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Getting started with colleyRstats

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
)
#> Registered S3 methods overwritten by 'ggpp':
#>   method                  from   
#>   heightDetails.titleGrob ggplot2
#>   widthDetails.titleGrob  ggplot2
#> Registered S3 method overwritten by 'lme4':
#>   method           from
#>   na.action.merMod car

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
colleyRstats::check_homogeneity_by_group(main_df, "ConditionID", "score")
#> [1] TRUE

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

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.