Getting started with colleyRstats

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

Session setup

colleyRstats::colleyRstats_setup(
  set_options = FALSE,
  set_theme = FALSE,
  set_conflicts = FALSE,
  print_citation = FALSE,
  verbose = FALSE
)

Example data

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

colleyRstats::check_normality_by_group(main_df, "ConditionID", "score")
#> [1] TRUE
#> attr(,"tests")
#>   ConditionID         W   p_value
#> 1     Control 0.9378270 0.2180765
#> 2   Treatment 0.9667112 0.6844808
colleyRstats::check_homogeneity_by_group(main_df, "ConditionID", "score")
#> [1] TRUE
#> attr(,"test")
#>   df1 df2 statistic         p
#> 1   1  38 0.1081505 0.7440653

Create a plot

colleyRstats::generateEffectPlot(
  data = transform(main_df, Group = ConditionID),
  x = "ConditionID",
  y = "score",
  fillColourGroup = "Group",
  ytext = "Score",
  xtext = "Condition"
)
#> `geom_line()`: Each group consists of only one observation.
#> ℹ Do you need to adjust the group aesthetic?

Produce a reporting sentence

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")
#> The ART found a significant main effect of \ConditionID on score (\F{1}{19}{5.42}, \p{0.027}, $\eta_{p}^{2}$ = 0.22, 95\% CI: [0.01, 1.00]).

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.

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.