First, load the report package:
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.sidedWe 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.sidedIn 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 |
|---|---|---|---|---|
| mtcarsmpg | mtcarswt | -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"
)| Parameter1 | Parameter2 | r | 95% CI | p |
|---|---|---|---|---|
| mtcarsmpg | mtcarswt | -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 | Cohen's d CI
# ----------------------------------------------------------------------------
# -7.24 | [-11.28, -3.21] | -3.77 | 0.001 | -1.41 | [-2.26, -0.53]
#
# Alternative hypothesis: two.sidedNote 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 -0.5342256In 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 Cohen's 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 | Cohen’s d CI |
|---|---|---|---|---|---|---|---|---|---|---|
| mtcarsmpg | mtcarsam | 17.15 | 24.39 | -7.24 | (-11.28, -3.21) | -3.77 | 18.33 | 0.001 | -1.41 | (-2.26, -0.53) | |
| mtcarswt | mtcarsam | 3.77 | 2.41 | 1.36 | (0.85, 1.86) | 5.49 | 29.23 | < .001 | 1.93 | (1.08, 2.77) | |
| mtcarsqsec | mtcarsam | 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
# ------------------------------------------------
# (Intercept) | 1.33
# Species [versicolor] | 0.45
# Species [virginica] | 2.91
# Petal Width | 0.55
# Species [versicolor] × Petal Width | 1.32
# Species [virginica] × Petal Width | 0.10
# |
# AIC |
# AICc |
# BIC |
# R2 |
# R2 (adj.) |
# Sigma |
#
# Parameter | 95% CI | t(144)
# -----------------------------------------------------------
# (Intercept) | [ 1.07, 1.59] | 10.14
# Species [versicolor] | [-0.28, 1.19] | 1.21
# Species [virginica] | [ 2.11, 3.72] | 7.17
# Petal Width | [-0.42, 1.52] | 1.12
# Species [versicolor] × Petal Width | [ 0.23, 2.42] | 2.38
# Species [virginica] × Petal Width | [-0.94, 1.14] | 0.19
# | |
# AIC | |
# AICc | |
# BIC | |
# R2 | |
# R2 (adj.) | |
# Sigma | |
#
# Parameter | p | Std. Coef.
# --------------------------------------------------------
# (Intercept) | < .001 | -1.01
# Species [versicolor] | 0.227 | 1.16
# Species [virginica] | < .001 | 1.72
# Petal Width | 0.267 | 0.24
# Species [versicolor] × Petal Width | 0.019 | 0.57
# Species [virginica] × Petal Width | 0.848 | 0.04
# | |
# AIC | |
# AICc | |
# BIC | |
# R2 | |
# R2 (adj.) | |
# Sigma | |
#
# Parameter | Std. Coef. 95% CI | Fit
# ---------------------------------------------------------------
# (Intercept) | [-1.53, -0.48] |
# Species [versicolor] | [ 0.63, 1.69] |
# Species [virginica] | [ 1.16, 2.28] |
# Petal Width | [-0.18, 0.65] |
# Species [versicolor] × Petal Width | [ 0.10, 1.05] |
# Species [virginica] × Petal Width | [-0.40, 0.49] |
# | |
# AIC | | 128.29
# AICc | | 129.08
# BIC | | 149.36
# R2 | | 0.96
# R2 (adj.) | | 0.96
# Sigma | | 0.36