Getting started with colleyRstats

Session setup

Attach the packages you need first, then configure the session. colleyRstats_setup() sets the package’s ggplot2 theme, so every figure below comes out with consistent typography.

library(colleyRstats)

colleyRstats_setup(print_citation = FALSE, verbose = FALSE)

If you also want the package’s conflicted preferences – dplyr::filter() over stats::filter(), psych::describe() over Hmisc::describe(), and so on – pass set_conflicts = TRUE, and put that call after every library() call in the script:

library(colleyRstats)
library(easystats)
library(dplyr)

colleyRstats_setup(set_conflicts = TRUE)   # last

The ordering matters in both directions. Activating conflicted replaces library() for the rest of the session, and meta-packages such as easystats cannot be attached once it has; and conflicted resolves only those names that are ambiguous among the packages attached at the time, so a call made before the rest of your library() calls has less to work with.

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

Every participant here saw both conditions, so the design is within-subjects. Name the participant column as subject: the check then tests the per-participant differences, which is what a paired test assumes to be normal.

check_normality_by_group(main_df, "ConditionID", "score", subject = "Participant")
#> [1] TRUE
#> attr(,"tests")
#>         group  n        W   p_value p_adjusted testable
#> 1 differences 20 0.958116 0.5069337  0.5069337     TRUE
#> attr(,"method")
#> [1] "differences"
#> attr(,"p_adjust")
#> [1] "none"
#> attr(,"untestable")
#> character(0)
#> attr(,"dropped_subjects")
#> character(0)

For a between-subjects design, leave subject out: each group is then tested on its own (Holm-corrected across groups), and check_homogeneity_by_group() adds the Brown-Forsythe test of equal variances.

Create a plot

plot_effect(
  data = transform(main_df, Group = ConditionID),
  x = "ConditionID",
  y = "score",
  fillColourGroup = "Group",
  ytext = "Score",
  xtext = "Condition"
)
#> Warning: Not every combination of 'ConditionID' and 'Group' has data; the
#> main-effect line averages only the cells that do, so its levels are not based
#> on the same groups.
#> `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
)

report_art(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: [0.00, 0.50]).

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.