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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

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

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.