## ----global_options, include=FALSE--------------------------------------------
library(knitr)

knitr::opts_chunk$set(
  fig.width = 5, fig.height = 3, warning = FALSE,
  message = FALSE, out.width = "70%"
)
knitr::opts_knit$set(root.dir = tempdir())

pkgs <- c("effectsize", "flextable", "interactions")
successfully_loaded <- vapply(pkgs, requireNamespace, FUN.VALUE = logical(1L), quietly = TRUE)
can_evaluate <- all(successfully_loaded)

if (can_evaluate) {
  knitr::opts_chunk$set(eval = TRUE)
  vapply(pkgs, require, FUN.VALUE = logical(1L), quietly = TRUE, character.only = TRUE)
} else {
  knitr::opts_chunk$set(eval = FALSE)
}

## -----------------------------------------------------------------------------
head(mtcars)

## -----------------------------------------------------------------------------
library(rempsyc)

## -----------------------------------------------------------------------------
pkgs <- c("effectsize", "flextable", "interactions")
install_if_not_installed(pkgs)

## -----------------------------------------------------------------------------
mtcars2 <- lapply(mtcars, scale) |> as.data.frame()

## -----------------------------------------------------------------------------
moderations <- nice_mod(
  data = mtcars2,
  response = "mpg",
  predictor = "gear",
  moderator = "wt"
)
moderations

## -----------------------------------------------------------------------------
(my_table <- nice_table(moderations, highlight = TRUE))

## ----eval = FALSE-------------------------------------------------------------
#  # Open in Word
#  print(my_table, preview = "docx")
#  
#  # Save in Word
#  flextable::save_as_docx(my_table, path = "moderations.docx")

## -----------------------------------------------------------------------------
slopes <- nice_slopes(
  data = mtcars2,
  response = "mpg",
  predictor = "gear",
  moderator = "wt"
)
slopes
nice_table(slopes, highlight = TRUE)

## -----------------------------------------------------------------------------
# Moderations
nice_mod(
  data = mtcars2,
  response = c("mpg", "disp", "hp"),
  predictor = "gear",
  moderator = "wt"
) |>
  nice_table(highlight = TRUE)

# Simple slopes
nice_slopes(
  data = mtcars2,
  response = c("mpg", "disp", "hp"),
  predictor = "gear",
  moderator = "wt"
) |>
  nice_table(highlight = TRUE)

## -----------------------------------------------------------------------------
nice_mod(
  data = mtcars2,
  response = "mpg",
  predictor = "gear",
  moderator = "wt",
  covariates = c("am", "vs")
) |>
  nice_table(highlight = TRUE)

## -----------------------------------------------------------------------------
nice_slopes(
  data = mtcars2,
  response = "mpg",
  predictor = "gear",
  moderator = "wt",
  covariates = c("am", "vs")
) |>
  nice_table(highlight = TRUE)

## -----------------------------------------------------------------------------
# First need to define model for plot function
mod <- lm(mpg ~ gear * wt + am + vs, data = mtcars2)

# Plot the model
library(interactions)
interact_plot(mod, pred = "gear", modx = "wt", interval = TRUE)

## -----------------------------------------------------------------------------
mtcars2$am <- mtcars$am

## -----------------------------------------------------------------------------
nice_mod(
  response = "mpg",
  predictor = "gear",
  moderator = "disp",
  moderator2 = "am",
  data = mtcars2
) |>
  nice_table(highlight = TRUE)

## -----------------------------------------------------------------------------
nice_slopes(
  data = mtcars2,
  response = "mpg",
  predictor = "gear",
  moderator = "disp",
  moderator2 = "am"
) |>
  nice_table(highlight = TRUE)

## -----------------------------------------------------------------------------
model1 <- lm(mpg ~ cyl + wt * hp, mtcars2)
model2 <- lm(qsec ~ disp + drat * carb, mtcars2)
my.models <- list(model1, model2)
nice_lm(my.models) |>
  nice_table(highlight = TRUE)

## -----------------------------------------------------------------------------
model1 <- lm(mpg ~ gear * wt, mtcars2)
model2 <- lm(disp ~ gear * wt, mtcars2)
my.models <- list(model1, model2)
nice_lm_slopes(my.models, predictor = "gear", moderator = "wt") |>
  nice_table(highlight = TRUE)