUE) %>% factor())
head(dat)
#>   Species Sepal.Length animal   food
#> 1  setosa          5.1 Mammal  fries
#> 2  setosa          4.9   Fish  fries
#> 3  setosa          4.7 Mammal  fries
#> 4  setosa          4.6   Fish wedges
#> 5  setosa          5.0 Mammal  fries
#> 6  setosa          5.4   Fish wedges

Producing beautiful descriptive tables is now as easy as typing:

descr(dat)

Variables	Total	p
	(N=150)
Species
setosa	50 (33%)	>0.999^chi1
versicolor	50 (33%)
virginica	50 (33%)
Sepal.Length
N	150	<0.001^tt1
mean	5.8
sd	0.83
median	5.8
Q1 - Q3	5.1 – 6.4
min - max	4.3 – 7.9
animal
Fish	75 (50%)	>0.999^chi1
Mammal	75 (50%)
food
fries	76 (51%)	0.870^chi1
wedges	74 (49%)
^chi1 Chi-squared goodness-of-fit test
^tt1 Student’s one-sample t-test

Accessing table elements

The object returned from the descr function is basically just a named list. You may be interested in referencing certain summary statistics from the table in your document. To do this, you can save the list returned by descr:

my_table <- descr(dat)

You can then access the elements of the list using the $ operator.

my_table$variables$Sepal.Length$results$Total$mean
#> NULL

Rstudios autocomplete suggestions are very helpful when navigating this list.

The print function returns a formatted version of this list, which you can also save and access using the same syntax.

my_table <- descr(dat) %>% print(silent=TRUE)

Specifying a group

Use the group option to specify the name of a grouping variable in your data:

descr(dat, "Species")

Variables	setosa	versicolor	virginica	Total	p
	(N=50)	(N=50)	(N=50)	(N=150)
Sepal.Length
N	50	50	50	150	<0.001^F
mean	5	5.9	6.6	5.8
sd	0.35	0.52	0.64	0.83
median	5	5.9	6.5	5.8
Q1 - Q3	4.8 – 5.2	5.6 – 6.3	6.2 – 6.9	5.1 – 6.4
min - max	4.3 – 5.8	4.9 – 7	4.9 – 7.9	4.3 – 7.9
animal
Fish	25 (50%)	25 (50%)	25 (50%)	75 (50%)	>0.999^chi2
Mammal	25 (50%)	25 (50%)	25 (50%)	75 (50%)
food
fries	30 (60%)	27 (54%)	19 (38%)	76 (51%)	0.075^chi2
wedges	20 (40%)	23 (46%)	31 (62%)	74 (49%)
^F F-test (ANOVA)
^chi2 Pearson’s chi-squared test

Assigning labels

Use the group_labels option to assign group labels and the var_labels option to assign variable labels:

descr(dat, "Species", group_labels=list(setosa="My custom group label"), var_labels = list(Sepal.Length = "My custom variable label"))

Variables	My custom group label	versicolor	virginica	Total	p
	(N=50)	(N=50)	(N=50)	(N=150)
My custom variable label
N	50	50	50	150	<0.001^F
mean	5	5.9	6.6	5.8
sd	0.35	0.52	0.64	0.83
median	5	5.9	6.5	5.8
Q1 - Q3	4.8 – 5.2	5.6 – 6.3	6.2 – 6.9	5.1 – 6.4
min - max	4.3 – 5.8	4.9 – 7	4.9 – 7.9	4.3 – 7.9
animal
Fish	25 (50%)	25 (50%)	25 (50%)	75 (50%)	>0.999^chi2
Mammal	25 (50%)	25 (50%)	25 (50%)	75 (50%)
food
fries	30 (60%)	27 (54%)	19 (38%)	76 (51%)	0.075^chi2
wedges	20 (40%)	23 (46%)	31 (62%)	74 (49%)
^F F-test (ANOVA)
^chi2 Pearson’s chi-squared test

Assigning a table caption

Use the caption member of the format_options argument to assign a table caption:

descr(dat, "Species", format_options = list(caption="Description of our example dataset."))

Description of our example dataset.
Variables	setosa	versicolor	virginica	Total	p
	(N=50)	(N=50)	(N=50)	(N=150)
Sepal.Length
N	50	50	50	150	<0.001^F
mean	5	5.9	6.6	5.8
sd	0.35	0.52	0.64	0.83
median	5	5.9	6.5	5.8
Q1 - Q3	4.8 – 5.2	5.6 – 6.3	6.2 – 6.9	5.1 – 6.4
min - max	4.3 – 5.8	4.9 – 7	4.9 – 7.9	4.3 – 7.9
animal
Fish	25 (50%)	25 (50%)	25 (50%)	75 (50%)	>0.999^chi2
Mammal	25 (50%)	25 (50%)	25 (50%)	75 (50%)
food
fries	30 (60%)	27 (54%)	19 (38%)	76 (51%)	0.075^chi2
wedges	20 (40%)	23 (46%)	31 (62%)	74 (49%)
^F F-test (ANOVA)
^chi2 Pearson’s chi-squared test

Confidence intervals for two group comparisons

For 2-group comparisons, decrtab automatically calculates confidence intervals for differences in effect measures:

descr(dat, "animal")

Variables	Fish	Mammal	Total	p	CI
	(N=75)	(N=75)	(N=150)
Species
setosa	25 (33%)	25 (33%)	50 (33%)	>0.999^chi2
versicolor	25 (33%)	25 (33%)	50 (33%)
virginica	25 (33%)	25 (33%)	50 (33%)
Sepal.Length
N	75	75	150	0.961^tt2	[-0.26, 0.27]^t
mean	5.8	5.8	5.8
sd	0.86	0.81	0.83
median	5.7	5.8	5.8
Q1 - Q3	5.1 – 6.4	5.1 – 6.5	5.1 – 6.4
min - max	4.3 – 7.9	4.4 – 7.7	4.3 – 7.9
food
fries	37 (49%)	39 (52%)	76 (51%)	0.744^chi2	[-0.19, 0.13]^PWa
wedges	38 (51%)	36 (48%)	74 (49%)
^chi2 Pearson’s chi-squared test
^tt2 Welch’s two-sample t-test
^t CI for difference in means derived from the t-distribution
^PWa CI for difference in proportions derived from a normal (“Wald”) approximation

Different tests

There are a lot of different tests available. Check out the test_choice vignette for details: https://imbi-heidelberg.github.io/DescrTab2/articles/b_test_choice_tree_pdf.pdf

Here are some different tests in action:

descr(dat %>% select(-"Species"), "animal", test_options = list(exact=TRUE, nonparametric=TRUE))

Variables	Fish	Mammal	Total	p	CI
	(N=75)	(N=75)	(N=150)
Sepal.Length
N	75	75	150	0.870^MWU	[-0.3, 0.3]^HL
mean	5.8	5.8	5.8
sd	0.86	0.81	0.83
median	5.7	5.8	5.8
Q1 - Q3	5.1 – 6.4	5.1 – 6.5	5.1 – 6.4
min - max	4.3 – 7.9	4.4 – 7.7	4.3 – 7.9
food
fries	37 (49%)	39 (52%)	76 (51%)	0.803^Bolo	[-0.14, 0.18]^PUnc
wedges	38 (51%)	36 (48%)	74 (49%)
^MWU Mann-Whitney’s U test
^Bolo Boschloo’s test
^HL CI for the Hodges-Lehmann estimator
^PUnc CI for difference in proportions derived from an unconditional exact test

descr(dat %>% select(c("Species", "Sepal.Length")), "Species", test_options = list(nonparametric=TRUE))

Variables	setosa	versicolor	virginica	Total	p
	(N=50)	(N=50)	(N=50)	(N=150)
Sepal.Length
N	50	50	50	150	<0.001^KW
mean	5	5.9	6.6	5.8
sd	0.35	0.52	0.64	0.83
median	5	5.9	6.5	5.8
Q1 - Q3	4.8 – 5.2	5.6 – 6.3	6.2 – 6.9	5.1 – 6.4
min - max	4.3 – 5.8	4.9 – 7	4.9 – 7.9	4.3 – 7.9
^KW Kruskal-Wallis’s one-way ANOVA

Paired observations

In situations with paired data, the group variable usually denotes the timing of the measurement (e.g. “before” and “after” or “time 1”, “time 2”, etc.). In these scenarios, you need an additional index variable that specifies which observations from the different timepoints should be paired. The test_options =list(paired=TRUE, indices = <Character name of index variable name or vector of indices>) option can be used to specify the pairing indices, see the example below. DescrTab2 only works with data in “long format”, see e.g. ?reshape or ?tidyr::pivot_longer for information on how to transoform your data from wide to long format.

descr(dat %>% mutate(animal = fct_recode(animal, Before="Fish", After="Mammal")) %>% select(-"Species"), "animal", test_options = list(paired=TRUE, indices=rep(1:75, each=2)))
#> You specified paired tests and did not explicitly
#> specify format_options$print_Total. print_Total is set to FALSE.
#> Warning in sig_test(var, group, test_options, test_override, var_name): Confidence intervals for differences in proportions ignore the paired structure of the data.
#> Use Exact McNemar's test if you want confidence intervals which use the test statistic of the
#> exact McNemar's test.

Variables	Before	After	p	CI
	(N=75)	(N=75)
Sepal.Length
N	75	75	0.937^tpar	[-0.16, 0.18]^t
mean	5.8	5.8
sd	0.86	0.81
median	5.7	5.8
Q1 - Q3	5.1 – 6.4	5.1 – 6.5
min - max	4.3 – 7.9	4.4 – 7.7
food
fries	37 (49%)	39 (52%)	0.877^McN	[-0.19, 0.13]^PWa
wedges	38 (51%)	36 (48%)
^tpar Student’s paired t-test
^McN McNemar’s test
^t CI for difference in means derived from the t-distribution
^PWa CI for difference in proportions derived from a normal (“Wald”) approximation


descr(dat %>% mutate(animal = fct_recode(animal, Before="Fish", After="Mammal"), idx = rep(1:75, each=2)) %>% select(-"Species"), "animal", test_options = list(paired=TRUE, indices="idx" ))
#> You specified paired tests and did not explicitly
#> specify format_options$print_Total. print_Total is set to FALSE.
#> Warning in sig_test(var, group, test_options, test_override, var_name): Confidence intervals for differences in proportions ignore the paired structure of the data.
#> Use Exact McNemar's test if you want confidence intervals which use the test statistic of the
#> exact McNemar's test.

Variables	Before	After	p	CI
	(N=75)	(N=75)
Sepal.Length
N	75	75	0.937^tpar	[-0.16, 0.18]^t
mean	5.8	5.8
sd	0.86	0.81
median	5.7	5.8
Q1 - Q3	5.1 – 6.4	5.1 – 6.5
min - max	4.3 – 7.9	4.4 – 7.7
food
fries	37 (49%)	39 (52%)	0.877^McN	[-0.19, 0.13]^PWa
wedges	38 (51%)	36 (48%)
^tpar Student’s paired t-test
^McN McNemar’s test
^t CI for difference in means derived from the t-distribution
^PWa CI for difference in proportions derived from a normal (“Wald”) approximation

Significant digits

Every summary statistic in DescrTab2 is formatted by a corresponding formatting function. You can exchange these formatting functions as you please:

descr(dat, "Species", format_summary_stats = list(mean=function(x)formatC(x, digits = 4)) )

Variables	setosa	versicolor	virginica	Total	p
	(N=50)	(N=50)	(N=50)	(N=150)
Sepal.Length
N	50	50	50	150	<0.001^F
mean	5.006	5.936	6.588	5.843
sd	0.35	0.52	0.64	0.83
median	5	5.9	6.5	5.8
Q1 - Q3	4.8 – 5.2	5.6 – 6.3	6.2 – 6.9	5.1 – 6.4
min - max	4.3 – 5.8	4.9 – 7	4.9 – 7.9	4.3 – 7.9
animal
Fish	25 (50%)	25 (50%)	25 (50%)	75 (50%)	>0.999^chi2
Mammal	25 (50%)	25 (50%)	25 (50%)	75 (50%)
food
fries	30 (60%)	27 (54%)	19 (38%)	76 (51%)	0.075^chi2
wedges	20 (40%)	23 (46%)	31 (62%)	74 (49%)
^F F-test (ANOVA)
^chi2 Pearson’s chi-squared test

Omitting summary statistics

Let’s say you don’t want to calculate quantiles for your numeric variables. You can specify the summary_stats_cont option to include all summary statistics but quantiles:

descr(dat, "Species", summary_stats_cont = list(N = DescrTab2:::.N, Nmiss = DescrTab2:::.Nmiss, mean =
    DescrTab2:::.mean, sd = DescrTab2:::.sd, median = DescrTab2:::.median, min = DescrTab2:::.min, max =
    DescrTab2:::.max))

Variables	setosa	versicolor	virginica	Total	p
	(N=50)	(N=50)	(N=50)	(N=150)
Sepal.Length
N	50	50	50	150	<0.001^F
mean	5	5.9	6.6	5.8
sd	0.35	0.52	0.64	0.83
median	5	5.9	6.5	5.8
min - max	4.3 – 5.8	4.9 – 7	4.9 – 7.9	4.3 – 7.9
animal
Fish	25 (50%)	25 (50%)	25 (50%)	75 (50%)	>0.999^chi2
Mammal	25 (50%)	25 (50%)	25 (50%)	75 (50%)
food
fries	30 (60%)	27 (54%)	19 (38%)	76 (51%)	0.075^chi2
wedges	20 (40%)	23 (46%)	31 (62%)	74 (49%)
^F F-test (ANOVA)
^chi2 Pearson’s chi-squared test

Adding summary statistics

Let’s say you have a categorical variable, but for some reason it’s levels are numerals and you want to calculate the mean. No problem:

# Create example dataset
dat2 <- iris
dat2$cat_var <- c(1,2) %>% sample(150, TRUE) %>% factor()
dat2 <- dat2[, c("Species", "cat_var")]

descr(dat2, "Species", summary_stats_cat=list(mean=DescrTab2:::.factormean))

Variables	setosa	versicolor	virginica	Total	p
	(N=50)	(N=50)	(N=50)	(N=150)
cat_var
mean	1.5	1.6	1.4	1.5	0.083^chi2
1	27 (54%)	20 (40%)	31 (62%)	78 (52%)
2	23 (46%)	30 (60%)	19 (38%)	72 (48%)
^chi2 Pearson’s chi-squared test

Combining mean and sd

Use the format_options = list(combine_mean_sd=TRUE) option:

descr(dat, "Species", format_options = c(combine_mean_sd=TRUE))

Variables	setosa	versicolor	virginica	Total	p
	(N=50)	(N=50)	(N=50)	(N=150)
Sepal.Length
N	50	50	50	150	<0.001^F
mean ± sd	5 ± 0.35	5.9 ± 0.52	6.6 ± 0.64	5.8 ± 0.83
median	5	5.9	6.5	5.8
Q1 - Q3	4.8 – 5.2	5.6 – 6.3	6.2 – 6.9	5.1 – 6.4
min - max	4.3 – 5.8	4.9 – 7	4.9 – 7.9	4.3 – 7.9
animal
Fish	25 (50%)	25 (50%)	25 (50%)	75 (50%)	>0.999^chi2
Mammal	25 (50%)	25 (50%)	25 (50%)	75 (50%)
food
fries	30 (60%)	27 (54%)	19 (38%)	76 (51%)	0.075^chi2
wedges	20 (40%)	23 (46%)	31 (62%)	74 (49%)
^F F-test (ANOVA)
^chi2 Pearson’s chi-squared test

Omitting p-values

You can declare the format_options = list(print_p = FALSE) option to omit p-values:

descr(dat, "animal", format_options = list(print_p = FALSE))

Variables	Fish	Mammal	Total	CI
	(N=75)	(N=75)	(N=150)
Species
setosa	25 (33%)	25 (33%)	50 (33%)
versicolor	25 (33%)	25 (33%)	50 (33%)
virginica	25 (33%)	25 (33%)	50 (33%)
Sepal.Length
N	75	75	150	[-0.26, 0.27]^t
mean	5.8	5.8	5.8
sd	0.86	0.81	0.83
median	5.7	5.8	5.8
Q1 - Q3	5.1 – 6.4	5.1 – 6.5	5.1 – 6.4
min - max	4.3 – 7.9	4.4 – 7.7	4.3 – 7.9
food
fries	37 (49%)	39 (52%)	76 (51%)	[-0.19, 0.13]^PWa
wedges	38 (51%)	36 (48%)	74 (49%)
^t CI for difference in means derived from the t-distribution
^PWa CI for difference in proportions derived from a normal (“Wald”) approximation

Similarily for Confidence intervals:

descr(dat, "animal", format_options = list(print_CI = FALSE))

Variables	Fish	Mammal	Total	p
	(N=75)	(N=75)	(N=150)
Species
setosa	25 (33%)	25 (33%)	50 (33%)	>0.999^chi2
versicolor	25 (33%)	25 (33%)	50 (33%)
virginica	25 (33%)	25 (33%)	50 (33%)
Sepal.Length
N	75	75	150	0.961^tt2
mean	5.8	5.8	5.8
sd	0.86	0.81	0.83
median	5.7	5.8	5.8
Q1 - Q3	5.1 – 6.4	5.1 – 6.5	5.1 – 6.4
min - max	4.3 – 7.9	4.4 – 7.7	4.3 – 7.9
food
fries	37 (49%)	39 (52%)	76 (51%)	0.744^chi2
wedges	38 (51%)	36 (48%)	74 (49%)
^chi2 Pearson’s chi-squared test
^tt2 Welch’s two-sample t-test

Controling options on a per-variable level

You can use the var_options list to control formatting and test options on a per-variable basis. Let’s say in the dataset iris, we want that only the Sepal.Length variable has more digits in the mean and a nonparametric test:

descr(iris, "Species", var_options = list(Sepal.Length = list(
  format_summary_stats = list(
    mean = function(x)
      formatC(x, digits = 4)
  ),
  test_options = c(nonparametric = TRUE)
)))

Variables	setosa	versicolor	virginica	Total	p
	(N=50)	(N=50)	(N=50)	(N=150)
Sepal.Length
N	50	50	50	150	<0.001^KW
mean	5.006	5.936	6.588	5.843
sd	0.35	0.52	0.64	0.83
median	5	5.9	6.5	5.8
Q1 - Q3	4.8 – 5.2	5.6 – 6.3	6.2 – 6.9	5.1 – 6.4
min - max	4.3 – 5.8	4.9 – 7	4.9 – 7.9	4.3 – 7.9
Sepal.Width
N	50	50	50	150	<0.001^F
mean	3.4	2.8	3	3.1
sd	0.38	0.31	0.32	0.44
median	3.4	2.8	3	3
Q1 - Q3	3.2 – 3.7	2.5 – 3	2.8 – 3.2	2.8 – 3.3
min - max	2.3 – 4.4	2 – 3.4	2.2 – 3.8	2 – 4.4
Petal.Length
N	50	50	50	150	<0.001^F
mean	1.5	4.3	5.6	3.8
sd	0.17	0.47	0.55	1.8
median	1.5	4.3	5.5	4.3
Q1 - Q3	1.4 – 1.6	4 – 4.6	5.1 – 5.9	1.6 – 5.1
min - max	1 – 1.9	3 – 5.1	4.5 – 6.9	1 – 6.9
Petal.Width
N	50	50	50	150	<0.001^F
mean	0.25	1.3	2	1.2
sd	0.11	0.2	0.27	0.76
median	0.2	1.3	2	1.3
Q1 - Q3	0.2 – 0.3	1.2 – 1.5	1.8 – 2.3	0.3 – 1.8
min - max	0.1 – 0.6	1 – 1.8	1.4 – 2.5	0.1 – 2.5
^KW Kruskal-Wallis’s one-way ANOVA
^F F-test (ANOVA)

Use user defined test statistics

DescrTab2 has many predefined significance tests, but sometimes you may need to use a custom test. In this case, you can use the test_override option in test_options (or as a part of per variable options, see above). To do so, test_override must be a list with at least 3 members:

the name of the test (e.g. “custom t-test”)
an abbreviation for this name (e.g. “ct”)
a function that takes the variable to be analysed and possibly a group variable and returns the p-value.

custom_ttest <- list(
  name = "custom t-test",
  abbreviation = "ct",
  p = function(var) {
    return(t.test(var, alternative = "greater")$p.value)
  }
)

descr(iris %>% select(-Species), test_options = list(test_override = custom_ttest))

Variables	Total	p
	(N=150)
Sepal.Length
N	150	<0.001^ct
mean	5.8
sd	0.83
median	5.8
Q1 - Q3	5.1 – 6.4
min - max	4.3 – 7.9
Sepal.Width
N	150	<0.001^ct
mean	3.1
sd	0.44
median	3
Q1 - Q3	2.8 – 3.3
min - max	2 – 4.4
Petal.Length
N	150	<0.001^ct
mean	3.8
sd	1.8
median	4.3
Q1 - Q3	1.6 – 5.1
min - max	1 – 6.9
Petal.Width
N	150	<0.001^ct
mean	1.2
sd	0.76
median	1.3
Q1 - Q3	0.3 – 1.8
min - max	0.1 – 2.5
^ct custom t-test

Supress the last factor level

If you have a lot of binary factors, you may want to suppress one of the factor levels to save space. A common use case for this practise is when you analyse questionaires with a great deal of “yes” / “no” items. You can do so by setting the omit_factor_level option to either "first" or "last".

descr(factor(c("a", "b")), format_options=list(omit_factor_level = "last"))
#> Warning in (function (x, y = NULL, correct = TRUE, p = rep(1/length(x), : Chi-
#> squared approximation may be incorrect

Variables	Total	p
	(N=2)
value
a	1 (50%)	>0.999^chi1
^chi1 Chi-squared goodness-of-fit test

Confidence intervals as summary statistics

Sometimes it may be a good idea to show the confidence intervals as summary statistics. To do so, you can supply appropriate summary statistics for the confidence intervals with corresponding formatting functions. DescrTab2 offers the following predefined CI functions:

DescrTab2:::.meanCIlower
DescrTab2:::.meanCIupper
DescrTab2:::.factor_firstlevel_CIlower
DescrTab2:::.factor_firstlevel_CIupper
DescrTab2:::.factor_lastlevel_CIlower
DescrTab2:::.factor_lastlevel_CIupper
DescrTab2:::.HLCIlower
DescrTab2:::.HLCIupper

summary_stats_cat <- list(
  CIL = DescrTab2:::.factor_firstlevel_CIlower,
  CIU = DescrTab2:::.factor_firstlevel_CIupper)

summary_stats_cont  <-  list(
  N = DescrTab2:::.N,
  Nmiss = DescrTab2:::.Nmiss,
  mean = DescrTab2:::.mean,
  sd = DescrTab2:::.sd,
  CILM = DescrTab2:::.meanCIlower,
  CIUM = DescrTab2:::.meanCIupper)

format_summary_stats <- list(
  CIL = scales::label_percent(),
  CIU = scales::label_percent(),
  CILM = function(x) format(x, digits = 2, scientific = 3),
  CIUM = function(x) format(x, digits = 2, scientific = 3),
  N = function(x) {
    format(x, digits = 2, scientific = 3)
  },
  mean = function(x) {
    format(x, digits = 2, scientific = 3)
  },
  sd = function(x) {
    format(x, digits = 2, scientific = 3)
  },
  CI = function(x) {
    format(x, digits = 2, scientific = 3)
  }
)

reshape_rows <- list(
  `CI` = list(
    args = c("CIL", "CIU"),
    fun = function(CIL, CIU) {
      paste0("[", CIL, ", ", CIU, "]")
    }
  ),
  `CI` = list(
    args = c("CILM", "CIUM"),
    fun = function(CILM, CIUM) {
      paste0("[", CILM, ", ", CIUM, "]")
    }
  )
)

set.seed(123)
dat <- tibble(a_factor = factor(c(rep("a", 70), rep("b", 30))),
              a_numeric = rnorm(100),
              group = sample(c("Trt", "Ctrl"), 100, TRUE)
)

descr(dat, "group",
  format_options=list(omit_factor_level = "last",
  categories_first_summary_stats_second = FALSE,
  combine_mean_sd = TRUE
  ),
  summary_stats_cat = summary_stats_cat,
  summary_stats_cont = summary_stats_cont,
  reshape_rows = reshape_rows,
  format_summary_stats = format_summary_stats)

Variables	Ctrl	Trt	Total	p	CI
	(N=49)	(N=51)	(N=100)
a_factor
CI	[63%, 88%]	[48%, 76%]	[60%, 79%]	0.106^chi2	[-0.029, 0.32]^PWa
a	38 (78%)	32 (63%)	70 (70%)
a_numeric
N	49	51	100	0.122^tt2	[-0.077, 0.64]^t
mean ± sd	0.23 ± 0.88	-0.048 ± 0.93	0.09 ± 0.91
CI	[-0.019, 0.49]	[-0.31, 0.21]	[-0.091, 0.27]
^chi2 Pearson’s chi-squared test
^tt2 Welch’s two-sample t-test
^PWa CI for difference in proportions derived from a normal (“Wald”) approximation
^t CI for difference in means derived from the t-distribution