---
title: "Getting started with colleyRstats"
output: rmarkdown::html_vignette
vignette: >
  %\VignetteIndexEntry{Getting started with colleyRstats}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---

```{r, include = FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
)
```

`colleyRstats` helps streamline a typical analysis workflow: configure a session,
check assumptions, create a plot, and generate manuscript-ready text.

## Session setup

```{r}
colleyRstats::colleyRstats_setup(
  set_options = FALSE,
  set_theme = FALSE,
  set_conflicts = FALSE,
  print_citation = FALSE,
  verbose = FALSE
)
```

## Example data

```{r}
set.seed(123)

main_df <- data.frame(
  Participant = factor(rep(1:20, each = 2)),
  ConditionID = factor(rep(c("Control", "Treatment"), times = 20)),
  score = rnorm(40, mean = rep(c(50, 55), times = 20), sd = 8)
)
```

## Check assumptions

```{r}
colleyRstats::check_normality_by_group(main_df, "ConditionID", "score")
colleyRstats::check_homogeneity_by_group(main_df, "ConditionID", "score")
```

## Create a plot

```{r}
colleyRstats::generateEffectPlot(
  data = transform(main_df, Group = ConditionID),
  x = "ConditionID",
  y = "score",
  fillColourGroup = "Group",
  ytext = "Score",
  xtext = "Condition"
)
```

## Produce a reporting sentence

```{r}
art_summary <- data.frame(
  Effect = "ConditionID",
  Df = 1,
  `F value` = 5.42,
  `Pr(>F)` = 0.027,
  Df.res = 19,
  check.names = FALSE
)

colleyRstats::reportART(art_summary, dv = "score")
```

## Next steps

- `vignette("analyzing-a-user-study")` walks a complete within-subjects study
  from raw data to manuscript-ready text and figures, including the one-call
  `analyze_and_report()` / `report_all()` pipeline.
- `vignette("choosing-a-test")` shows how `recommend_test()` selects the right
  test or mixed model from the data, and how to report GLMMs/CLMMs.
- `vignette("overleaf")` covers getting the LaTeX output into an Overleaf
  project that compiles immediately (`latex_preamble()`, `use_colleyrstats_sty()`,
  `emit_overleaf()`).
- Browse the reference for reporting helpers such as `reportMeanAndSD()` and
  `reportDunnTest()`, and use `generateMoboPlot()` / `generateMoboPlot2()` for
  optimization studies.