# 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 | M 1 - M 2 | 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
    ## ----------------------------------------------------------------------------
    ## mpg       |    am |       17.15 |       24.39 |      -7.24 | [-11.28, -3.21]
    ##
    ## Parameter | t(18.33) |     p |     d |          d  CI
    ## -----------------------------------------------------
    ## mpg       |    -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.