First, load the {report} package:

library(report)

## Getting started with correlations

Let’s start by demonstrating some features with simple tests. The function report_table() can be used to create a table for many R objects.

results <- cor.test(mtcars$mpg, mtcars$wt)

report_table(results)
# Pearson's product-moment correlation
#
# Parameter1 | Parameter2 |     r |         95% CI | t(30) |      p
# -----------------------------------------------------------------
# mtcars$mpg | mtcars$wt | -0.87 | [-0.93, -0.74] | -9.56 | < .001
#
# Alternative hypothesis: two.sided

We can also obtain a shorter version by running summary() on the output (that we are going to store in a variable called t - like table)

t <- summary(report_table(results))
t
# Pearson's product-moment correlation
#
# Parameter1 | Parameter2 |     r |         95% CI |      p
# ---------------------------------------------------------
# mtcars$mpg | mtcars$wt | -0.87 | [-0.93, -0.74] | < .001
#
# Alternative hypothesis: two.sided

In the example above, running just t ran print(t) under the hood, which prints the table inside the console. However, one can nicely display that table in markdown documents using display().

display(t)
Parameter1 Parameter2 r 95% CI p
mtcars$$mpg | mtcars$$wt -0.87 (-0.93, -0.74) < .001

Alternative hypothesis: true correlation is not equal to 0

We can further customize this table, by adding significance stars,

display(t,
stars = TRUE,
title = "Table 1",
footer = "Correlation in the mtcars (n = 32) dataset.\n*** p < .001"
)
Table 1
Parameter1 Parameter2 r 95% CI p
mtcars$$mpg | mtcars$$wt -0.87 (-0.93, -0.74) < .001***

Correlation in the mtcars (n = 32) dataset. *** p < .001

Alternative hypothesis: true correlation is not equal to 0

## t-tests, …

It works similarly for t-tests.

results <- t.test(mtcars$mpg ~ mtcars$am)

t <- summary(report_table(results))
t
# Difference |          95% CI | t(18.33) |     p | Cohen's d |   Cohens_d  CI
# ----------------------------------------------------------------------------
# -7.24      | [-11.28, -3.21] |    -3.77 | 0.001 |     -1.41 | [-2.26, -0.53]
#
# Alternative hypothesis: two.sided

Note that, by default, report_table() prettifies the printing: that means that the column names and its content is, underneath, not necessarily what is printed, which can be a bit confusing. For instance, while the confidence interval CI appears as one column, it this actually made of three columns! One can access this raw table as a dataframe:

as.data.frame(t)
#   Difference   CI    CI_low   CI_high         t df_error
# 1  -7.244939 0.95 -11.28019 -3.209684 -3.767123 18.33225
#             p Alternative  Cohens_d Cohens_d_CI_low
# 1 0.001373638   two.sided -1.411046       -2.260021
#   Cohens_d_CI_high
# 1       -0.5342257

In fact, the function used to prettify the output is called insight::format_table() and is accessible to you too, so that you can prettify the output while keeping it as a data frame.

insight::format_table(as.data.frame(t), stars = TRUE)
#   Difference          95% CI t(18.33)       p Alternative
# 1      -7.24 [-11.28, -3.21]    -3.77 0.001**   two.sided
#   Cohen's d   Cohens_d  CI
# 1     -1.41 [-2.26, -0.53]

Also, you can join the results of multiple tables:

results1 <- t.test(mtcars$mpg ~ mtcars$am)
results2 <- t.test(mtcars$wt ~ mtcars$am)
results3 <- t.test(mtcars$qsec ~ mtcars$am)

results <- c(
report_table(results1),
report_table(results2),
report_table(results3)
)
display(results)
Parameter Group Mean_Group1 Mean_Group2 Difference 95% CI t df p Cohen’s d Cohens_d CI
mtcars$$mpg | mtcars$$am 17.15 24.39 -7.24 (-11.28, -3.21) -3.77 18.33 0.001 -1.41 (-2.26, -0.53)
mtcars$$wt | mtcars$$am 3.77 2.41 1.36 (0.85, 1.86) 5.49 29.23 < .001 1.93 (1.08, 2.77)
mtcars$$qsec | mtcars$$am 18.18 17.36 0.82 (-0.49, 2.14) 1.29 25.53 0.209 0.46 (-0.26, 1.18)

## Linear Models

model <- lm(Petal.Length ~ Species * Petal.Width, data = iris)

report_table(model)
# Parameter                          | Coefficient |        95% CI | t(144) |      p | Std. Coef. | Std. Coef. 95% CI |    Fit
# ----------------------------------------------------------------------------------------------------------------------------
# (Intercept)                        |        1.33 | [ 1.07, 1.59] |  10.14 | < .001 |      -1.01 |    [-1.53, -0.48] |
# Species [versicolor]               |        0.45 | [-0.28, 1.19] |   1.21 | 0.227  |       1.16 |    [ 0.63,  1.69] |
# Species [virginica]                |        2.91 | [ 2.11, 3.72] |   7.17 | < .001 |       1.72 |    [ 1.16,  2.28] |
# Petal Width                        |        0.55 | [-0.42, 1.52] |   1.12 | 0.267  |       0.24 |    [-0.18,  0.65] |
# Species [versicolor] × Petal Width |        1.32 | [ 0.23, 2.42] |   2.38 | 0.019  |       0.57 |    [ 0.10,  1.05] |
# Species [virginica] × Petal Width  |        0.10 | [-0.94, 1.14] |   0.19 | 0.848  |       0.04 |    [-0.40,  0.49] |
#                                    |             |               |        |        |            |                   |
# AIC                                |             |               |        |        |            |                   | 128.29
# AICc                               |             |               |        |        |            |                   | 129.08
# BIC                                |             |               |        |        |            |                   | 149.36
# R2                                 |             |               |        |        |            |                   |   0.96
# R2 (adj.)                          |             |               |        |        |            |                   |   0.96
# Sigma                              |             |               |        |        |            |                   |   0.36