Publication-ready t-tests in R

Rémi Thériault

February 3, 2022

Getting started

This function makes it really easy to get all all your t-test results in one simple, publication-ready table.

Let’s first load the demo data. This data set comes with base R (meaning you have it too and can directly type this command into your R console).

head(mtcars)

##                    mpg cyl disp  hp drat    wt  qsec vs am gear carb
## Mazda RX4         21.0   6  160 110 3.90 2.620 16.46  0  1    4    4
## Mazda RX4 Wag     21.0   6  160 110 3.90 2.875 17.02  0  1    4    4
## Datsun 710        22.8   4  108  93 3.85 2.320 18.61  1  1    4    1
## Hornet 4 Drive    21.4   6  258 110 3.08 3.215 19.44  1  0    3    1
## Hornet Sportabout 18.7   8  360 175 3.15 3.440 17.02  0  0    3    2
## Valiant           18.1   6  225 105 2.76 3.460 20.22  1  0    3    1

Load the rempsyc package:

library(rempsyc)

Note: If you haven’t installed this package yet, you will need to install it via the following command: install.packages("rempsyc"). Furthermore, you may be asked to install the following packages if you haven’t installed them already (you may decide to install them all now to avoid interrupting your workflow if you wish to follow this tutorial from beginning to end):

pkgs <- c("effectsize", "flextable", "broom", "report")
install_if_not_installed(pkgs)

---

nice_t_test(
  data = mtcars,
  response = "mpg",
  group = "am",
  warning = FALSE
)

##   Dependent Variable         t       df           p         d  CI_lower
## 1                mpg -3.767123 18.33225 0.001373638 -1.477947 -2.265973
##     CI_upper
## 1 -0.6705684

Note: This function relies on the base R t.test function, which uses the Welch t-test per default (see why here: https://daniellakens.blogspot.com/2015/01/always-use-welchs-t-test-instead-of.html). To use the Student t-test, simply add the following argument: var.equal = TRUE.

Now the best thing about this function is that you can put all your dependent variables of interest in the function call and it will output a sweet, pre-formatted table for your convenience.

t.test.results <- nice_t_test(
  data = mtcars,
  response = names(mtcars)[1:6],
  group = "am",
  warning = FALSE
)
t.test.results

##   Dependent Variable         t       df            p          d   CI_lower
## 1                mpg -3.767123 18.33225 1.373638e-03 -1.4779471 -2.2659732
## 2                cyl  3.354114 25.85363 2.464713e-03  1.2084550  0.4315895
## 3               disp  4.197727 29.25845 2.300413e-04  1.4452210  0.6417836
## 4                 hp  1.266189 18.71541 2.209796e-01  0.4943081 -0.2260463
## 5               drat -5.646088 27.19780 5.266742e-06 -2.0030843 -2.8592770
## 6                 wt  5.493905 29.23352 6.272020e-06  1.8924060  1.0300224
##     CI_upper
## 1 -0.6705684
## 2  1.9683142
## 3  2.2295594
## 4  1.2066995
## 5 -1.1245499
## 6  2.7329219

If we want it to look nice

my_table <- nice_table(t.test.results)
my_table

Dependent Variable	t	df	p	d	95% CI
mpg	-3.77	18.33	.001**	-1.48	[-2.27, -0.67]
cyl	3.35	25.85	.002**	1.21	[0.43, 1.97]
disp	4.20	29.26	< .001***	1.45	[0.64, 2.23]
hp	1.27	18.72	.221	0.49	[-0.23, 1.21]
drat	-5.65	27.20	< .001***	-2.00	[-2.86, -1.12]
wt	5.49	29.23	< .001***	1.89	[1.03, 2.73]

Note: The d is Cohen’s d, and the 95% CI is the confidence interval of the effect size (Cohen’s d). p is the p-value, df is degrees of freedom, and t is the t-value.

Save table to Word

Let’s open (or save) it to word for use in a publication (optional).

# Open in Word
print(my_table, preview = "docx")

# Save in Word
flextable::save_as_docx(my_table, path = "t-tests.docx")

Special cases

The function can be passed some of the regular arguments of the base t.test() function. For example:

Student t-test (instead of Welch)

nice_t_test(
  data = mtcars,
  response = "mpg",
  group = "am",
  var.equal = TRUE
) |>
  nice_table()

Dependent Variable	t	df	p	d	95% CI
mpg	-4.11	30	< .001***	-1.48	[-2.27, -0.67]

One-sided (instead of two-sided)

nice_t_test(
  data = mtcars,
  response = "mpg",
  group = "am",
  alternative = "less",
  warning = FALSE
) |>
  nice_table()

Dependent Variable	t	df	p	d	95% CI
mpg	-3.77	18.33	.001***	-1.48	[-2.27, -0.67]

One-sample (instead of two-sample)

nice_t_test(
  data = mtcars,
  response = "mpg",
  mu = 17,
  warning = FALSE
) |>
  nice_table()

Dependent Variable	t	df	p	d	95% CI
mpg	2.90	31	.007**	0.51	[0.14, 0.88]

Paired t test (instead of independent samples)

Note that for paired t tests, you need to use paired = TRUE, and you also need data in “long” format rather than wide format (like for the ToothGrowth data set). In this case, the group argument refers to the participant ID for example, so the same group/participant is measured several times, and thus has several rows.

nice_t_test(
  data = ToothGrowth,
  response = "len",
  group = "supp",
  paired = TRUE
) |>
  nice_table()

Note that R >= 4.4.0 has stopped supporting the paired argument for the formula method used internally in nice_t_test(), but since version 0.1.7.8, we use a workaround for backward compatibility.

Multiple comparison corrections

It is also possible to correct for multiple comparisons. Note that only a Bonferroni correction is supported at this time (which simply multiplies the p-value by the number of tests). Bonferroni will automatically correct for the number of tests.

nice_t_test(
  data = mtcars,
  response = names(mtcars)[1:6],
  group = "am",
  correction = "bonferroni",
  warning = FALSE
) |>
  nice_table()

Dependent Variable	t	df	p	d	95% CI
mpg	-3.77	18.33	.008**	-1.48	[-2.27, -0.67]
cyl	3.35	25.85	.015*	1.21	[0.43, 1.97]
disp	4.20	29.26	.001**	1.45	[0.64, 2.23]
hp	1.27	18.72	1.326	0.49	[-0.23, 1.21]
drat	-5.65	27.20	< .001***	-2.00	[-2.86, -1.12]
wt	5.49	29.23	< .001***	1.89	[1.03, 2.73]

Integrations

There are other ways to do t-tests and format the results properly, should you wish—for example through the broom and report packages. Examples below.

model <- t.test(mpg ~ am, data = mtcars)

broom table

library(broom)
(stats.table <- tidy(model, conf.int = TRUE))

## # A tibble: 1 × 10
##   estimate estimate1 estimate2 statistic p.value parameter conf.low conf.high
##      <dbl>     <dbl>     <dbl>     <dbl>   <dbl>     <dbl>    <dbl>     <dbl>
## 1    -7.24      17.1      24.4     -3.77 0.00137      18.3    -11.3     -3.21
## # ℹ 2 more variables: method <chr>, alternative <chr>

nice_table(stats.table, broom = "t.test")

Method	Alternative	Mean 1	Mean 2	M1 - M2	t	df	p	95% CI
Welch Two Sample t-test	two.sided	17.15	24.39	-7.24	-3.77	18.33	.001**	[-11.28, -3.21]

report table

library(report)
(stats.table <- as.data.frame(report(model)))

## Welch Two Sample t-test
## 
## Parameter | Group | Mean_Group1 | Mean_Group2 | Difference |          95% CI | t(18.33) |     p |     d |          d  CI
## ------------------------------------------------------------------------------------------------------------------------
## mpg       |    am |       17.15 |       24.39 |      -7.24 | [-11.28, -3.21] |    -3.77 | 0.001 | -1.76 | [-2.82, -0.67]
## 
## Alternative hypothesis: two.sided

nice_table(stats.table, report = "t.test")

Parameter	Group	Mean_Group1	Mean_Group2	Difference	t	95% CI (t)	df	p	Method	Alternative	d	95% CI (d)
mpg	am	17.15	24.39	-7.24	-3.77	[-11.28, -3.21]	18.33	.001**	Welch Two Sample t-test	two.sided	-1.76	[-2.82, -0.67]

The report package provides quite comprehensive tables, so one may request an abbreviated table with the short argument.

nice_table(stats.table, report = "t.test", short = TRUE)

Parameter	Group	t	df	p	Method	Alternative	d	95% CI (d)
mpg	am	-3.77	18.33	.001**	Welch Two Sample t-test	two.sided	-1.76	[-2.82, -0.67]

And there you go!

Thanks for checking in

Make sure to check out this page again if you use the code after a time or if you encounter errors, as I periodically update or improve the code. Feel free to contact me for comments, questions, or requests to improve this function at https://github.com/rempsyc/rempsyc/issues. See all tutorials here: https://remi-theriault.com/tutorials.