Build a manuscript-ready results table

Formats a fitted workflow's results as a compact table for a manuscript or a Quarto or R Markdown document. By default the table is written in APA style (7th ed.): readable column headings, two decimals, no leading zero on values that cannot exceed 1, p values to three decimals or < .001, and intervals as [LL, UL] under a heading that names their level.

Markdown output is generated directly, so no reporting package is required to use it. Nothing in the core analysis depends on one.

Usage

content_report(
  x,
  digits = 2,
  format = c("apa", "data.frame", "markdown"),
  include = c("all", "flagged"),
  caption = NULL
)

Arguments

x

A fitted contentvalid_workflow object.

digits

Decimal places for estimates. p values always get three, as APA requires.

format

"apa" (default), a table of formatted text in APA style; "markdown", the same table as Markdown lines; or "data.frame", the selected columns as rounded numbers under their names in results, for further computation.

include

"all" (default) or "flagged", which keeps only units whose status is not Supported.

caption

Optional caption line placed above a Markdown table.

Value

For "apa", a data frame of character columns that prints without row names; as.data.frame() drops its print class. For "data.frame", a plain data frame with recommendation and status columns. For "markdown", a character vector of Markdown lines that prints as the table, carrying the analysis provenance as its "settings" attribute.

Changed in 0.9.0

The default is now format = "apa". Earlier versions returned the numeric table by default, with p values rounded to digits; use format = "data.frame" for that table, where p values now keep three decimals.

Reporting the decision rules

A results table alone is not a reproducible report. The thresholds that produced each status live in the fitted object's settings, and are attached to Markdown output as an attribute so they travel with the table. Report them alongside it: two analyses of identical data can disagree entirely because one used a different criterion.

See also

as.data.frame.contentvalid_workflow() for the untrimmed table, and compare_rounds() for reporting change across pretest rounds.

Examples

sorts <- read.csv(
  system.file("extdata", "sort_example.csv", package = "contentvalidR"),
  stringsAsFactors = FALSE
)
fit <- sort_validity(sorts)
content_report(fit)
#>  item target judges competitor Psa     95% CI Csv      p decision
#>    A1      A  18/20       B; C .90 [.70, .97] .85 < .001   Retain
#>    A2      A  15/20          B .75 [.53, .89] .60   .021   Retain
#>    B1      B  17/20          A .85 [.64, .95] .75   .001   Retain
#>    B2      B  13/20          A .65 [.43, .82] .40   .132   Review
#>    C1      C  18/20       A; B .90 [.70, .97] .85 < .001   Retain
#>    C2      C  14/20          B .70 [.48, .85] .50   .058   Review
content_report(fit, format = "markdown", include = "flagged")
#> | item | target | judges | competitor | Psa | 95% CI | Csv | p | decision |
#> | --- | --- | --- | --- | --- | --- | --- | --- | --- |
#> | B2 | B | 13/20 | A | .65 | [.43, .82] | .40 | .132 | Review |
#> | C2 | C | 14/20 | B | .70 | [.48, .85] | .50 | .058 | Review |