lukaswallrich / timesaver Goto Github PK

R package to quickly perform data analysis and reporting tasks that might otherwise be time intensive

Home Page: https://lukaswallrich.github.io/timesaveR

License: GNU General Public License v3.0

R 99.72% Vim Snippet 0.28%

timesaver's Introduction

timesaveR

This package aims to speed up research projects in social psychology (and related fields). For that, it primarily includes some functions that help lay the groundwork and others that facilitate the reporting of results.

Among others, the package can help with the following:

Creating scales, including reverse-coding and reporting their internal consistency,
Creating correlation tables in APA style, including descriptive statistics and confidence intervals, and an option to use survey weights, multiple imputation or full-information maximum-likelihood estimation
Creating regression tables comparing standardised and non-standardised regression coefficients, and comparing the F-change between two nested models
Formatting numbers for statistical reporting, including rounding with trailing zeros, or displaying numbers as p-values or as confidence intervals
Data entry and transfer, for quick interactive use. This includes splitting clipboard content into vectors, converting tibbles to tribble code, or obtaining nicely formatted code and results to paste into another application

Why use this package?

There are many packages that support data analysis and reporting. For instance, the psych package offers functions to create scales, while the modelsummary package offers options to create customisable tables in a wide variety of output format. They power many of the functions offered here ‘under the hood.’

apa and papaja are two packages that directly support the reporting of results in APA style - they can complement this package well. However, none of the existing offered quite what we needed. This package

takes an end-to-end view of the data analysis process, streamlining time-consuming steps at various stages
offers analysis templates that make it easy to get started, particularly for R novices,
prioritises publication-readiness and good reporting practices over customisability in creating tables and charts
integrates with the tidyverse by supporting tidy evaluation and returning tibbles where possible

Installation

You can install timesaveR from GitHub with the command below. If you do not have the remotes-package installed, run install.packages("remote") first.

remotes::install_github('lukaswallrich/timesaveR')

Get started

There are many functions in the package, and we will create vignettes detailing various use cases. However, the following can give you an initial flavor. The examples use data from the European Social Survey Wave 7 (2014). Here, I ignore survey weights. However, the package offers similar functions for analysing weighted survey data, which are explained in the survey data vignette.

Load the package

(I also load dplyr since that is the recommended usage - of course, there are base R alternatives for all of the steps.)

library(dplyr)
#> 
#> Attaching package: 'dplyr'
#> The following objects are masked from 'package:stats':
#> 
#>     filter, lag
#> The following objects are masked from 'package:base':
#> 
#>     intersect, setdiff, setequal, union
library(timesaveR)
#> Note re timesaveR: Many functions in this package are alpha-versions - please treat results with care and report bugs and desired features.

Create scales

Let’s create scales for health behaviours and depressive symptoms, each including some reverse coding.

scales <- list(
  depression = c("fltdpr", "flteeff", "slprl", "wrhpp", "fltlnl", 
                 "enjlf", "fltsd", "cldgng"),
  healthy_eating = c("etfruit", "eatveg")
  )
  
scales_reverse <- list(
  depression = c("wrhpp", "enjlf"),
  healthy_eating = c("etfruit", "eatveg")
)

scales <- make_scales(ess_health, items = scales, reversed = scales_reverse)
#> The following scales will be calculated with specified reverse coding: depression, healthy_eating

#Check descriptives, including reliability
scales$descriptives
#> # A tibble: 2 × 10
#>   Scale      n_items reliability reliability_method  mean    SD reversed rev_min
#>   <chr>        <int>       <dbl> <chr>              <dbl> <dbl> <chr>      <dbl>
#> 1 depression       8       0.802 cronbachs_alpha     1.67 0.484 wrhpp e…       1
#> 2 healthy_e…       2       0.658 spearman_brown      4.97 1.11  etfruit…       1
#> # ℹ 2 more variables: rev_max <dbl>, text <chr>

#Add scale scores to dataset
ess_health <- bind_cols(ess_health, scales$scores)

Report correlations and descriptive statistics

Next, we are often interested in descriptive statistics, variable distributions and correlations.

ess_health %>% select(agea, health, depression, healthy_eating) %>% 
    cor_matrix() %>% report_cor_table()

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<tr class="gt_col_headings">
  <th class="gt_col_heading gt_columns_bottom_border gt_left" rowspan="1" colspan="1" scope="col" id="Variable">Variable</th>
  <th class="gt_col_heading gt_columns_bottom_border gt_right" rowspan="1" colspan="1" scope="col" id="&lt;em&gt;M (SD)&lt;/em&gt;"><em>M (SD)</em></th>
  <th class="gt_col_heading gt_columns_bottom_border gt_left" rowspan="1" colspan="1" scope="col" id="1">1</th>
  <th class="gt_col_heading gt_columns_bottom_border gt_left" rowspan="1" colspan="1" scope="col" id="2">2</th>
  <th class="gt_col_heading gt_columns_bottom_border gt_left" rowspan="1" colspan="1" scope="col" id="3">3</th>
</tr>

agea	50.61 (18.51)
health	2.26 (0.92)	.28 *** [0.25, 0.30]
depression	1.67 (0.48)	.03 * [0.00, 0.05]	.42 *** [0.40, 0.44]
healthy_eating	4.97 (1.11)	.17 *** [0.15, 0.19]	-.09 *** [-0.11, -0.07]	-.13 *** [-0.15, -0.10]
M and SD are used to represent mean and standard deviation, respectively. Values in square brackets indicate the 95% confidence interval for each correlation.
† p < .1, * p < .05, p < .01, * p < .001

#It is often helpful to rename variables in this step #Use get_rename_tribbles(ess_health) to get most of this code var_renames <- tibble::tribble( ~old, ~new,
"agea", "Age",
"health", "Poor health", "depression", "Depression", "healthy_eating", "Healthy eating", )

#A rename tibble or vector automatically only selects the variables included into it ess_health %>% cor_matrix(var_names = var_renames) %>% report_cor_table()

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<tr class="gt_col_headings">
  <th class="gt_col_heading gt_columns_bottom_border gt_left" rowspan="1" colspan="1" scope="col" id="Variable">Variable</th>
  <th class="gt_col_heading gt_columns_bottom_border gt_right" rowspan="1" colspan="1" scope="col" id="&lt;em&gt;M (SD)&lt;/em&gt;"><em>M (SD)</em></th>
  <th class="gt_col_heading gt_columns_bottom_border gt_left" rowspan="1" colspan="1" scope="col" id="1">1</th>
  <th class="gt_col_heading gt_columns_bottom_border gt_left" rowspan="1" colspan="1" scope="col" id="2">2</th>
  <th class="gt_col_heading gt_columns_bottom_border gt_left" rowspan="1" colspan="1" scope="col" id="3">3</th>
</tr>

Age	50.61 (18.51)
Poor health	2.26 (0.92)	.28 *** [0.25, 0.30]
Depression	1.67 (0.48)	.03 * [0.00, 0.05]	.42 *** [0.40, 0.44]
Healthy eating	4.97 (1.11)	.17 *** [0.15, 0.19]	-.09 *** [-0.11, -0.07]	-.13 *** [-0.15, -0.10]
M and SD are used to represent mean and standard deviation, respectively. Values in square brackets indicate the 95% confidence interval for each correlation.
† p < .1, * p < .05, p < .01, * p < .001

#Often, it is also interesting to include variable distributions ess_health %>% cor_matrix(var_names = var_renames) %>% report_cor_table(add_distributions = TRUE, data = ess_health)

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<tr class="gt_col_headings">
  <th class="gt_col_heading gt_columns_bottom_border gt_left" rowspan="1" colspan="1" scope="col" id="Variable">Variable</th>
  <th class="gt_col_heading gt_columns_bottom_border gt_right" rowspan="1" colspan="1" scope="col" id="&lt;em&gt;M (SD)&lt;/em&gt;"><em>M (SD)</em></th>
  <th class="gt_col_heading gt_columns_bottom_border gt_right" rowspan="1" colspan="1" scope="col" id="Distributions">Distributions</th>
  <th class="gt_col_heading gt_columns_bottom_border gt_left" rowspan="1" colspan="1" scope="col" id="1">1</th>
  <th class="gt_col_heading gt_columns_bottom_border gt_left" rowspan="1" colspan="1" scope="col" id="2">2</th>
  <th class="gt_col_heading gt_columns_bottom_border gt_left" rowspan="1" colspan="1" scope="col" id="3">3</th>
</tr>

Age	50.61 (18.51)
Poor health	2.26 (0.92)	.28 *** [0.25, 0.30]
Depression	1.67 (0.48)	.03 * [0.00, 0.05]	.42 *** [0.40, 0.44]
Healthy eating	4.97 (1.11)	.17 *** [0.15, 0.19]	-.09 *** [-0.11, -0.07]	-.13 *** [-0.15, -0.10]
M and SD are used to represent mean and standard deviation, respectively. Values in square brackets indicate the 95% confidence interval for each correlation.
† p < .1, * p < .05, p < .01, * p < .001

Describe categorical variables and their relation with an outcome

Often, we are also interested in how the means of an outcome variable differ between different groups. It can be fiddly to get these tables and the pairwise significance tests done, but this function does it in a breeze.

# Start with this in the console - that gets you 80% of the tribbles below.
# get_rename_tribbles(ess_health, gndr, cntry)

var_renames <- tribble(
    ~old,     ~new,     
    "gndr",   "Gender",  
    "cntry",  "Country"
)

level_renames <- tribble(
    ~var,     ~level_old, ~level_new, 
    "gndr",   "1",        "male",       
    "gndr",   "2",        "female",       
    "cntry",  "DE",       "Germany",      
    "cntry",  "FR",       "France",      
    "cntry",  "GB",       "UK"
)

report_cat_vars(ess_health, health, gndr, cntry, var_names = var_renames, 
              level_names = level_renames)

	N	Share	M (SD)
Gender
male	3482	48.2%	2.23 (0.90) ^a
female	3744	51.8%	2.30 (0.93) ^b
Country
Germany	3045	42.1%	2.34 (0.88) ^a
France	1917	26.5%	2.29 (0.89) ^a
UK	2264	31.3%	2.14 (0.97) ^b
M and SD are used to represent mean and standard deviation for health for that group, respectively.
Within each variable, the means of groups with different superscripts differ with p \< .05 (p-values were adjusted using the Holm-method.)

Report regression models with standardized coefficients

In psychology, it is often expected that regression models are reported with both unstandardised (B) and standardized (beta) coefficients. This can be fiddly as separate tables will contain too much redundant information. The functions below easily run a model with standardised variables and create a publication-ready table.

ess_health$gndr <- factor(ess_health$gndr)

#Standard lm model
mod1 <- lm(depression ~ agea + gndr + health + cntry, ess_health)

#Model with standardised coefficients

mod2 <- lm_std(depression ~ agea + gndr + health + cntry, ess_health)

report_lm_with_std(mod1, mod2)

	B (SE)	β \[95% CI\]
(Intercept)	1.20 (0.02)\\\*	-0.14 \[-0.18, -0.10\]
agea	-0.00 (0.00)\\\*	-0.10 \[-0.12, -0.08\]
gndr2	0.12 (0.01)\\\*	0.24 \[0.20, 0.28\]
health	0.23 (0.01)\\\*	0.44 \[0.42, 0.47\]
cntryFR	-0.01 (0.01)	-0.02 \[-0.08, 0.03\]
cntryGB	0.04 (0.01)\\	0.08 \[0.03, 0.13\]
N	7171
R²	.20
F-tests	F(5, 7165) = 358.25, p \< .001
Given that dummy variables lose their interpretability when standardized (Fox, 2015), β for dummy variables are semi-standardized, indicating the impact of that dummy on the standardized outcome variable.
† p \< .1, \* p \< .05, \\ p \< .01, \\\* p \< .001

#Often the coefficients should be renamed - get_coef_rename_tribble(mod1) 
#is the starting point. In that, markdown formatting can be used.

coef_names <- tribble(
  ~old,           ~new,           
   "(Intercept)",  "*(Intercept)*", 
   "agea",         "Age",        
   "gndr2",        "Gender *(female)*",       
   "health",       "Poor health",      
   "cntryFR",      "France *(vs DE)*",     
   "cntryGB",      "UK *(vs DE)*"
)

report_lm_with_std(mod1, mod2, coef_renames = coef_names)

	B (SE)	β \[95% CI\]
(Intercept)	1.20 (0.02)\\\*	-0.14 \[-0.18, -0.10\]
Age	-0.00 (0.00)\\\*	-0.10 \[-0.12, -0.08\]
Gender (female)	0.12 (0.01)\\\*	0.24 \[0.20, 0.28\]
Poor health	0.23 (0.01)\\\*	0.44 \[0.42, 0.47\]
France (vs DE)	-0.01 (0.01)	-0.02 \[-0.08, 0.03\]
UK (vs DE)	0.04 (0.01)\\	0.08 \[0.03, 0.13\]
N	7171
R²	.20
F-tests	F(5, 7165) = 358.25, p \< .001
Given that dummy variables lose their interpretability when standardized (Fox, 2015), β for dummy variables are semi-standardized, indicating the impact of that dummy on the standardized outcome variable.
† p \< .1, \* p \< .05, \\ p \< .01, \\\* p \< .001

#You can also easily display multiple nested models side-by-side and get the
#F-change significance test. For that, all models need to be fit on the same
#dataset, so that I will drop all missing data.

mod1 <- lm(depression ~ agea + gndr + health + cntry, tidyr::drop_na(ess_health))
mod2 <- lm_std(depression ~ agea + gndr + health + cntry, tidyr::drop_na(ess_health))


mod3 <- lm(depression ~ agea * gndr + eisced + health + cntry, tidyr::drop_na(ess_health))
mod4 <- lm_std(depression ~ agea * gndr + eisced + health + cntry, tidyr::drop_na(ess_health))

coef_names <- tribble(
  ~old,           ~new,           
   "(Intercept)",  "*(Intercept)*", 
   "agea",         "Age",        
   "gndr2",        "Gender *(female)*",       
   "health",       "Poor health",      
   "cntryFR",      "France *(vs DE)*",     
   "cntryGB",      "UK *(vs DE)*",
   "eisced",       "Education",
   "agea:gndr2",   "Age x Female",
)

report_lm_with_std(mod = list(mod1, mod3), mod_std = list(mod2, mod4),
                   coef_renames = coef_names, R2_change = TRUE)
#> Warning: Automatic coercion from double to character was deprecated in purrr 1.0.0.
#> ℹ Please use an explicit call to `as.character()` within `map_chr()` instead.
#> ℹ The deprecated feature was likely used in the timesaveR package.
#>   Please report the issue at
#>   <https://github.com/LukasWallrich/timesaveR/issues>.
#> This warning is displayed once every 8 hours.
#> Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
#> generated.

	Model 1		Model 2
	B (SE)	β \[95% CI\]	B (SE)	β \[95% CI\]
(Intercept)	1.21 (0.02)\\\*	-0.14 \[-0.18, -0.10\]	1.27 (0.02)\\\*	-0.14 \[-0.18, -0.10\]
Age	-0.00 (0.00)\\\*	-0.10 \[-0.12, -0.08\]	-0.00 (0.00)\\\*	-0.14 \[-0.17, -0.10\]
Gender (female)	0.11 (0.01)\\\*	0.24 \[0.20, 0.28\]	0.03 (0.03)	0.24 \[0.20, 0.28\]
Poor health	0.23 (0.01)\\\*	0.44 \[0.42, 0.46\]	0.23 (0.01)\\\*	0.44 \[0.42, 0.46\]
France (vs DE)	-0.01 (0.01)	-0.03 \[-0.08, 0.02\]	-0.01 (0.01)	-0.03 \[-0.08, 0.02\]
UK (vs DE)	0.04 (0.01)\\	0.08 \[0.03, 0.13\]	0.04 (0.01)\\	0.08 \[0.03, 0.13\]
Education			-0.00 (0.00)†	-0.02 \[-0.04, 0.00\]
Age x Female			0.00 (0.00)\\	0.07 \[0.02, 0.11\]
N	6852		6852
R²	.20		.20
F-tests	F(5, 6846) = 337.06, p \< .001		F(7, 6844) = 243.10, p \< .001
Change	ΔR² = .00, F(2, 6844) = 6.78, p = .001
Given that dummy variables lose their interpretability when standardized (Fox, 2015), β for dummy variables are semi-standardized, indicating the impact of that dummy on the standardized outcome variable.
† p \< .1, \* p \< .05, \\ p \< .01, \\\* p \< .001

Related/alternative packages

modelsummary allows you to create highly customisable tables with data summaries or the output of statistical models that can be saved in a wide range of formats.
apa mostly offers functions that turn the output of statistical tests (e.g., t-tests) into text, in line with APA guidelines.
papaja offers the opportunity to create full APA-style journal manuscripts in R. It’s apa_table function is a generic alternative to the table functions in this package, which support many more types of models, but includes fewer details.

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pedersebastian

timesaver's Issues

Make dump_to_clip pipe-ready (or at least accept variables as well as characters)

fmt_p returns misleading results with character input

Especially false significant results must be avoided.

library(timesaveR)
#> Note re timesaveR: Many functions in this package are alpha-versions - please treat results with care and report bugs and desired features.
fmt_p("a")
#> [1] "> 1 (!!)"
# and worse
fmt_p(".1")
#> [1] "< .001"

^{Created on 2024-07-10 with reprex v2.0.2}

Add option equal_sign = FALSE to the fmt_p so that it can be used in tables

gt_apa_style: format group headings

Often gt tables benefit from group headings (stubs) - but both in default and in gt_apa_style, they are not clear. Ideally, they should have negative indent and be in italics, in tables like this:

Harmonise argument names

Before submitting this to CRAN, argument names should be cleaned up. Particularly, the data argument should always be called data (unless a single vector, then x), and always come first. Also, same argument in related functions should have the same name (items vs scale_items in make_scale(s).

Below the status quo:

Folder	Function	Arguments
R/cat_desc_table.R	.add_css	code, class, add
R/cat_desc_table.R	report_cat_vars	data, dv, ..., var_names, level_names, p_adjust, alpha_level, filename, notes, dv_name, bold_vars, apa_style, css_tags, na.rm, exclude_na
R/cor_tables.R	.wtd_cor_test_lm	x, y, wt, ...
R/cor_tables.R	cor_matrix	data, var_names, missing, conf_level, method, adjust, bootstrap, seed
R/cor_tables.R	cor_matrix_mi	data, weights, var_names
R/cor_tables.R	gt_add_plots	gt_table, plots, col_index
R/cor_tables.R	plot_distributions	data, var_names, plot_type, hist_align_y, plot_theme
R/cor_tables.R	report_cor_table	cor_matrix, ci, n, add_distributions, data, filename, notes, stars, add_title, extras, apa_style, ...
R/cor_tables.R	svy_cor_matrix	svy_data, var_names, return_n
R/cor_tables.R	tidy.cor_matrix	x, both_directions, ...
R/cor_tables.R	tidy.svy_cor_matrix	x, both_directions, ...
R/cor_tables.R	wtd_cor_matrix_mi	mi_list, weights, var_names
R/formatters.R	fmt_ci	lower, upper, digits, drop_0
R/formatters.R	fmt_cor	cor_value, digits
R/formatters.R	fmt_p	p_value, digits, equal_sign
R/formatters.R	fmt_pct	x, digits
R/formatters.R	round_	x, digits
R/formatters.R	round_df	df, digits
R/make_scales.R	.cronbach_boot	list_compl_data, boot
R/make_scales.R	.scale_formula	items
R/make_scales.R	make_scale	data, scale_items, scale_name, reverse, reverse_items, two_items_reliability, r_key, proration_cutoff, print_hist, print_desc, return_list, harmonize_ranges
R/make_scales.R	make_scale_mi	data, scale_items, scale_name, proration_cutoff, seed, alpha_ci, boot, parallel, ...
R/make_scales.R	make_scales	data, items, reversed, two_items_reliability, print_desc, ...
R/make_scales.R	pool_estimates	x, ci
R/make_scales.R	range_	x, digits, simplify
R/make_scales.R	spearman_brown	data, items, name, SB_only
R/make_scales.R	svy_make_scale	data, scale_items, scale_name, print_hist, print_desc, scale_title, reversed, r_key
R/med_mod_code.R	run_mediation	data, X, Y, Ms, CVs, standardized_all, conf_level, seed, bootstraps, ...
R/med_mod_plots.R	.grViz_and_save	code, filename
R/med_mod_plots.R	plot_mediation	IV, DV, Ms, data, digits, coef_offset, filename, ind_p_values
R/med_mod_plots.R	plot_moderated_mediation	X, M, W, Y, CV, mod_direct_path, labels, filename
R/misc.R	.floor_ceiling	x, i
R/misc.R	.is.numeric_col	col, data
R/misc.R	.make_stars_note	stars, markdown
R/misc.R	.pad	x, len, padding
R/misc.R	.unescape_html	str
R/misc.R	add_class	x, class_to_add
R/misc.R	clip_excel	data, row_names, col_names, ...
R/misc.R	cut_p	x, p, ties.method, fct_levels, verbose
R/misc.R	dump_to_clip	objects
R/misc.R	dupl_items	x
R/misc.R	get_coef_rename_tribble	mod, show
R/misc.R	get_rename_tribbles	data, ..., show, which, max_levels
R/misc.R	l2v	x, strings, separators, to_clip, return
R/misc.R	line_to_vector	x, strings, separators, to_clip, return
R/misc.R	na_ifs	x, replace
R/misc.R	na_share	x, round
R/misc.R	na_when	x, ..., operator
R/misc.R	named_list	...
R/misc.R	paste_	..., sep, collapse
R/misc.R	rename_cat_variables	data, var_names, level_names
R/misc.R	rm_na	x
R/misc.R	run_and_format	code
R/misc.R	scale_blank	x, center, scale
R/misc.R	sigstars	p, stars, pad_html, ns, return_NAs
R/misc.R	to_tribble	x, show, digits, to_clip
R/mod_tables.R	.lm_F_test	mod
R/mod_tables.R	fmt_labels_md	tab, position
R/mod_tables.R	glance.mira	x, adj_R2, ...
R/mod_tables.R	gt_apa_style	gt_table, fmt_labels_md
R/mod_tables.R	mira.lm_F_test	mod, return_list
R/mod_tables.R	report_lm_with_std	mod, mod_std, conf_level, coef_renames, fmt, statistic_vertical, filename, model_names, show_nimp, R2_change, notes, apa_style, stars, ...
R/mod_tables.R	report_polr_with_std	mod, mod_std, OR, conf_level, fmt, statistic_vertical, filename, model_names, show_nimp, notes, apa_style, stars, ...
R/mod_tables.R	tidy_custom.polr	x, ...
R/mod_tables.R	tidy.mira	x, conf.int, conf.level, ...
R/mod_tables.R	tidy.tsR_std	x, ...
R/pcor.R	pcor_matrix	data, given, ...
R/project_management.R	add_package_snippets
R/project_management.R	ggsave_show	filename, ..., device, units
R/project_management.R	rproj_to_clip
R/project_management.R	setup_analysis_project	folder, analyses, pipeline_name, code_folder, standard_packages, github_packages
R/report_stats.R	report_anova	x
R/stat_tests.R	.clean_names	x
R/stat_tests.R	.run_t_test_mi	mi_list
R/stat_tests.R	.wtd_t_test_lm	dv, g, wt, ...
R/stat_tests.R	dummy_code	x, prefix, drop_first, verbose
R/stat_tests.R	get_pairwise_letters	tests, alpha_level, p.adjust.method
R/stat_tests.R	lm_std	formula, data, weights, ...
R/stat_tests.R	paired_t_test_d	data, x, y
R/stat_tests.R	pairwise_t_test_mi	mi_list, dv, groups, weights, p.adjust.method
R/stat_tests.R	pairwise_t_tests	data, outcome, groups, p.adjust.method, conf_level, var_equal
R/stat_tests.R	polr_std	formula, data, weights, ...
R/stat_tests.R	svy_cohen_d_pair	data, dv, iv, pair, ttest, print
R/stat_tests.R	svy_pairwise_t_test	data, dv, iv, cats, p.adjust, ...
R/stat_tests.R	t_test_mi	mi_list, dv, groups, weights
R/svy_helpers.R	svy_miss_var_summary	svy_data, ..., .any_missing, .include_complete
R/timesaveR.R	.check_req_packages	x, note
R/timesaveR.R	.onAttach	libname, pkgname
R/timesaveR.R	knit_print.timesaveR_raw_html	x, ...
R/timesaveR.R	print.timesaveR_raw_html	x, ...

Singular variables break cor_matrix_mi

The following fails with uninformative error - take check from make_scales() and remove singular variables with warning/message.

 df <- tibble(.imp = rep(1, 10), a = 1:10, b = rep(10, 10))
cor_matrix_mi(df)

Add ORCiD to DESCRIPTION

pairwise_t_tests - better error with only 1 grou[p

This is not clear

library(tidyverse)
library(timesaveR)
#> Note re timesaveR: Many functions in this package are alpha-versions - please treat results with care and report bugs and desired features.

df <- mtcars %>% filter(cyl == 6)

pairwise_t_tests(df, mpg ~ cyl)
#> Error in utils::combn(., 2): n < m

^{Created on 2024-05-17 with reprex v2.0.2}

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